Why Effective CTEM Must be an Intelligence-Led Program
Continuous Threat Exposure Management (CTEM) is a continuous program and operational framework, not a single pre-boxed platform. Flashpoint believes that effective CTEM must be intelligence-led, using curated threat intelligence as the operational core to prioritize risk and turn exposure data into defensible decisions.
Continuous Threat Exposure Management (CTEM) is Not a Product
Since Gartner’s introduction of CTEM as a framework in 2022, cybersecurity vendors have engaged in a rapid “productization” race. This has led to inconsistent market definitions, with a variety of vendors from vulnerability scanners to Attack Surface Management (ASM) providers now claiming to be an “exposure management” solution.
The current approach to productizing CTEM is flawed. There is no such thing as a single “exposure management platform.” The enterprise reality is that most enterprises buy three or more products just to approximate what CTEM promises in theory. Even with these technologies, organizations still require heavy lifting with people, process, and custom integrations to actually make it work.
The Exposure Stack: When One Platform Becomes Three (or More)
A functional CTEM approach typically requires multiple platforms or tools, including:
Continuous Penetration/Exploitation Testing & Attack Path Analysis for continuous pentesting, attack path validation, and hands-on exposure validation.
Vulnerability and Exposure Management for vulnerability scanning, exposure scoring, and asset risk views.
Intelligence for deep, curated vulnerability, compromised credentials, card fraud, and other forms of intelligence that goes far beyond the scope of technology-based “management platforms”.
In some cases, organizations may also use an ASM vendor for shadow IT discovery, a CMDB for asset context, and ticketing integrations to drive remediation. This multi-platform model is the rule, not the exception. And that raises a hard truth: if you need three or more products, plus a dedicated team to implement CTEM, you need an intelligence-led CTEM program.
CTEM is an Operational Discipline, Not a Single Product
The narrative that CTEM can be packaged into a single product breaks down for three critical reasons:
1. CTEM is a Program, Not a Platform
You cannot buy a capability that requires full-stack asset visibility, contextualized threat actor data, real-world validation, and remediation orchestration from one tool. Each component spans a different domain of expertise and data. A vulnerability scanner, alone, cannot validate exploitability, a pentest service has a tough time scaling to daily monitoring, and generic threat intelligence feeds cannot provide critical business context.
However, CTEM requires orchestration of all these components in one operational loop. No single product delivers this comprehensively out of the box; this is why CTEM must be viewed as a continuous program, not a one-size-fits-all product.
2. Human Expertise is Irreplaceable
Vendors often advertise automation, however, key intelligence functions are still powered by and reliant on human analysis. Even with best-in-class AI tools in place, security teams are depending on human insights for:
Triaging noisy CVE lists
Cross-referencing exposure data with asset inventories
Manually validating if risks are real
Prioritizing based on threat intelligence and internal context
Writing custom logic and integrations to bridge platforms together
In other words, exposure management today still relies on human insights and expertise. So while vendors advertise “automation and intelligence,” what they’re really delivering is a starting point. Ultimately, AI is a force multiplier for threat analysts, not a replacement.
3. Risk Without Intelligence Is Just Data
Most platforms treat exposure like a math problem. But real risk isn’t just CVSS (Common Vulnerability Scoring System) scores or asset counts, it requires answering critical, intelligence-based questions:
How likely is this vulnerability to be exploited, and what’s the impact if it is?
How likely is this misconfiguration to be exploited, and what is its impact?
How likely is this compromised credential to be used by a threat actor, and what is the potential impact?
These answers require intelligence, not just data. Best-in-class intelligence provides security teams with confirmed exploit activity in the wild, context around attacker usage in APT (Advanced Persistent Threat) campaigns, and detailed metadata for prioritization where CVSS fails. That is why Flashpoint intelligence is leveraged by over 800 organizations as the operational core of exposure management, turning exposure data into defensible decisions.
CTEM Productization vs. CTEM Reality
If your risk strategy requires continuous penetration and exploit testing, vulnerability management, threat intelligence, and manual prioritization and validation, you’re not buying CTEM; you’re building it. At Flashpoint, we’re helping organizations build CTEM the right way: driven by intelligence, and powered by integrations and AI.
The Intelligence-Led Future of Exposure Management
Flashpoint treats CTEM for what it really is, as a program that must be constructed intelligently, iteratively, and contextually.
That means:
Using threat and vulnerability intelligence to drive what actually gets prioritized
Treating scanners, ASM platforms, and pentesting as inputs, not outcomes
Building processes where intelligence, context, and validation inform exposure decisions, not just ticket creation
Investing in platform interconnectivity, not just feature checklists
Using Flashpoint’s intelligence collections, organizations can achieve intelligence-led exposure management, with threat and vulnerability intelligence working together to provide context and actionable insights in a continuous, prioritized loop. This empowers security teams to build and scale their own CTEM programs, which is the only realistic approach in a cybersecurity landscape where no single platform can do it all.
Achieve Elite Operation Control Over Your CTEM Program Using Flashpoint
If you’re evaluating exposure management tools, ask yourself:
What happens when we find a critical vulnerability and how do we know it matters?
Can this platform correlate attacker behavior with our asset landscape?
Does it validate risk or just report it?
How many other tools will we need to buy just to complete the picture?
The answers may surprise you. At Flashpoint, we’re helping organizations build CTEM the right way, driven by intelligence, powered by integration, and grounded in reality. Request a demo today and see how best-in-class intelligence is the key to achieving an effective CTEM program.
As businesses and service providers deploy AI tools and systems, having strong cyber security across the entire AI pipeline is a foundational requirement, from design to deployment. Even at this stage of AI adoption, attacks on AI infrastructure and prompt-based manipulation are gaining traction. Per a recent Gartner report, 32% of organizations have already experienced an AI attack involving prompt manipulation, while 29% faced attacks on their GenAI infrastructure in the past year. Nearly 70% of cyber security leaders said emerging GenAI risks demand significant changes to existing cyber security approaches. And a recent Lakera survey found that only 19% of organizations […]
“The Justice Department announced two indictments in the Central District of California charging Ukrainian national Victoria Eduardovna Dubranova, 33, also known as Vika, Tory, and SovaSonya, for her role in conducting cyberattacks and computer intrusions against critical infrastructure and other victims around the world, in support of Russia’s geopolitical interests. Dubranova was extradited to the United States earlier this year on an indictment charging her for her actions supporting CyberArmyofRussia_Reborn (CARR). Today, Dubranova was arraigned on a second indictment charging her for her actions supporting NoName057(16) (NoName). Dubranova pleaded not guilty in both cases, and is scheduled to begin trial in the NoName matter on Feb. 3, 2026 and in the CARR matter on April 7, 2026.”
“As described in the indictments, the Russian government backed CARR and NoName by providing, among other things, financial support. CARR used this financial support to access various cybercriminal services, including subscriptions to distributed denial of service-for-hire services. NoName was a state-sanctioned project administered in part by an information technology organization established by order of the President of Russia in October 2018 that developed, along with other co-conspirators, NoName’s proprietary distributed denial of service (DDoS) program.”
Cyber Army of Russia Reborn
“According to the indictment, CARR, also known as Z-Pentest, was founded, funded, and directed by the Main Directorate of the General Staff of the Armed Forces of the Russian Federation (GRU). CARR claimed credit for hundreds of cyberattacks against victims worldwide, including attacks against critical infrastructure in the United States, in support of Russia’s geopolitical interests. CARR regularly posted on Telegram claiming credit for its attacks and published photos and videos depicting its attacks. CARR primarily hacked industrial control facilities and conducted DDoS attacks. CARR’s victims included public drinking water systems across several states in the U.S., resulting in damage to controls and the spilling of hundreds of thousands of gallons of drinking water. CARR also attacked a meat processing facility in Los Angeles in November 2024, spoiling thousands of pounds of meat and triggering an ammonia leak in the facility. CARR has attacked U.S. election infrastructure during U.S. elections, and websites for U.S. nuclear regulatory entities, among other sensitive targets.”
“An individual operating as ‘Cyber_1ce_Killer,’ a moniker associated with at least one GRU officer instructed CARR leadership on what kinds of victims CARR should target, and his organization financed CARR’s access to various cybercriminal services, including subscriptions to DDoS-for-hire services. At times, CARR had more than 100 members, including juveniles, and more than 75,000 followers on Telegram.”
NoName057(16)
“NoName was covert project whose membership included multiple employees of The Center for the Study and Network Monitoring of the Youth Environment (CISM), among other cyber actors. CISM was an information technology organization established by order of the President of Russia in October 2018 that purported to, among other things, monitor the safety of the internet for Russian youth.”
“According to the indictment, NoName claimed credit for hundreds of cyberattacks against victims worldwide in support of Russia’s geopolitical interests. NoName regularly posted on Telegram claiming credit for its attacks and published proof of victim websites being taken offline. The group primarily conducted DDoS cyberattacks using their own proprietary DDoS tool, DDoSia, which relied on network infrastructure around the world created by employees of CISM.”
“NoName’s victims included government agencies, financial institutions, and critical infrastructure, such as public railways and ports. NoName recruited volunteers from around the world to download DDoSia and used their computers to launch DDoS attacks on the victims that NoName leaders selected. NoName also published a daily leaderboard of volunteers who launched the most DDoS attacks on its Telegram channel and paid top-ranking volunteers in cryptocurrency for their attacks.” (Source: US Department of Justice)
Key Vulnerabilities: Week of December 20 – December 26, 2025
Foundational Prioritization
Of the vulnerabilities Flashpoint published this week, there are 34 that you can take immediate action on. They each have a solution, a public exploit exists, and are remotely exploitable. As such, these vulnerabilities are a great place to begin your prioritization efforts.
Diving Deeper – Urgent Vulnerabilities
Of the vulnerabilities Flashpoint published last week, four are highlighted in this week’s Vulnerability Insights and Prioritization Report because they contain one or more of the following criteria:
Are in widely used products and are potentially enterprise-affecting
Are exploited in the wild or have exploits available
Allow full system compromise
Can be exploited via the network alone or in combination with other vulnerabilities
Have a solution to take action on
In addition, all of these vulnerabilities are easily discoverable and therefore should be investigated and fixed immediately.
To proactively address these vulnerabilities and ensure comprehensive coverage beyond publicly available sources on an ongoing basis, organizations can leverage Flashpoint Vulnerability Intelligence. Flashpoint provides comprehensive coverage encompassing IT, OT, IoT, CoTs, and open-source libraries and dependencies. It catalogs over 100,000 vulnerabilities that are not included in the NVD or lack a CVE ID, ensuring thorough coverage beyond publicly available sources. The vulnerabilities that are not covered by the NVD do not yet have CVE ID assigned and will be noted with a VulnDB ID.
NOTES: The severity of a given vulnerability score can change whenever new information becomes available. Flashpoint maintains its vulnerability database with the most recent and relevant information available. Login to view more vulnerability metadata and for the most up-to-date information.
CVSS scores: Our analysts calculate, and if needed, adjust NVD’s original CVSS scores based on new information being available.
Social Risk Score: Flashpoint estimates how much attention a vulnerability receives on social media. Increased mentions and discussions elevate the Social Risk Score, indicating a higher likelihood of exploitation. The score considers factors like post volume and authors, and decreases as the vulnerability’s relevance diminishes.
Ransomware Likelihood: This score is a rating that estimates the similarity between a vulnerability and those known to be used in ransomware attacks. As we learn more information about a vulnerability (e.g. exploitation method, technology affected) and uncover additional vulnerabilities used in ransomware attacks, this rating can change.
Flashpoint Ignite lays all of these components out. Below is an example of what this vulnerability record for CVE-2025-33223 looks like.
This record provides additional metadata like affected product versions, MITRE ATT&CK mapping, analyst notes, solution description, classifications, vulnerability timeline and exposure metrics, exploit references and more.
Analyst Comments on the Notable Vulnerabilities
Below, Flashpoint analysts describe the five vulnerabilities highlighted above as vulnerabilities that should be of focus for remediation if your organization is exposed.
CVE-2025-33222
NVIDIA Isaac Launchable contains a flaw that is triggered by the use of unspecified hardcoded credentials. This may allow a remote attacker to trivially gain privileged access to the program.
