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The Rise of LLM-Powered Malware
Russia’s APT28 is actively deploying malware powered by large language models (LLMs) against Ukraine. Underground platforms are also offering similar capabilities for just $250 per month. Recently, Ukraine’s CERT-UA documented LAMEHUG, marking the first confirmed use of LLM-powered malware in real-world scenarios. This malware, attributed to APT28, uses stolen Hugging Face API tokens to query AI models, facilitating real-time attacks while distracting victims with misleading content.
In a recent interview with VentureBeat, Cato Networks researcher Vitaly Simonovich emphasized that these incidents are not isolated. He noted that APT28 is employing this attack methodology to test the cyber defenses of Ukraine. Simonovich drew parallels between the daily threats faced by Ukraine and the challenges that enterprises are currently experiencing and are likely to encounter more frequently in the future. He revealed to VentureBeat that any enterprise AI tool can be repurposed