CVE-2025-33223
NVIDIA Isaac Launchable contains an unspecified flaw that is triggered as certain activities are executed with unnecessary privileges. This may allow a remote attacker to potentially execute arbitrary code.
CVE-2025-68613
n8n Package for Node.js contains a flaw in packages/workflow/src/expression-evaluator-proxy.ts that is triggered as workflow expressions are evaluated in an improperly isolated execution context. This may allow an authenticated, remote attacker to execute arbitrary code with the privileges of the n8n process.
CVE-2025-14847
MongoDB contains a flaw in the ZlibMessageCompressor::decompressData() function in mongo/transport/message_compressor_zlib.cpp that is triggered when handling mismatched length fields in Zlib compressed protocol headers. This may allow a remote attacker to disclose uninitialized memory contents on the heap.
The Infostealer Gateway: Uncovering the Latest Methods in Defense Evasion
In this post, we analyze the evolving bypass tactics threat actors are using to neutralize traditional security perimeters and fuel the global surge in infostealer infections.
Infostealer-driven credential theft in 2025 has surged, with Flashpoint observing a staggering 800% increase since the start of the year. With over 1.8 billion corporate and personal accounts compromised, the threat landscape finds itself in a paradox: while technical defenses have never been more advanced, the human attack surface has never been more vulnerable.
Information-stealing malware has become the most scalable entry point for enterprise breaches, but to truly defend against them, organizations must look beyond the malware itself. As teams move into 2026 security planning, it is critical to understand the deceptive initial access vectors—the latest tactics Flashpoint is seeing in the wild—that threat actors are using to manipulate users and bypass modern security perimeters.
Here are the latest methods threat actors are leveraging to facilitate infections:
1. Neutralizing Mark of the Web (MotW) via Drag-and-Drop Lures
Mark of the Web (MotW) is a critical Windows defense feature that tags files downloaded from the internet as “untrusted” by adding a hidden NTFS Alternate Data Stream (ADS) to the file. This tag triggers “Protected View” in Microsoft Office programs and prompts Windows SmartScreen warnings when a user attempts to execute an unknown file.
Flashpoint has observed a new social engineering method to bypass these protections through a simple drag-and-drop lure. Instead of asking a user to open a suspicious attachment directly, which would trigger an immediate MotW warning, threat actors are instead instructing the victim to drag the malicious image or file from a document onto their desktop to view it. This manual interaction is highly effective for two reasons:
Contextual Evasion: By dragging the file out of the document and onto the desktop, the file is executed outside the scope of the Protected View sandbox.
Metadata Stripping: In many instances, the act of dragging and dropping an embedded object from a parent document can cause the operating system to treat the newly created file as a local creation, rather than an internet download. This effectively strips the MotW tag and allows malicious code to run without any security alerts.
2. Executing Payloads via Vulnerabilities and Trusted Processes
Flashpoint analysts uncovered an illicit thread detailing a proof of concept for a client-side remote code execution (RCE) in the Google Web Designer for Windows, which was first discovered by security researcher Bálint Magyar.
Google Web Designer is an application used for creating dynamic ads for the Google Ads platform. Leveraging this vulnerability, attackers would be able to perform remote code execution through an internal API using CSS injection by targeting a configuration file related to ads documents.
Within this thread, threat actors were specifically interested in the execution of the payload using the chrome.exe process. This is because using chrome.exe to fetch and execute a file is likely to bypass several security restrictions as Chrome is already a trusted process. By utilizing specific command-line arguments, such as the –headless flag, threat actors showed how to force a browser to initiate a remote connection in the background without spawning a visible window. This can be used in conjunction with other malicious scripts to silently download additional payloads onto a victim’s systems.
3. Targeting Alternative Softwares as a Path of Least Resistance
As widely-used software becomes more hardened and secure, threat actors are instead pivoting to targeting lesser-known alternatives. These tools often lack robust macro-protections. By targeting vulnerabilities in secondary PDF viewers or Office alternatives, attackers are seeking to trick users into making remote server connections that would otherwise be flagged as suspicious.
Understanding the Identity Attack Surface
Social engineering is one of the driving factors behind the infostealer lifecycle. Once an initial access vector is successful, the malware immediately begins harvesting the logs that fuel today’s identity-based digital attacks.
As detailed in The Proactive Defender’s Guide to Infostealers, the end goal is not just a password. Instead, attackers are prioritizing session cookies, which allow them to perform session hijacking. By importing these stolen cookies into anti-detect browsers, they bypass Multi-Factor Authentication and step directly into corporate environments, appearing as a legitimate, authenticated user.
Understanding how threat actors weaponize stolen data is the first step toward a proactive defense. For a deep dive into the most prolific stealer strains and strategies for managing the identity attack surface, download The Proactive Defender’s Guide to Infostealers today.
Surfacing Threats Before They Scale: Why Primary Source Collection Changes Intelligence
This blog explores how Primary Source Collection (PSC) enables intelligence teams to surface emerging fraud and threat activity before it reaches scale.
Spend enough time investigating fraud and threat activity, and a familiar pattern emerges. Before a tactic shows up at scale—before credential stuffing floods login pages or counterfeit checks hit customers—there is almost always a quieter formation phase. Threat actors test ideas, trade techniques, and refine playbooks in small, often closed communities before launching coordinated campaigns.
The signals are there. The challenge is that most organizations never see them.
For years, intelligence programs have leaned heavily on static feeds: prepackaged streams of indicators, alerts, and reports delivered on a fixed cadence. These feeds validate what is already known, but they rarely surface what is still taking shape. They are designed to summarize activity after it has matured, not to discover it while it is still evolving.
Meanwhile, the real innovation in fraud and threat ecosystems happens elsewhere in invite-only Telegram channels, dark web marketplaces, and regional-language forums that update in real time. By the time a static feed flags a new technique, it is often already widespread.
This disconnect has consequences. When intelligence arrives too late, teams are left responding to impact rather than shaping outcomes.
How Threats Actually Evolve
Fraudsters and threat actors do not work in isolation, they collaborate. In closed forums and encrypted channels, one actor experiments with a new login bypass, another tests two-factor authentication evasion, and a third packages those ideas into a tool or service. What begins as a handful of screenshots or code snippets quickly becomes a repeatable process.
These shared processes often take the form of playbooks that act as step-by-step guides that document how to execute a fraud scheme or exploit a weakness. Once a playbook begins circulating, scale is inevitable. Techniques that started as limited tests turn into thousands of coordinated attempts almost overnight.
Every intelligence or fraud analyst has experienced the moment when an unfamiliar tactic suddenly overwhelms detection systems. The frustrating reality is that the warning signs were often visible weeks earlier, they simply never made it into the static feeds teams were relying on.
Why Static Collection Falls Short
Static collection creates a sense of coverage, but that coverage is often shallow. Sources are fixed. Cadence is slow. Context is stripped away.
A feed might tell you that a domain, handle, or email address is associated with a known tactic, but not how that tactic was developed, who is promoting it, or whether it has any relevance to your organization’s specific exposure. You are seeing the exhaust, not the engine.
This lag matters. The window between a tactic being tested in a small community and being deployed at scale is often the most valuable moment for intervention. Miss that window, and response becomes exponentially more expensive.
As threats accelerate and collaboration among adversaries increases, intelligence programs that depend solely on static inputs struggle to keep pace.
A Different Model: Primary Source Collection
Primary Source Collection (PSC) changes how intelligence is gathered by starting with the questions that matter most and collecting directly from the original environments where those answers exist.
Rather than relying on a predefined list of sources or vendor-determined priorities, PSC begins with a defined intelligence requirement. Collection is then shaped around that requirement, directing analysts to the forums, marketplaces, and channels where relevant activity is actively unfolding.
This means monitoring closed communities advertising check alteration services. It means observing invite-only groups trading identity fraud tutorials. It means collecting original posts, screenshots, files, and discussions while they are still part of an active conversation instead of weeks later in summarized form. When actors begin discussing a new bypass technique or sharing proof-of-concept screenshots, that is the moment to act, not weeks later when the same method is being resold across marketplaces.
Primary Source Collection provides that window. It surfaces the conversations, artifacts, and early indicators that reveal what is coming next and gives teams the time they need to intervene before campaigns scale.
This does not replace analytics, automation, or baseline monitoring. It strengthens them by feeding earlier, richer insight into downstream systems. It ensures that detection and response are informed by how threats are actually developing, not just how they appear after the fact.
In one case, a financial institution using this approach identified counterfeit checks featuring its brand being advertised in underground marketplaces weeks before customers began reporting losses. By collecting directly from those spaces, analysts flagged the images, traced sellers, and alerted internal teams early enough to prevent further exploitation.
That is what early warning looks like when collection is aligned with purpose.
Making Intelligence Taskable
One of the most important shifts enabled by Primary Source Collection is tasking.
Traditional intelligence programs operate like autopilot. They deliver a steady stream of data, but that stream reflects the provider’s priorities rather than the organization’s evolving needs. Analysts spend valuable time triaging irrelevant information while emerging risks go unnoticed.
In classified intelligence environments, this problem has long been addressed through tasking. Every collection effort begins with a clearly defined requirement and priorities drive collection, not the other way around.
PSC applies that same discipline to open-source and commercial intelligence. Teams define Priority Intelligence Requirements (PIRs), such as identifying actors testing bypass methods for specific login flows, and immediately direct collection toward those needs. As priorities change, tasking changes with them.
This transforms intelligence from a passive stream into an operational capability. Analysts are no longer waiting for someone else’s update cycle. They are shaping visibility in real time, testing hypotheses, validating concerns, and uncovering tactics before they mature.
For leadership, this provides something more valuable than indicators: confidence that critical developments are not happening just out of sight.
How Taskable Collection Works in Practice
A taskable Primary Source Collection framework is dynamic by design. As stakeholder priorities shift due to a new campaign, incident, or geopolitical development, collection pivots immediately.
In practice, this approach includes:
Source discovery: Identifying new, relevant sources as they emerge, using a combination of analyst expertise and automated tooling.
Secure access: Entering closed or restricted spaces safely and ethically through controlled environments and vetted identities.
Direct collection: Capturing original content directly from threat actor environments, including posts, images, and files.
Processing and enrichment: Applying techniques such as optical character recognition, entity extraction, and metadata tagging to transform raw material into usable intelligence.
Delivery and collaboration: Routing outputs into investigative workflows or directly to stakeholders to accelerate response.
Intelligence can then mirror the agility of modern threats instead of lagging behind them.
Why This Shift Matters Now
Threat and fraud operations are moving faster than ever. Barriers to entry are lower. Tooling is more accessible. Collaboration rivals legitimate software development cycles.
Defenders cannot afford to move slower than the adversaries they are trying to stop.
Primary Source Collection is how intelligence teams keep pace. It aligns collection with mission needs, enables real-time tasking, and delivers insight early enough to change outcomes instead of just documenting them.
The signals have always been there. What has changed is the ability to surface them while they still matter.
By Troy Wojewoda During a recent Breach Assessment engagement, BHIS discovered a highly stealthy and persistent intrusion technique utilized by a threat actor to maintain Command-and-Control (C2) within the client’s […]
The CTI Analyst’s Isolated Arsenal: Desktop Tools for High-Risk Intelligence
This blog explores how CTI teams safely analyze high-risk environments, engage with threat actors, and process sensitive data using Flashpoint Managed Attribution.
Cyber Threat Intelligence (CTI) analysts routinely operate in high-risk digital spaces where threat actors operate, such as Dark Web forums, encrypted chat rooms, and sites hosting massive breached datasets. Engaging with this data requires absolute confidence that your operational security (OPSEC) is up-to-date.
OPSEC failures can have significant consequences. A single attribution error or host-machine exposure can put both the analyst at risk, and compromise the organization’s security posture. To ensure your organization’s CTI activities remain anonymous, secure, and effective, this post focuses on two essentials:
The types of desktop applications and tools that must run in a secure, isolated environment
How Flashpoint Managed Attribution (MA) provides the operational foundation for safe CTI workflows.
OPSEC & Access
Successful execution of CTI operations hinges on establishing a complete shield between the analyst and the target environment. These tools form the base layer for secure and anonymous activity, ensuring that an analyst’s real identity and location are never exposed.
Tool Category
Tool/Type
Use Case
Network Anonymity
VPN Clients
IP Masking & Geo-Shifting: Adding a layer of IP obfuscation, especially when accessing geo-restricted content or high-risk sites (often used before Tor for added protection).
Secure Communication
Telegram, Session, Tox, Pidgin (with OTR/OMEMO)
Threat Actor Engagements: Contacting a threat actor (TA) about a posted dataset, discussing access, or validating a claimed compromise.
Network Utility
Torsocks / Proxychains
Script Anonymization: Forcing data collection scripts (Python, Go, etc.) to use an anonymized network when scraping or downloading data.
Operational Case Study: Secure Threat Actor Engagement with Telegram and Flashpoint Managed Attribution
When communicating anonymously with a threat actor, the Flashpoint Managed Attribution workflow provides the following key advantages for CTI teams:
Identity Protection: Creates a secure, isolated virtual machine with robust anonymization (VPN, Tor, rotating IPs) to protect the analyst’s identity. The analyst sets up messaging clients like Telegram within this secure environment, making it impossible for the threat actor to trace their real IP or location.
Continuous OPSEC: Continuously masks the operational footprint with constantly changing and untraceable IP addresses, ensuring all communication is routed through multiple layers of anonymity.
Host Machine Isolation & Secure Logging: All information exchanged is handled within this isolated environment to prevent malicious files from affecting the analyst’s host machine, while all communications are securely logged for later analysis.
Data Processing & Automation
CTI analysts routinely process massive log files and breach dumps that are unstable, unvalidated, or potentially malicious. By deploying essential data processing and automation tools within an isolated environment like Flashpoint Managed Attribution, you ensure this high-risk content never compromises the analyst’s host machine.
Tool Category
Tool/Type
Use Case
Scripting & Automation
Python, Golang, Bash/PowerShell
Breach Data Analysis: Creating custom scraping and parsing scripts to download and search breached datasets (often multi-terabyte files) from ransomware or other leak sites.
Command-Line Tools
grep, awk, sed, curl, wget
Assess Exposure: Quickly search for company-specific keywords, employee names, or technical indicators across massive, potentially compromised datasets.
Data Encoding/Decoding
CyberChef (Desktop/Local Instance)
Indicator of Compromise (IOC) Transformation: Decoding obfuscated strings, converting data formats, or analyzing potentially malicious content without sending it to an external server.
Operational Case Study: Automating Breach Data Analysis with Python and Flashpoint Managed Attribution
Within a Flashpoint Managed Attribution workspace, a CTI analyst deploys a Python script. The anonymized MA environment ensures:
This script crawls and downloads data through an untraceable, constantly changing IP network, performing on-the-fly parsing and storing extracted intelligence in an encrypted database.
Data ingestion and analysis is executed securely, leaving no trace of the analyst’s activity.
Open Source Intelligence (OSINT) & Analysis
The below applications help analysts connect the dots between various pieces of intelligence but often require handling data from unverified or hostile sources, necessitating strict isolation.
Tool Category
Tool/Type
Use Case
Research
Tor Browser
Dark Web Collection: Accessing closed forums, markets, and hosting sites for intelligence gathering and monitoring.
Link Analysis
Maltego
Mapping Threat Actors: Identifying the infrastructure, affiliates, and complex relationships of a cybercrime group under investigation.
Evidence Preservation
Hunch.ly
Chain of Custody: Securely capturing and preserving online evidence (e.g., from a hacktivist blog or a ransomware leak page) before it is taken down.
Metadata Analysis
ExifTool (Desktop Client)
Source Attribution: Analyzing a file downloaded from a threat actor site to extract potential clues like hidden usernames, internal network paths, or original creation dates.
Operational Case Study: Analyzing a Ransomware Leak Page with Hunch.ly
When a new ransomware group emerges, a CTI analyst uses tools like Hunch.ly to safely collect evidence from leak sites. Hunch.ly captures all data, timestamps it, and creates a cryptographic hash to ensure integrity. Using tools like Hunch.ly inside of a secure virtual machine like Flashpoint Managed Attribution ensures the analyst’s anonymity, enabling thorough analysis without risking the analyst’s system or identity.
Unlock Maximum Tool Utility with Flashpoint Managed Attribution
Ultimately, while these desktop tools are indispensable for CTI analysts operating in high-risk environments, their effective and secure deployment hinges on a robust underlying platform. This is where Flashpoint Managed Attribution becomes an invaluable asset. By providing a secure, anonymous workspace, Flashpoint Managed Attribution allows analysts to leverage these powerful tools, from network anonymizers and secure communication channels to advanced OSINT and data processing applications within an environment specifically built for operational security.
Request a demo today to ensure that gathered critical intelligence remains untraceable to your organization or analysts.
Beyond the Malware: Inside the Digital Empire of a North Korean Threat Actor
In this post Flashpoint reveals how an infostealer infection on a North Korean threat actor’s machine exposed their digital operational security failures and reliance on AI. Leveraging Flashpoint intelligence, we pivot from a single persona to a network of fake identities and companies targeting the Web3 and crypto industry.
Last week, Hudson Rock published a blog on “Trevor Greer,” a persona tied to a North Korean IT Worker. Flashpoint shared additional insights with our clients back in July, and we’re now making those findings public.
Trevor Greer, a North Korean operative, was identified via an infostealer infection on their own machine. Information-stealing malware, also known as Infostealers or stealers, are malware designed to scrape passwords and cookies from unsuspecting victims. Stealers (like LummaC2 or RedLine) are typically used by cybercriminals to steal login credentials from everyday users to sell on the Dark Web. It is rare to see them infect the machines of a state-sponsored advanced persistent threat group (APT).
However, when adversaries unknowingly infect themselves, they can expose valuable insights into the inner workings of their campaigns. Leveraging Flashpoint intelligence sourced from the leaked logs of “Trevor Greer,” our analysts uncovered a myriad of fake identities and companies used by DPRK APTs.
Finding Trevor Greer
Flashpoint analysts have been tracking the Trevor Greer email address since December 2024 in relation to the “Contagious Interview” campaign, in which threat actors operated as LinkedIn recruiters to target Web3 developers, resulting in the deployment of multiple stealers compromising developer Web3 wallets. Flashpoint also identified the specific persona’s involvement in a campaign in which North Korean threat actors posed as IT freelance workers and applied for jobs at legitimate companies before compromising the organizations internally.
ByBit Compromise
The ByBit compromise in late February 2025 further fueled Flashpoint’s investigations into the Trevor Greer email address. Bybit, a cryptocurrency exchange, suffered a critical incident resulting in North Korean actors extorting US $1.5 billion worth of cryptocurrency. In the aftermath, Silent Push researchers identified the persona “Trevor Greer” associated with the email address trevorgreer9312@gmail[.]com, which registered the domain “Bybit-assessment[.]com” prior to the Bybit compromise.
A later report claimed that the domain “getstockprice[.]com” was involved in the compromise. Despite these domain discrepancies, both investigations attributed the attack to North Korean advanced persistent threat (APT) nexus groups.
Tracing the Infection
Using Flashpoint’s vast intelligence collections, we performed a full investigation of compromised virtual private servers (VPS), revealing the actor’s potential involvement in several other operations, including remote IT work, several self-made blockchain and cryptocurrency exchange companies, and a potential crypto scam dating back to 2022.
Flashpoint analysts also discovered that the Trevor Greer email address was linked to domains infected with information-stealing malware.
What the Logs Revealed
Analysts extracted information about the associated infected host from Trevor Greer, revealing possible tradecraft and tools used. Analysts further identified specific indicators of compromise (IOCs) used in the campaigns mentioned above, as well as email addresses used by the actor for remote work.
The data painted a vivid picture of how these threat actors operate:
Preparation for “Contagious Interviews”
The browser history revealed the actor logging into Willo, a legitimate video interview platform. This suggests the actor was conducting reconnaissance to clone the site for the “Contagious Interview” campaign, where they lured Web3 developers into fake job interviews to deploy malware.
Reliance on AI Tools
The logs exposed the actor’s reliance on AI to bridge the language gap. The operator frequently accessed ChatGPT and Quillbot, likely using them to write convincing emails, build resumes, and generate code for their malware.
Pivoting: One Node to a Network
By analyzing the “Trevor Greer” logs, we were able to pivot to other personas and campaigns involved in the operation.
Fake Employment: The logs contained credentials for freelance platforms, such as Upwork and Freelancer, associated with other aliases, including “Kenneth Debolt” and “Fabian Klein.” This confirmed the actor was part of a broader scheme to infiltrate Western companies as remote IT workers.
Fake Companies: The data linked the actor to fake corporate entities, such as Block Bounce (blockbounce[.]xyz), a sham crypto trading firm set up to appear legitimate to potential victims.
Developer Personas: The infection data linked the actor to the GitHub account svillalobosdev, which had been active in open source projects to build credibility before the attack.
Legitimate Platforms & Tools: Analysts observed the actor using job boards such as Dice and HRapply[.]com, freelance platforms such as Upwork and Freelancer, and direct applications through company Workday sites. To improve their resume, the actor used resumeworded[.]com or cakeresume[.]com. For conversing, the threat actor likely relies on a mix of both GPT and Quilbot, as found in infected host logins, to ensure they sound human. During interviews, analysts determined that they potentially used Speechify.
Deep & Dark Web Resources: The actor also likely purchased Social Security numbers (SSNs) from SSNDOB24[.]com, a site for acquiring Social Security data.
Disrupt Threat Actors Using Flashpoint
The “Trevor Greer” case study illustrates a critical shift in modern threat intelligence. We are no longer limited to analyzing the malware adversaries deploy; sometimes, we can analyze the adversaries themselves.
Using their own tools against them, Flashpoint transformed a faceless state-sponsored entity into a tangible user with bad habits, sloppy OPSEC, and a trail of digital breadcrumbs. Behind every sophisticated APT campaign is a human operator, and sometimes, they click the wrong link too.
Request a demo today to delve deeper into the tactics, techniques, and procedures of advanced persistent threats and learn how Flashpoint’s intelligence strengthens your defenses.
We are excited to announce the launch of Saved Searches in Google Threat Intelligence (GTI) and VirusTotal (VT), a powerful new feature designed to streamline your threat hunting workflows and foster seamless collaboration across your security team.
From Campaign to Feature: Better Search Efficiency
For the last month, we’ve highlighted the critical importance of mastering search in our ongoing #monthofgoogletisearch campaign. We saw how security teams rely on complex, highly-tuned queries to identify threats, track adversaries, and perform deep-dive investigations.
This campaign emphasized a key challenge: once you craft the perfect query - a cornerstone of your investigation - it should be easy to reuse and share. Saved Searches is the direct answer to this need, turning successful, repeatable threat-hunting logic into a shared institutional asset.
Collaboration, Simplified: Save and Share Your Queries
With this initial launch of Saved Searches, we’re delivering two foundational capabilities that will immediately improve your team’s efficiency:
Save Searches: Instantly save any complex or frequently used query directly within GTI. This ensures your best investigative logic is always accessible, eliminating the need to rebuild queries from scratch or store them externally.
Share with Users: Critical insights are often time-sensitive. You can now easily share your saved searches with any other user in your organization with access to GTI. Whether you’re escalating a finding or establishing a standard workflow, sharing the exact query ensures consistency and accelerates joint analysis.
This means that a newly onboarded analyst can instantly access the expertise of senior members, and teams can maintain a unified approach to monitoring high-priority threats. It’s collaboration built right into your investigation tool.
Get Started Today with Campaign Searches
The Saved Searches feature is live now in Google Threat Intelligence and VirusTotal.
To help you hit the ground running, we have made the most impactful searches used throughout the #monthofgoogletisearch campaign public and available to all intelligence users! You can find these expert-crafted queries in your Saved Searches section today - a perfect starting point for your investigations.
Start by exploring these campaign searches and then easily save and share your own complex search queries. Look for the option to Save and Share your searches to transform your investigative logic into a shared asset.
This is just the first phase of enhancing search capabilities within GTI. We are committed to building on this foundation to provide even more robust tools that make your threat intelligence actionable and collaborative.
You can get more info by exploring our documentation page:
We peel back the layers on a threat we detected involving an adversary who brought their own virtual machine into an environment following an aggressive spam bombing attack.
From Endpoint Compromise to Enterprise Breach: Mapping the Infostealer Attack Chain
In Flashpoint’s latest webinar, we map the global infostealer attack chain step-by-step, from initial infection to enterprise-level account takeover. We analyze how the commodification of stolen identities works and demonstrate how Flashpoint intelligence provides the critical visibility necessary to disrupt this cycle.
Compromised digital identities have become one of the most valuable currencies in the cybercriminal ecosystem. The rise of information-stealing malware has created an industrial-scale supply chain for stolen credentials, session cookies, and browser fingerprints, directly fueling account takeover (ATO) campaigns that penetrate even the most mature security environments.
Flashpoint recently hosted an on-demand webinar, “From Compromise to Breach: How Infostealers Power Identity Attacks,” where our experts dissected this developing threat landscape. We exposed the exact sequence of events, providing defenders with the actionable intelligence required to disrupt the chain at multiple points. For the full technical breakdown, check out the full on-demand webinar.
Here are the main key takeaways you need to know:
Stage 1: Initial Infection and Data Harvest (The Compromise)
A full scale compromise often begins with a single event, typically a phishing lure, a malicious download, or a compromised cracked software installer. Once executed, the infostealer goes to work, quickly and stealthily, to build a “log” that grants post-MFA (multi-factor authentication) access.
Scouring now-compromised endpoints, the stealer searches for and compiles data such as:
Credentials: Saved logins, credit card details, and passwords for applications and websites.
Session Cookies/Tokens: These are the keys that allow an attacker to bypass login prompts entirely, appearing as an already-authenticated user.
Browser Fingerprints and System Metadata: Geolocation, IP address, and system language used to evade security tools by accurately mimicking the victim’s legitimate environment.
Stage 2: Commodification and the ATO Supply Chain (The Market)
Once a log is harvested, it enters the Infostealer-as-a-Service ecosystem, a critical industrialized stage of the attack chain. Here, threat actors can rent or purchase access to millions of fresh logs, effectively outsourcing the initial compromise phase and enabling mass identity exploitation for a minimal investment.
Check out the on-demand webinar for a full technical breakdown of this dark web economy and how the commodification of stealer logs drastically reduces the barrier to entry for follow-on attacks.
Stage 3: Post-MFA Account Takeover (The Breach)
This is the ultimate pivot point, where a simple endpoint infection escalates into an enterprise breach. Unlike the brute-forcing and phishing attacks of the past, attackers leverage the stolen session tokens and browser fingerprints.
Stolen log buyers leverage obfuscation tools such as anti-detect browsers. These tools ensure the attacker can seamlessly utilize the stolen cookies and digital fingerprints to appear identical to the original victim.
They inject valid, unexpired session tokens into their browser, which allows attackers to hijack the victim’s active session. This allows them to avoid fraud and anomaly detection systems, providing them access into corporate VPNs, cloud environments, and internal applications without ever needing to see a login prompt. From here, attackers can move laterally, exfiltrate sensitive data, or deploy ransomware.
Disrupting the Attack Chain Using Flashpoint’s Actionable Intelligence
Defense against this threat requires not only an understanding of the attack chain, but also comprehensive Cyber Threat Intelligence (CTI) to identify and mitigate risks at every stage:
Disruption Point in the Attack Chain
How Flashpoint Empowers Proactive Defense
Stage 1: Initial Infection/Log Creation
Gain immediate alerting on the sale of your organization’s compromised assets on the Dark Web before attackers can leverage stolen data.
Stage 2: Commodification/ATO Setup
Expose the illicit platforms and forums where threat actors discuss, buy, and sell stolen logs, allowing you to track the tooling and TTPs.
Stage 3: Post-MFA ATO/Breach
Identify and remediate the vulnerabilities within browsers or enterprise software that are most actively being targeted by infostealers.
The speed of infostealer-powered attacks demands an intelligence-driven response. Our recent webinar demonstrated how Flashpoint intelligence can empower your security teams to quickly identify and validate stolen logs, protecting your organization from compromise to breach. Watch the on-demand webinar to learn more, or request a demo today.
Digital Supply Chain Risk: Critical Vulnerability Affecting React Allows for Unauthorized Remote Code Execution
CVE-2025-55182 (VulnDB ID: 428930), is a severe, unauthenticated RCE impacting a major component of React and its ecosystem, putting global applications at immediate, high-fidelity risk.
Flashpoint’s vulnerability research team assesses significant enterprise and supply chain risk given React’s ubiquity: the impacted JavaScript library underpins modern UIs, with 168,640 dependents and more than 51 million weekly downloads.
How CVE-2025-55182 Works
CVE-2025-55182 (VulnDB ID: 428930) impacts all React versions since 19.0.0, meaning that this issue has been potentially exploitable since November 14, 2024. This vulnerability stems from how React handles payloads sent to React Server Function endpoints and deserializes them.
Flashpoint’s VulnDB entry for CVE-2025-55182
Depending on the implementation of this library, a remote, unauthenticated threat actor could send a crafted payload that would be deserialized in a way that causes remote code execution. This would lead to a total compromise of the system hosting the application, allowing for malware such as infostealers, ransomware, or cryptojackers (cryptocurrency mining) to be downloaded.
A working exploit for CVE-2025-55182 has already been published that is effective against some installations. In addition, Amazon has reported that two threat actors, attributed to Chinese Advanced Persistent Threat Groups (APTs), have begun to exploit this vulnerability. Those groups are:
Understanding the Impact and Scope of CVE-2025-55182
It is critical that security teams fully understand the potential downstream scope and impact so that they can fully focus on mitigation, rather than time-consuming research. While the vendor has provided a full disclosure, there are several important caveats to understand about CVE-2025-55182:
Applications not implementing any React Server Function endpoints may still be vulnerable as long as it supports React Server Components.
If an application’s React code does not use a server, it is not affected by this vulnerability.
Applications that do not use a framework, bundler, or bundler plugins that support React Server Components are unaffected by this vulnerability.
Additionally, several React frameworks and bundlers have been discovered to leverage vulnerable React packages in various ways. The following frameworks and bundlers are known to be affected:
next
react-router
waku
@parcel/rsc
@vitejs/plugin-rsc
rwsdk
NPMJS.com currently shows that the react-dom package, which is effectively part of React, has 168,640 dependents. This means that an incredible number of enterprise applications are likely to be affected. Nearly every commercial application is built on hundreds, sometimes thousands of components and dependencies. Furthermore, applications coded via Vibe and similar technology are also likely to leverage React: potentially amplifying the downstream risk this vulnerability poses.
How to Mitigate CVE-2025-55182
For mitigation, the React library has released versions 19.0.1, 19.1.2, and 19.2.1 that resolve the issue. Flashpoint advises organizations to upgrade their respective libraries urgently. Security teams leveraging dynamic SBOMs (Software Bill of Materials) can drastically increase risk mapping and triage for deployed React versions.
To avoid confusion, security teams should ignore CVE-2025-66478. It has been rejected for being a duplicate of the preferred CVE-2025-55182.
Mitigate Critical Vulnerabilities Using Flashpoint
Flashpoint strongly recommends security teams treat this vulnerability with utmost urgency. Our vulnerability research team will continue to monitor this vulnerability and its downstream impacts. All updates will be provided via Flashpoint’s VulnDB.
Request a demo today and gain access to quality vulnerability intelligence that helps address critical threats in a timely manner.
Flashpoint’s Top 5 Predictions for the 2026 Threat Landscape
Flashpoint’s forward-looking threat insights for security and executive teams, provides the strategic foresight needed to prepare for the convergence of AI, identity, and physical security threats in 2026.
As the global threat landscape accelerates its transformation, 2026 marks an inflection point requiring defensive strategies to fundamentally shift. The volatility observed in 2025 has paved the way for an era soon to be defined by AI-weaponized autonomy, information-stealing malware, systemic instability of public vulnerability systems, and the complete convergence of digital and physical risk.
Flashpoint offers a unique window into these complexities, providing organizations with the foresight needed to navigate what lies ahead. Drawing from Flashpoint’s leading intelligence and primary source collections, we highlight five key trends shaping the 2026 threat landscape. These insights aim to help organizations not only understand what’s next but also build the resilience needed to withstand and adapt to emerging challenges.
Prediction 1: Agentic AI Threats Will Weaponize Autonomy, Forcing a New Defensive Standard
2026 will see continued evolution of AI threats, with future attacks centering on autonomy and integration. Across the deep and dark web, Flashpoint is observing threat actors move past experimentation and into operational use of illegal AI.
As attackers train custom fraud-tuned LLMs (Large Language Models) and multilingual phishing tools directly on illicit data, these AI models will become more capable. The criminal intent shaping their misuse will also become more sophisticated. Additionally, 2026 will see a greater marketplace for paid jailbreaking communities and synthetic media kits for KYC (Know Your Customer) bypass.
These advancements are enabling criminals to move beyond simple tools and engage in scaled, autonomous fraud operations, leading to two major shifts:
Agentic AI is becoming the true flashpoint: Threat actors will be using agentic systems to automate reconnaissance, generate synthetic identities, and iterate on fraud playbooks in near real-time. In this SaaS ecosystem, AI will help attackers leverage subscription tiers and customer feedback loops at scale.
The attack surface will shift to focus on AI Integrations: Organizations are increasingly plugging LLMs into live data streams, internal tools, identity systems, and autonomous agents. This practice often lacks the same security vetting, access controls, and monitoring applied to other enterprise systems. As such, attackers will heavily target these integrations, such as APIs, plugins, and system connections, rather than the models themselves.
“The ubiquity of automation has dramatically increased attack tempo, leaving many security teams behind the curve. While automation can replace repetitive tasks across the enterprise, organizations must not make the critical mistake of substituting human judgement for AI at the intelligence level.
This is paramount because a critical threat in 2026 is Agentic AI autonomy weaponized against soft targets—API integrations and identity systems. The only winning defense will be human-led and AI-scaled, prioritizing purposeful use to keep organizations ahead of this exponential risk.”
Josh Lefkowitz, CEO at Flashpoint
These evolving AI threats will force a fundamental shift in defensive strategies. Defenders will have to shift to deploying systems around AI rather than trust them on their own.
Prediction 2: Identity Compromise via Infostealers Will Become the Foundation of Every Attack
Infostealers will become the entry point, the data broker, the reconnaissance layer, and the fuel for everything that comes after a cyberattack. This shift is already in motion and is accelerating rapidly: in just the first half of 2025, infostealers were responsible for 1.8 billion stolen credentials, an 800% spike from the start of the year. However, 2026 will redefine the malware’s role, making its most valuable output being access, rather than disruption.
Infostealers will become the upstream event that powers the rest of the attack chain. Identity and session data will be increasingly targeted, since it gives attackers immediate access into victim environments. Ransomware, fraud, data theft, and extortion will simply be downstream ways to monetize.
This upstream approach defines the new reality of the attack chain, which is already operational. Nearly every major stealer strain Flashpoint observes now exfiltrates the following:
An organization’s attack surface is no longer just composed of their own networks. It is the entire digital identity of their employees and partners. This new reality requires security teams to take a new approach. Instead of attempting to block attacks, they must proactively detect compromised credentials before they are weaponized. This will be the difference between reacting to a data breach and preventing one.
“The infostealer economy has fully industrialized the attack chain, making initial compromise a low-cost commodity. Multiple security incidents in 2025 tie back to credentials found in infostealer logs. This reality has underscored the critical importance of digital trust—specifically, verifying who can access what resources. For 2026, identity is the perimeter to watch, and security teams must proactively hunt for compromised credentials before they’re weaponized.”
Ian Gray, Vice President of Intelligence at Flashpoint
Prediction 3: CVE Volatility Will Force Redundancy in Vulnerability Intelligence
The temporary funding crisis at CVE in April 2025 and the subsequent CISA stopgap extension through March 2026 exposed the systemic fragility of a centralized vulnerability intelligence model. With the future of the CVE/NVD system hanging in the balance, 2026 will be defined by the urgent need for redundancy and diversification in vulnerability intelligence.
In today’s vulnerability intelligence ecosystem, nearly every organization’s vulnerability management framework relies on CVE and NVD—including its “alternatives” such as the EUVD (European Union Vulnerability Database). The CVE system has grown into a critical global cybersecurity utility, relied upon by nearly all vulnerability scanners, SIEM platforms, patch management tools, threat intelligence feeds, and compliance reports. A complete shutdown of CVE would result in a widespread loss of institutional infrastructure.
The next generation of security needs to be built on practices that are resilient, diversified, and intelligence-driven. It should be focused on providing insights that can be used to take action such as threat actor behavior, likelihood of exploitation in the wild, relevance to ransomware campaigns, and business context. Security teams will need to leverage a comprehensive source of vulnerability intelligence such as Flashpoint’s VulnDB that provides full coverage for CVE, while also cataloging more than 100,000 vulnerabilities missed by CVE and NVD.
Prediction 4: Executive Protection Will Remain a Critical Challenge as Cyber-Physical Threats Converge
The continued blurring of lines between cyber, physical, and geopolitical threats will elevate the risk to organizational leadership, turning executive protection into a holistic intelligence function in 2026. The rise of information warfare combined with physical world convergence means the threat to key personnel is no longer purely digital.
In the aftermath of the tragic December 2024 assassination of United Healthcare’s CEO, Flashpoint has seen the continued circulation and glorification of “wanted-style posters” of executives in extremist communities. Additionally, Flashpoint has seen nation-state actors participate, using espionage and influence to target high-value individuals. Organizations must adopt an integrated approach that connects insights from threat actor chatter and a wealth of other OSINT sources. This fusion of intelligence is essential for applying frameworks to ensure the safety of leadership and key personnel.
Prediction 5: Extortion Shifts to Identity-Based Supply Chain Risk
2025 was marked by several large-scale extortion campaigns, demonstrating how the threat landscape is rapidly evolving. Ransomware operations have shifted into a straight extortion play. Flashpoint has observed a surge in new entrants to the ransomware market, accompanied by a decline in the quality and decorum of ransomware groups.
Furthermore, vishing campaigns attributed to “Scattered Spider” have highlighted weaknesses in identity, trust, and verification. Campaigns from “Scattered LAPSUS$ Hunters” have also exposed vulnerabilities in third-party integrations. These attacks culminated in extortion, showcasing that modern attacks will target trusted users and trusted applications for initial access, and will forgo ransomware in place of data access.
As this shift continues into 2026, threat actors will increasingly focus their efforts on exploiting human behavior and identity systems. Instead of attempting to spend resources on breaking network perimeters, attackers will instead socially engineer employees to gain access to corporate systems at scale. This change in TTPs will undoubtedly greatly increase supply chain risk, especially for third parties.
Charting a Path Through an Evolving Threat Landscape with Flashpoint Intelligence
These five predictions highlight the transformative trends shaping the future of cybersecurity and threat intelligence. Staying ahead of these challenges demands more than just reactive measures—it requires actionable intelligence, strategic foresight, and cross-sector collaboration. By embracing these principles and investing in proactive security strategies, organizations can not only mitigate risks but also seize opportunities to enhance their resilience.
As the threat landscape continues to rapidly evolve, staying informed and prepared are critical components of risk mitigation. With the right tools, insights, and partnerships, security teams can navigate the complexities ahead and safeguard what matters most.
Artificial intelligence is transforming the way we work, learn, and innovate—and it’s doing so at a pace that surpasses every major technology before it. Microsoft’s inaugural AI Diffusion Report offers a comprehensive look at how AI adoption is accelerating worldwide, drawing on data from more than 100 countries. In less than three years, more than 1.2 billion people have used AI tools, a rate of adoption faster than the internet, the personal computer, or even the smartphone. This rapid diffusion underscores AI’s potential as a general-purpose technology but also highlights the urgent need to ensure equitable access.
The report introduces three indices—the AI Frontier Index, the AI Infrastructure Index, and the AI Diffusion Index—to help policymakers, researchers, and industry leaders understand where breakthroughs are happening, where capacity exists to scale, and where AI is being used to improve lives. These insights show that adoption is fastest where connectivity and digital infrastructure are strongest, while nearly four billion people still lack the basics needed to participate in the AI economy. Bridging this gap is essential to avoid deepening global divides.
Beyond the numbers, the report illustrates the need for collaborative action to expand access to digital infrastructure, strengthen skills development, and promote responsible AI policies. By investing in these foundational elements, governments and organizations can unlock AI’s potential for growth and innovation. The data makes clear that speed alone does not guarantee shared prosperity—broad accessibility does.
To explore the full findings and recommendations, read the AI Diffusion Report.
We have recently started a new blog series called #VTPRACTITIONERS. This series aims to share with the community what other practitioners are able to research using VirusTotal from a technical point of view.
Our first blog saw our colleagues at SEQRITE tracking UNG0002, Silent Lynx, and DragonClone. In this new post, Acronis Threat Research Unit (TRU) shares practical insights from multiple investigations, including the ClickFix variant known as FileFix, the long-running South Asian threat actor SideWinder, and the SVG-based campaign targeting Colombia and named Shadow Vector.
How VT plays a role in hunting for analysts
For the threat analyst, web-based threats present a unique set of challenges. Unlike file-based malware, the initial stages of a web-based attack often exist only as ephemeral artifacts within a browser. The core of the investigation relies on dissecting the components of a website, from its HTML and JavaScript to the payloads it delivers. This is where VT capabilities for archiving and analyzing web content become critical.
VT allows analysts to move beyond simple URL reputation checks and delve into the content of web pages themselves. For attacks like the *Fix family, which trick users into executing malicious commands, the entire attack chain is often laid bare within the page's source code. The analyst's starting point becomes the malicious commands themselves, such as navigator.clipboard.writeText or document.execCommand("copy"), which are used to surreptitiously copy payloads to the victim's clipboard.
The Acronis team's investigation into the FileFix variant demonstrates a practical application of this methodology. Their research began not with a specific sample, but with a hypothesis that could be translated into a set of hunting rules. Using VT's Livehunt feature, they were able to create YARA rules that searched for new web pages containing the clipboard commands alongside common payload execution tools like powershell, mshta, or cmd. This proactive hunting approach allowed them to cast a wide net and identify potentially malicious sites in real-time.
One of the main challenges in this type of hunting is striking a balance between rule specificity and the need to uncover novel threats. Overly broad rules can lead to a deluge of false positives, while highly specific rules risk missing creatively crafted commands. The Acronis team addressed this by creating multiple rulesets with varying levels of specificity, allowing them to both find known threats and uncover new variants like FileFix.
In the case of the SideWinder campaign, which uses document-based attacks, VT value comes from its rich metadata and filtering capabilities. Analysts can hunt for malicious documents exploiting specific vulnerabilities, and then narrow the results by focusing on specific geographic regions through submitter country information. This allows them to effectively isolate threats that match a specific actor's profile, such as SideWinder's focus on South Asia.
Similarly, for the Shadow Vector campaign, which used malicious SVG files to target users in Colombia, VT content search and archiving proved essential. The platform's ability to store and index SVG content allowed researchers to identify a campaign using judicial-themed lures. By combining content searches for legal keywords with filters like submitter:CO, the Acronis team could map the entire infection chain and its infrastructure, transforming fragmented indicators into a comprehensive intelligence picture.
Acronis - Success Story
[In the words of Acronis…]
Acronis Threat Research Unit (TRU) used VirusTotal’s platform for threat hunting and intelligence across several investigations, including FileFix, SideWinder, and Shadow Vector. In the FileFix case, TRU used VT’s Livehunt framework, developing rules to identify malicious web pages using clipboard manipulation to deliver PowerShell payloads. The ability to inspect archived HTML and JavaScript whitin the VirusTotal platform allowed the team to uncover not only known Fix-family attacks but also previously unseen variants that shared code patterns.
VirusTotal’s data corpus also supported Acronis TRU’s broader threat tracking. In the SideWinder campaign, VT’s metadata and sample filtering capabilities helped analysts trace targeted document-based attacks exploiting tag:CVE-2017-0199 and tag:CVE-2017-11882 across South Asia, leading to the creation of hunting rules later published in “From banks to battalions: SideWinder’s attacks on South Asia’s public sector”.
Similarly, during the “Shadow Vector targets Colombian users via privilege escalation and court-themed SVG decoys” investigation, VT’s archive of SVG content exposed a campaign targeting Colombian entities that embedded judicial lures and external payload links within SVG images. By correlating samples with metadata filters such as submitter:CO and targeted content searches for terms like href="https://" and legal keywords, the team mapped an entire infection chain and its supporting infrastructure. Across all these efforts, VirusTotal provided a unified environment where Acronis could pivot, correlate, and validate findings in real time, transforming fragmented indicators into comprehensive, actionable intelligence.
Hunting Exploits Like It’s 2017-0199 (SideWinder Edition)
SideWinder is a well-known threat actor that keeps going back to what works. Their document-based delivery chain has been active for years, and the group continues to rely on the same proven exploits to target government and defense entities across South Asia. Our goal in this hunt was to get beyond just finding samples. We wanted to understand where new documents were surfacing, who they were likely aimed at, and what types of decoys were in circulation during the latest campaign wave. VirusTotal gave us the visibility we needed to do that efficiently and at scale.
We started by digging into Microsoft Office and RTF files recently uploaded to VirusTotal that were tagged with CVE-2017-0199 or CVE-2017-11882 and coming from Pakistan, Bangladesh, Sri Lanka, and neighboring countries. By filtering based on VT metadata such as submitter country and file type, and by excluding obvious noise from bulk submissions or unrelated activity, we could narrow our focus to the samples that actually fit SideWinder’s operational profile.
/*
Checks if the file is tagged with CVE-2017-0199 or CVE-2017-11882
and originates from one of the targeted countries
and the file type is a Word document, RTF, or MS-Office file
*/
import "vt"
rule hunting_cve_maldocs {
meta:
author = "Acronis Threat Research Unit (TRU)"
description = "Hunting for malicious Word/RTF files exploiting CVE-2017-0199 or CVE-2017-11882 from specific countries"
distribution = "TLP:CLEAR"
version = "1.2"
condition:
// Match if the file has CVE-2017-0199 or CVE-2017-11882 in the tags
for any tag in vt.metadata.tags :
(
tag == "cve-2017-0199" or
tag == "cve-2017-11882"
)
// Originates from a specific country?
and
(
// Removed CN due to spam submissions of related maldocs
vt.metadata.submitter.country == "PK" or
vt.metadata.submitter.country == "LK" or
vt.metadata.submitter.country == "BD" or
vt.metadata.submitter.country == "NP" or
vt.metadata.submitter.country == "MM" or
vt.metadata.submitter.country == "MV" or
vt.metadata.submitter.country == "AF"
)
// Is it a DOC, DOCX, or RTF?
and
(
vt.metadata.file_type == vt.FileType.DOC or
vt.metadata.file_type == vt.FileType.DOCX or
vt.metadata.file_type == vt.FileType.RTF
)
// Different TA spotted using .ru TLD (excluding it for now)
and not (
for any url in vt.behaviour.memory_pattern_urls : (
url contains ".ru"
)
)
and vt.metadata.new_file
}
Next, we began translating those results into new livehunt rules. The initial version was intentionally broad: match any new document exploiting those CVEs, uploaded from a small list of countries of interest, and restricted to document file types like DOC, DOCX, or RTF. We also added logic to avoid hits that didn’t fit SideWinder’s patterns, such as samples calling out .ru infrastructure tied to other known threat clusters.
A good starting point when creating broad hunting rules is to define a daily notification limit and if everything works as expected and the level of false positives is tolerable, begin refining the rule as more and more hits come to our inbox.
It’s always a good idea to not spam your own inbox when creating broad hunting rules
In our case, the final hunting rule ended up matching a hexadecimal pattern for malicious documents used by SideWinder. By adding filters for submitter country and only triggering on new files, the rule produced a reliable feed of samples that we could confidently attribute to this actor for further analysis.
/*
Sidewinder related malicious documents exploiting CVE 2017-0199 used during 2025 campaign
*/
import "vt"
rule apt_sidewinder_documents
{
meta:
author = "Acronis Threat Research Unit (TRU)"
description = "Sidewinder related malicious documents exploiting CVE 2017-0199"
distribution = "TLP:CLEAR"
version = "1.0"
strings:
$a1 = {62544CB1F0B9E6E04433698E85BFB534278B9BDC5F06589C011E9CB80C71DF23}
$a2 = {E20F76CDABDFAB004A6BA632F20CE00512BA5AD2FE8FB6ED9EE1865DFD07504B0304140000}
condition:
filesize
Once we refined the rule set, SideWinder activity became much easier to track consistently. We began to see new decoys appear in near real time, allowing us to monitor changes in themes and spot repeated use of lure content and infrastructure across different campaigns. Using the same logic in retrohunt confirmed our observations that SideWinder had been using the same tactics for months, only changing the decoy topics while keeping the underlying delivery technique intact.
Using Retrohunt to uncover additional samples and establish the threat actor’s timeline
We also observed geofencing behavior in the delivery chain. If the server hosting the external resource did not recognize the visitor or the IP range did not match the intended target, the server often returned a benign decoy file (or an HTTP 404 error code) instead of the real payload.
While relying on exploits from 2017, SideWinder carefully filters the victims that will receive the final malicious payload
One recurring decoy had the SHA256 hash 1955c6914097477d5141f720c9e8fa44b4fe189e854da298d85090cbc338b35a, which corresponds to an empty RTF document. That decoy is useful as a hunting pivot: by searching for that hash and combining it with submitter country and file type filters in VT, you can separate likely targeted, genuine hits from broad noise and map where geofencing is being applied.
RTF empty decoy file used by SideWinder still presents valuable information for pivoting into other parts of their infrastructure
In addition, VirusTotal allowed us to trace the attack back to the initial infection vector and recover some of the spear phishing emails that started the chain. We pivoted from known samples and shared strings, and used file relations to follow linked URLs and artifacts upstream, and found an .eml file that contained the original message and attachment. One concrete example is the spear phish titled 54th CISM World Military Naval Pentathlon 2025 - Invitation.eml, indexed in VirusTotal with behavior metadata and attachments tied to the same infrastructure.
Getting initial infection spear-phishing e-mails allowed us to put together the different pieces of the puzzle, from beginning to end
For other hunters, the key takeaway is that even older exploits like CVE-2017-0199 can reveal a lot when you combine multiple VirusTotal features. In this case, we used metadata, livehunt, and regional telemetry to connect seemingly unrelated samples. We also checked hashtags and community votes, including those from researchers like Joseliyo, to cross-check our assumptions and spot ongoing discussions about similar activity. The Telemetry tab helped us see where submissions were coming from geographically, and the Threat Graph view made it easier to visualize how documents, infrastructure, and payloads were linked.
Every single data point counts when hunting for new samples
Using these tools together turned a noisy set of samples into a clear picture of SideWinder’s targeting and operations.
Uncovering Shadow Vector’s SVG-Based Crimeware Campaign in Colombia
An example of a rendered SVG lure with a judicial correspondence theme
These files mimicked official judicial correspondence and contained embedded links to externally hosted payloads, such as script-based downloaders or password-protected archives. The investigation began after we noticed an unusual pattern of SVG submissions from Colombia. By using a small set of samples for an initial rule, we began our hunt.
<!--
This YARA rule detects potentially malicious SVG files that are likely being used for crimeware campaigns targeting Colombia.
The rule identifies SVG images that contain legal or judicial terms commonly used in phishing scams,
along with embedded external links that could be used to deliver a payload.
-->
import "vt"
rule crimeware_svg_colombia {
meta:
author = "Acronis Threat Research Unit (TRU)"
description = "Detects potentially malicious SVG files that are likely being used for crimeware campaigns targeting Colombia"
distribution = "TLP:CLEAR"
version = "1.1"
// Reference hashes
hash1 = "6d4a53da259c3c8c0903b1345efcf2fa0d50bc10c3c010a34f86263de466f5a1"
hash2 = "2aae8e206dd068135b16ff87dfbb816053fc247a222aad0d34c9227e6ecf7b5b"
hash3 = "4cfeab122e0a748c8600ccd14a186292f27a93b5ba74c58dfee838fe28765061"
hash4 = "9bbbcb6eae33314b84f5e367f90e57f487d6abe72d6067adcb66eba896d7ce33"
hash5 = "60e87c0fe7c3904935bb1604bdb0b0fc0f2919db64f72666b77405c2c1e46067"
hash6 = "609edc93e075223c5dc8caaf076bf4e28f81c5c6e4db0eb6f502dda91500aab4"
hash7 = "4795d3a3e776baf485d284a9edcf1beef29da42cad8e8261a83e86d35b25cafe"
hash8 = "5673ad3287bcc0c8746ab6cab6b5e1b60160f07c7b16c018efa56bffd44b37aa"
hash9 = "b3e8ab81d0a559a373c3fe2ae7c3c99718503411cc13b17cffd1eee2544a787b"
hash10 = "b5311cadc0bbd2f47549f7fc0895848adb20cc016387cebcd1c29d784779240c"
hash11 = "c3319a8863d5e2dc525dfe6669c5b720fc42c96a8dce3bd7f6a0072569933303"
hash12 = "cb035f440f728395cc4237e1ac52114641dc25619705b605713ecefb6fd9e563"
hash13 = "cf23f7b98abddf1b36552b55f874ae1e2199768d7cefb0188af9ee0d9a698107"
hash14 = "f3208ae62655435186e560378db58e133a68aa6107948e2a8ec30682983aa503"
strings:
// SVG
$svg = "<svg xmlns=" ascii fullword
// Documents containing legal or judicial terms
$s1 = "COPIA" nocase
$s2 = "CITACION" nocase
$s3 = "JUZGADO" nocase
$s4 = "PENAL" nocase
$s5 = "JUDICIAL" nocase
$s6 = "BOGOTA" nocase
$s7 = "DEMANDA" nocase
// When image loads it retrieves payload from external website using HTTPS
$href1= "href='https://" nocase
$href2 = "href=\"https://" nocase
condition:
$svg
and filesize < 3MB
and 3 of ($s*)
and any of ($href*)
and vt.metadata.submitter.country == "CO"
}
By including reference hashes from manually verified samples, we used a broad hunting rule both as detection mechanism and a pivot point for uncovering related infrastructure or newly generated lures.
Once the initial hunting logic was in place, we refined it into a livehunt rule specifically tailored for SVG-based decoys. The rule matched files containing judicial terminology and outbound HTTPS links, while filtering by file size and origin to reduce false positives. Using this rule, we began collecting and analyzing related uploads.
We used the VT Diff functionality to compare variations between samples and quickly spot patterns, such as repeated words, hexadecimal values, URLs, or metadata tags that hinted at automated generation (i.e. the string “Generado Automaticamente”).
VT Diff feature helped us to identify patterns
Results of our VT Diff session
While we could not conclusively attribute the SVG decoy campaign to Blind Eagle at the time of research, the technical and thematic overlaps were difficult to ignore. The VT blog “Uncovering a Colombian Malware Campaign with AI Code Analysis” describes similar judicial-themed SVG files used as lures in operations targeting Colombian users. As with other open reports on this threat actor, attribution remains based on cumulative evidence, clustering campaigns based on commonalities such as infrastructure reuse, phishing template design, malware family selection, and linguistic or regional indicators observed across samples.
The evolution from the initial hunting rule to the refined detection rule illustrates our approach to threat hunting in VT, iterative and continuously refined through testing and analysis. The first rule was broad, meant to surface related samples and reveal the full scope of the campaign. It proved useful in livehunt and retrohunt, helping us find clusters of judicial-themed SVGs and their linked payloads. As the investigation progressed, we focused on precision, reducing false positives and removing elements that did not add value. Tuning a rule is always a balance: removing one pattern might miss some samples, but it can also make the rule more accurate and easier to maintain.
FileFix in the wild!
A few weeks ago, the TRU team at Acronis released research on a (at the time) rarely seen variant of the ClickFix attack, called FileFix. Much of the investigation of this attack vector was possible thanks to VirusTotal’s ability to archive, search, and write rules for finding web pages. We, at Acronis, together with VT, wanted to share a bit of information on how we did it- so that others can better research this type of emerging threat.
Anatomy of an attack- where do we start?
Like many phishing attacks, *Fix attacks rely on malicious websites where victims are tricked into running malicious commands. Lucky for us, these attacks have a few particular components that are in common to all, or many, *Fix attacks. Using VT, we were able to write rules and livehunt for any new web pages which included these components, and were able to quickly reiterate on rules that were too broad.
One thing all *Fix attacks have in common, is that they copy a malicious command to the victims clipboard- copying the malicious command, rather than letting the user copy the command themselves, allows attackers to try to hide the malicious part of the command from the victim, and only allow for a smaller, “benign” portion of the command to appear when they copy it into their Windows Run Dialogue or address bar. This commonality gives us two great strings to hunt for:
The commands used to copy text into the victims clipboard
The commands used to construct the malicious payload
We began our research by using the Livehunt feature, and wrote a rule to detect navigator.clipboard.writeText and document.execCommand("copy"), both used for copying into clipboard, as well as any string including the words powershell, mshta, cmd, and other commands we find commonly used in *Fix attacks. At its most basic form, a rule might look like this:
import "vt"
rule ClickFix
{
strings:
$clipboard = /(navigator\.clipboard\.writeText|document\.execCommand\(\"copy\"\))/
$pay01 = /(powershell|cmd|mshta|msiexec|pwsh)/gvfi
condition:
vt.net.url.new_url and
$clipboard and
any of ($pay*)
}
However, this is far from enough. There are plenty of benign sites that use the copy to clipboard feature, and also have the words powershell or cmd present (the three letters “cmd” appear often as part of Base64 strings). This makes things a bit more tricky, as it requires us to iron out these false positives. We need to make our patterns look more similar to real powershell or cmd commands.
Unfortunately, there is such a huge variance in how these commands are written, that the more rigid our patterns became, the more likely it was for us to miss a true positive that included something we haven’t seen before or couldn’t think of. This requires a balancing act- if your rules are too rigid, you will miss true positives that employ a creatively crafted command; too loose and you will receive a large number of false positives, which will slow down investigation.
For example, we can try narrowing down our rule to include more true positives of powershell commands by searching for a string that’s better resembling some of the powershell commands we’ve seen as part of a ClickFix payload, by including the “iex” cmdlet, which tells the powershell command to execute a command:
$pay03 = /powershell.{,80}iex/
This will match whenever the word powershell appears, with the word iex appearing 0 to 80 characters after it. This should reduce the number of false positives we see related to powershell, as it more clearly resembles a powershell command, but at the same time limits our rule to only catch powershell commands that follow this structure- any true positive command with more than 80 characters between the word powershell and iex, or commands forgoing the use of iex, will not be caught.
We ended up setting a number of separate rulesets, some were more specific, others more generic. The more generic ones helped us tune our more specific rulesets. This tactic allowed us to find a large number of ClickFix attacks. Most were run of the mill fake captchas, leveraging ClickFix, others were more interesting. As we continued fine tuning our rules, and within a week of setting up our Livehunt, one of our more generic rules has made an interesting detection. At first glance, it appeared to be a false positive, but as we looked closer, we discovered that it’s exactly what we were hoping to find- a FileFix attack.
Analyzing payloads
One of the nicest things about researching a *Fix attack is that the payload is right there on the website, right in plain site. This offers a few advantages- the first is that we can examine the payload even when the phishing site itself is down, as long as it’s archived by VT. The second advantage is we can further search for similar patterns on VT via VT queries to try and catch other attacks from the same campaign.
Payloads are visible directly in VT, by using the content tab on any suspected website (and in this case- obfuscated)
Often, these payloads may contain additional malicious urls which are used to download and execute additional payloads. These can also very easily be examined on VT, and any files they lead to may also be downloaded directly from VT.
In our investigation of the FileFix site, we found that the payload (a powershell command) downloads an image, and then runs a script that is embedded in the image file. That second-stage script then decrypts and extracts an executable from the image and runs it.
FileFix site downloading and extracting code from an image (highlighted)
We were using both a VM and VT to investigate these payloads. One interesting way we were able to use VT is to track additional examples of the malicious images, as parts of the command were embedded as strings in the image file, allowing us to match these patterns via a VT query and find new examples of the attack, or by searching for the file name or the domain which hosts it.
Pivoting on the domain hosting malicious .jpg files, to investigate additional stages of the attack, archived by VT
VT has been extremely helpful in allowing us to very easily analyze malicious URLs used not only for phishing, but also for delivering malware and additional scripts. In some examples, we were able to get quite far along the chain of scripts and payloads without ever having to spin up a VM, just by looking at the content tab, to see what’s inside a particular file. That’s not going to be the case every time, but it’s certainly nice when it does happen.
The malicious images used during the attack contain parts of the malicious code used in the second stage of the attack
By pivoting on specific strings from within that code, we are able to locate other samples of the malicious images and scripts created by the same attacker, and further pivot to uncover their infrastructure
The ability to investigate and correlate various stages, or multiple samples from the same attacker, were a huge boon to us during the investigation. It allowed us to quickly connect the dots without leaving VT, and should be a great asset in your investigation.
Looking for a *Fix
So now that you know all this- what's next? How can this be useful? Well, we hope it can be helpful in a number of ways.
Firstly, working together as a community, it is important that we continue to catch and block URLs that are employing *Fix attacks. It’s not easy to detect a *Fix site dynamically, and prevention may still happen in many cases after the payload has already been run. Maintaining a robust blocklist remains a very good and accessible option for stopping these threats.
Secondly, those of us interested in continuing to track this threat and follow its evolution may use this to find these threats and potentially automate detection. As a side note, *Fix attacks are great investigation topics for those of us starting out in security, and as long as appropriate precautions are taken, it can be relatively safely investigated via VT, and can be very useful for learning about malicious commands, phishing sites, etc.
Thirdly, for those of us protecting organizations, this can be a useful guide for finding these attacks by yourself, in the wild, in order to gain a deeper understanding of how they operate, and what relevant ways you can find to defend your organization, although there are certainly many reports written on the subject which would also come in handy.
VT Tips (based on the success story)
[In the words of VirusTotal…]
The Acronis team’s investigation into FileFix, SideWinder, and ShadowVector is a goldmine of threat hunting techniques. Let’s move beyond the narrative and extract some advanced, practical methods you can apply to your own hunts for web-based threats and multi-stage payloads.
Supercharge Your Web-Content YARA Rules
A simple YARA rule looking for clipboard commands and "powershell" is a good start, but attackers know this. You can significantly improve your detection rate by building rules that look for the context in which these commands appear.
Instead of a generic search, try focusing on the obfuscation and page structure common in these attacks. For instance, attackers often hide their malicious script inside other functions or encoded strings. Your YARA rules can hunt for the combination of a clipboard command and indicators of de-obfuscation functions like atob() (for Base64) or String.fromCharCode.
Combine content searches with URL metadata. The content modifier is also available for URLs, when you set the entity to url you can use the content modifier to search for strings within the URL content. For example, the next query can be useful to identify potential ClickFix URLs combining some of the findings shared by Acronis and potential strings used to avoid detections.
entity:url (content:"navigator.clipboard.writeText" or content:"document.execCommand(\"copy\")") (content:"String.fromCharCode" or content:"atob")
Dissect Payloads with Advanced Content Queries
When you find a payload, as Acronis did within the FileFix site's source code, your job has just begun. The next step is to find related samples. Attackers often reuse code, and even when they obfuscate their scripts, unique strings or logic patterns can give them away. Isolate unique, non-generic parts of the script. Look for:
Custom function names
Specific variable names
Uncommon comments
Unique sequences of commands or API calls
Focus on the unobfuscated parts of the code. In the FileFix payload, the attackers might obfuscate the C2 domain, but the PowerShell command structure used to decode and run it could be consistent across samples. Use that structure as your pivot. For example, if a payload uses a specific combination of [System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String(...)), you can build a query to find other files using that exact deobfuscation chain.
Acronis has been tracking SideWinder in a very intelligent way. Their experience with VirusTotal is evident. Most of our users use VirusTotal primarily for file analysis, but sometimes we forget that there are powerful features for tracking infrastructure through livehunt.
In the SideWinder intrusions, there is a continuously monitored hash that corresponds to a
decoy file, and this file is downloaded from different URLs.
ITW URLs means that these URLs were downloading the file being studied, in this case the RTF decoy file
An interesting way to proactively identify new URLs quickly is by creating a YARA rule in livehunt for URLs, where the objective is to discover new URLs that are downloading that specific RTF decoy file.
import "vt"
rule URLs_Downloading_Decoy_RTF_SideWinder {
meta:
target_entity = "url"
author = "Virustotal"
description = "This YARA rule identify new URLs downloading the decoy file related to SideWinder"
condition:
vt.net.url.downloaded_file.sha256 == "1955c6914097477d5141f720c9e8fa44b4fe189e854da298d85090cbc338b35a"
and vt.net.url.new_url
}
Another approach that could also be interesting is to directly query the itw_urls relationship of the decoy file using the API. One use case could be creating a script that regularly (perhaps daily) calls the relationship API, retrieves the URLs, stores them in a database, and then repeats the call each day to identify new URLs. It's a simple, yet effective way to integrate with technology that any company might already have.
The following code snippet can be executed in
Google Colab and once you establish the API Key, you will obtain all the itw_urls related to the decoy file in the all_itw_urls variable.
!pip install vt-py nest_asyncio
import getpass, vt, json, nest_asyncio
nest_asyncio.apply()
cli = vt.Client(getpass.getpass('Introduce your VirusTotal API key: '))
FILEHASH = "1955c6914097477d5141f720c9e8fa44b4fe189e854da298d85090cbc338b35a"
RELATIONS = "itw_urls"
all_itw_urls = []
async for itemobj in cli.iterator(f'/files/{FILEHASH}/{RELATIONS}', limit=0):
all_itw_urls.append(itemobj.to_dict())
The great forgotten one: VT Diff
When we read researchs using VT Diff, we are pleased, as it is a tool that is truly good for creating YARA rules.
When analyzing a set of related samples, use the VT Diff feature to spot commonalities and variations. This can help you identify patterns, such as repeated strings, hardcoded values, or metadata artifacts that indicate automated generation.
As the Acronis team notes, "We used the VT Diff functionality to compare variations between samples and quickly spot patterns, such as repeated words, hexadecimal values, URLs, or metadata tags that hinted at automated generation (i.e. the string “Generado Automaticamente”)".
You can easily use VT Diff from multiple places: intelligence search results, collections, campaigns, reports, VT Graph…
The examples shared by the Acronis Threat Research Unit in tracking campaigns like FileFix, SideWinder, and Shadow Vector demonstrates the power of VT as a comprehensive threat intelligence and hunting platform. By leveraging a combination of proactive Livehunt rules, deep content analysis, and rich metadata pivoting, security researchers can effectively uncover and track elusive and evolving threats.
These examples highlight that successful threat hunting is not just about having the right tools, but about applying creative and persistent investigation techniques. The ability to pivot from a simple YARA rule to a full-fledged campaign analysis, as Acronis did, is crucial to connecting the dots and revealing the full scope of an attack. From hunting for clipboard manipulation in web-based threats to tracking decade-old exploits and analyzing malicious SVG decoys, the Acronis team has demonstrated a deep understanding of modern threat hunting, and we appreciate them sharing their valuable insights with the community.
We hope this blog have been insightful and will help you in your own threat-hunting endeavors. The fight against cybercrime is a collective effort, and the more we share our knowledge and experiences, the stronger we become as a community.
If you have a success story of using VirusTotal that you would like to share with the community, we would be delighted to hear from you. Please reach out to us, and we will be happy to feature your story in a future blog post at practitioners@virustotal.com.
Together, we can make the digital world a safer place.
This November, we’re celebrating the power of VirusTotal Enterprise search!
All VirusTotal customers will enjoy uncapped searches through the GUI — no quota consumption for the entire month so long as it is manual searches via the web interface.
Whether you’re investigating malware campaigns, analyzing infrastructure, or tracking threat actor activity, this is your chance to search freely and explore advanced use cases using VirusTotal Intelligence.
Experiment with powerful VT search modifiers to uncover patterns, hunt for related samples, and pivot across hashes, domains, IP addresses, or URLs — without worrying about your quota.
What’s happening
No quota consumption for all GUI searches during November (API interaction will continue to consume).
Every day, we’ll share interesting and creative search queries on our LinkedIn and X channels using the hashtag #MonthOfVTSearch.
We invite you to try these searches, interact with us, and share your own search tips and findings with the community.
Learn and level up
Make the most of this month to sharpen your threat-hunting skills:
This search helps identify document files that, when executed in a sandbox environment, show behavior consistent with potential malicious activity involving .ru infrastructure. It specifically looks for:
Documents (type:document) that were uploaded to VT.
During execution, they show process behavior containing:
HTTP traffic (behavior_processes:*http*)
The string DavSetCookie (often observed in HTTP request headers or custom cookie operations)
And references to .ru domains
And additionally, they show network or embedded indicators related to .ru domains via:
Behavior-based network connections (behavior_network:*.ru*), or
Embedded domains or URLs within the file (embedded_domain:*.ru*, embedded_url:*.ru*)
Join the community
Let’s make November a month of discovery and collaboration!
Tag your posts with #MonthOfVTSearch, share your favorite searches, and show the world how you use VirusTotal to explore and understand the threat landscape.
One of the best parts of being at VirusTotal (VT) is seeing all the amazing ways our community uses our tools to hunt down threats. We love hearing about your successes, and we think the rest of the community would too.
That's why we're so excited to start a new blog series where we'll be sharing success stories from some of our customers. They'll be giving us a behind-the-scenes look at how they pivot from an initial clue to uncover entire campaigns.
To
kick things off, we're thrilled to have our friends from SEQRITE
join us. Their APT-Team
is full of incredible threat hunters, and they've got a great story to share about how they've used VT to
track some sophisticated actors.
How VT plays a role in hunting for analysts
For a threat analyst, the hunt often begins with a single, seemingly isolated clue—a suspicious file, a strange domain, or an odd IP address. The challenge is to connect that one piece of the puzzle to the larger picture. This is where VT truly shines.
VT is more than just a tool for checking if a file is malicious. It's a massive, living database of digital artifacts (process activity, registry key activity, memory dumps, LLM verdicts, among others) and their relationships. It allows analysts to pivot from one indicator of compromise to another, uncovering hidden connections and mapping out entire attack campaigns. It's this ability to connect the dots—to see how a piece of malware communicates with a C2 server, what other files are associated with it, what processes were launched or files were used to set persistence or exfiltrate information, and who else has seen it—that transforms a simple file check into a full-blown investigation. The following story from SEQRITE is a perfect example of this process in action.
Seqrite - Success Story
[In the words of SEQRITE…]
We at SEQRITE APT-Team perform a lot of activities, including threat hunting and threat intelligence, using customer telemetry and multiple other data corpuses. Without an iota of doubt, apart from our customer telemetry, the VT corpus has aided us a decent amount in converting our research, which includes hunting unique campaigns and multiple pivots that have led us to an interesting set of campaigns, ranging across multiple spheres of Asian geography, including Central, South, and East Asia.
UNG0002
SEQRITE
APT-Team have been tracking a south-east asian threat entity, which was termed as UNG0002,
using certain behavioral artefacts, such using similar OPSEC mistakes across multiple campaigns and using
similar set of decoys and post-exploitation toolkit across multiple operational campaigns ranging from May
2024 to May 2025.
During
the initial
phase
of this campaign, the threat actor performed multiple targets across Hong Kong and Pakistan against
sectors involving defence, electrotechnical, medical science, academia and much more.
VT corpus has helped us to pivot through Cobalt Strike oriented beacons, which were used by this threat actor to target various sectors. In our hunt for malicious activity, we discovered a series of Cobalt Strike beacons. These were all delivered through similar ZIP files, which acted as lures. Each ZIP archive contained the same set of file types: a malicious executable, along with LNK, VBS, and PDF decoy files. The beacons themselves were also similar, sharing configurations, filenames and compilation timestamps.
Using
the timestamps from the malicious executables and the filenames previously mentioned, we discovered up to 14
different samples, all of them related to the campaign with this query
based on the configuration extracted by VT, we could use the public key extracted to identify more samples
using exactly the same with the following query
Besides
these executables, we mentioned that there were also LNK
files within the ZIP files. After analyzing them, a consistent LNK-ID metadata revealed the same identifiers
across many samples. Querying
VT for those LNK-IDs exposed we could identify new files related to the campaign.
VirusTotal query: metadata:"laptop-g5qalv96"
Decoy documents identified within the ZIP files mentioned above
We initially tracked several campaigns leveraging LNK-based device IDs and Cobalt Strike beacons. However, an intriguing shift began to emerge in the September-October activity. We observed a new set of campaigns that frequently used CV-themed decoys, often impersonating students from prominent Chinese research institutions.
While the spear-phishing tactics remained similar, the final execution changed. The threat actors dropped their Cobalt Strike beacons and pivoted toward DLL-Sideloading for their payloads, all while keeping the same decoy theme. This significant change in technique led us to identify a second major wave of this activity, which we're officially labeling Operation AmberMist.
Tracking this second wave of operations attributed to the UNG0002 cluster, we observed a recurring behavioral artifact: the use of academia-themed lures targeting victims in China and Hong Kong.
Across these campaigns, multiple queries were leveraged, but a consistent pattern emerged—heavy reliance on LOLBINS such as wscript.exe, cscript.exe, and VBScripts for persistence.
By
developing a simple yet effective hunting query,
we were able to uncover a previously unseen sample not publicly reported:
type:zip AND (metadata:"lnk" AND metadata:".vbs" AND metadata:".pdf") and submitter:HK
VirusTotal query: type:zip AND (metadata:"lnk" AND metadata:".vbs" AND metadata:".pdf") and submitter:HK
Silent Lynx
Another
campaign tracked by the SEQRITE APT-team, named Silent
Lynx,
targeted multiple sectors including banking. As in the previous described case, thanks to VT we were able to
pivot and identify new samples associated with this campaign.
Initial Discovery and Pivoting
During
the initial phase of this campaign, we discovered a decoy-based
SPECA-related archive file
targeting Kyrgyzstan around December 2024 - January 2025. The decoy was designed to distract from the real
payload: a malicious C++ implant.
Decoy document identified during our research
Email identified during our reserach
We performed multiple pivots focusing on the implant, starting by analyzing the sample’s metadata and network indicators and functionalities, we found that the threat actor had been using a similar C++ implant, which led us to another campaign targeting the banking sector of Kyrgyzstan related to Silent Lynx too.
Information obtained during the analysis of the C++ implants
Information obtained during the analysis of the C++ implants
We
leveraged VT corpus for deploying multiple Livehunt
rules on multiple junctures, some of the simpler examples are as follows:
Looking
at the usage of encoded Telegram Bot based payload inside the C++ implant. Using
either content or malware_config modifiers when extracted from the config could help us to identify
new samples.
Spawning
Powershell.exe LOLBIN.
VT
search enablers for checking for malicious email files, if uploaded from Central Asian
Geosphere.
ISO-oriented
first-stagers.
Multiple
behavioral overlaps between YoroTrooper & Silent Lynx and further hunting hypothesis developed
by us.
Leveraging VT corpus and using further pivots on the above metrics and many others included on the malicious spear-phishing email, we also tracked some further campaigns. Most importantly, we developed a new YARA rule and a new hypothesis every time to hunt for similar implants leveraging the Livehunt feature depending on the tailored specifications and the raw data we received during hunting keeping in mind the cases of false positives and false negatives.
Decoy document identified during our hunting activities
Submissions identified in the decoy document
The
threat actor repeatedly used the same implant across multiple campaigns in Uzbekistan
and
Turkmenistan.
Using hunting queries
through VT along with submitter:UZ or submitter:TM helped us to identify these samples.
The
most important pivot
in our investigation was the malware sample itself as shown in the previous screenshots was the usage of
encoded PowerShell blob
spawning powershell.exe,
which was used multiple times across different campaigns. This sample acted as a key indicator, allowing us
to uncover other campaigns targeting critical sectors in the region, and confirmed the repetitive nature of
the actor's operations.
Also,
thanks to VT feature of collections,
we further leveraged it to build an attribution of the threat entity.
Collections used during the attribution process
DragonClone
Finally,
the last campaign that we wanted to illustrate how pivoting within the VT ecosystem enabled our team to
uncover new samples was by a group we named DRAGONCLONE
The SEQRITE APT Team has been monitoring DRAGONCLONE as they actively target critical sectors across Asia and the globe. They utilize sophisticated methods for cyber-espionage, compromising strategic organizations in sectors like telecom and energy through the deployment of custom malware implants, the exploitation of unpatched vulnerabilities, and extensive spear-phishing.
Initial Discovery
Recently,
on 13th
May,
our team discovered a malicious
ZIP
file
that surfaced across various sources, including VT. The ZIP file was used as a preliminary infection vector
and contained multiple EXE and DLL files inside the archive, like this
one
which contains the malicious payload.
Chinese-based
threat actors have a well-known tendency to deliver DLL
sideloading implants
as part of their infection chains. Leveraging crowdsourced
Sigma rules in VT,
along with personal hunting techniques using static YARA
signatures,
we were able to track and hunt this malicious spear-phishing attachment effectively. In their public
Sigma
Rules list
you can find different Sigma Rules that are created to identify DLL SideLoading.
Pivoting Certificates via VT Corpus
While
exploring the network of related artifacts, we could not initially find any direct commonalities. However, a
particular clean-looking executable
named “2025 China Mobile Tietong Co., Ltd. Internal Training Program” raised our concern. Its naming and
metadata suggested potential masquerading behavior, making it a critical pivot point that required deeper
investigation.
Certificates
are one of the most key indicators, while looking into malicious artefacts, we saw that it is a fresh and
clean copy of WonderShare’s Repairit Software, a well known software for repairing corrupted files, whereas
a suspicious concern is that it has been signed by ShenZhen
Thunder NetWorking Technologies Ltd
Using this hunch, we discovered and hunted for executables, which have been signed by similar and found there have been multiple malicious binaries, although, this has not been the only indicator or pivot, but a key one, to research for further ones.
Pivoting on Malware Configs via VT Corpus
We analyzed the loader and determined it's slightly advanced, performing complex tasks like anti-debugging. More significantly, it drops V-Shell, a post-exploitation toolkit. V-Shell was originally open-source but later taken down by its authors and has been observed in campaigns by Earth Lamia.
After
extracting the V-Shell shellcode, we discovered an unusual malware configuration property: qwe123qwe.
By leveraging the VT corpus to pivot
on this finding, we were able to identify additional V-Shell implant samples potentially linked to this
campaign.
VirusTotal query: malware_config:"qwe123qwe"
VT Tips (based on the success story)
[In the words of VirusTotal…]
Threat hunting is an art, and a good artist needs the right tools and techniques. In this section, we'll share some practical tips for pivoting and hunting within the VirusTotal ecosystem, inspired by the techniques used in the campaigns discussed in this blog post.
Hunt by Malware Configuration
Many malware families use configuration files to store C2 information, encryption keys, and other operational data. For some malware families, VirusTotal automatically extracts these configurations. You can use unique values from these configurations to find other samples from the same campaign.
For instance, in the DRAGONCLONE investigation, the V-Shell implant had an unusual malware configuration property: qwe123qwe. A simple query like malware_config:"qwe123qwe" in VT can reveal other samples using the same configuration. Similarly, the Cobalt Strike beacons used by UNG0002 had a unique public key in their configuration that could be used for pivoting. That's thanks to Backscatter. We've written blogs showing how to do advanced hunting using only the malware_config modifier. Remember that you can search for samples by family name like malware_config:"redline" up to Telegram tokens and even URLs configured in the malware configuration like malware_config:"https://steamcommunity.com/profiles/76561198780612393".
Don't Overlook LNK File Metadata
Threat actors often make operational security (OPSEC) mistakes. One common mistake is failing to remove metadata from files, including LNK (shortcut) files. This metadata can reveal information about the attacker's machine, such as the hostname.
In the UNG0002 campaign, the actor consistently used LNK files with the same metadata, specifically the machine identifier laptop-g5qalv96. We know that this information can be also modified by them to deceive security researchers, but often we observe good information that can be used to track them. This allowed the SEQRITE team to uncover a wider set of samples by querying VirusTotal for this metadata string.
Track Actors via Leaked Bot Tokens
Some malware, especially those using public platforms for command and control, will have hardcoded API tokens. As seen in the "Silent Lynx" campaign, a PowerShell script used a hardcoded Telegram bot token for C2 communication and data exfiltration.
These
tokens can be extracted from memory dumps during sandbox execution or from the malware's code itself. Once
you have a token, you may be able to track the threat actor's commands and even identify other victims, as
was done in the Silent
Lynx investigation. A concrete example of using Telegram bot tokens is the query malware_config:"bot7213845603:AAFFyxsyId9av6CCDVB1BCAM5hKLby41Dr8", which is associated with four infostealer samples uploaded between 2024 and 2025.
Leverage Code-Signing Certificates
Threat actors sometimes sign their malicious executables to make them appear legitimate. They may use stolen certificates or freshly created ones. These certificates can be a powerful pivot point.
In the DRAGONCLONE case, a suspicious executable was signed by "ShenZhen Thunder Networking Technologies Ltd.". By searching for other files signed with the same certificate (signature:"ShenZhen Thunder Networking Technologies Ltd."), you can uncover other tools in the attacker's arsenal.
Utilize YARA and Sigma Rules
For proactive hunting, you can develop your own YARA rules to find malware families based on unique strings, code patterns, or other characteristics. This was a key technique in the "Silent Lynx" campaign for hunting similar implants.
Additionally,
you can leverage the power of the community by using crowdsourced
Sigma rules
in VirusTotal, even within
your YARA rules.
These rules can help you identify malicious behaviors, such as the DLL sideloading techniques used by
DRAGONCLONE, directly from sandbox execution data.
For example, If you want to search for the Sigma rule "Potential DLL Sideloading Of MsCorSvc.DLL" in VT files, you can use the query sigma_rule:99b4e5347f2c92e8a7aeac6dc7a4175104a8ba3354e022684bd3780ea9224137 to do so. All the Sigma rules are updated from the public repo and can be consumed here.
Conclusion
The success stories of the SEQRITE APT-Team in tracking campaigns like UNG0002, Silent Lynx, and DRAGONCLONE demonstrate the power of VirusTotal as a collaborative and comprehensive threat intelligence platform. By leveraging a combination of malware configuration analysis, metadata pivoting, and community-driven tools like YARA and Sigma rules, security researchers can effectively uncover and track sophisticated threat actors.
These examples highlight that successful threat hunting is not just about having the right tools, but also about applying creative and persistent investigation techniques. The ability to pivot from one piece of evidence to another is crucial in connecting the dots and revealing the full scope of a campaign. The SEQRITE team has demonstrated a deep understanding of these pivoting techniques, and we appreciate that they have decided to share their valuable insights with the rest of the community.
We hope these tips and stories have been insightful and will help you in your own threat-hunting endeavors. The fight against cybercrime is a collective effort, and the more we share our knowledge and experiences, the stronger we become as a community.
If you have a success story of using VirusTotal that you would like to share with the community, we would be delighted to hear from you. Please reach out to us, and we will be happy to feature your story in a future blog post at practitioners@virustotal.com.
Together, we can make the digital world a safer place.
In Part 2, we’re diving headfirst into one of the most critical attack surfaces in the LLM ecosystem - Prompt Injection: The AI version of talking your way past the bouncer.
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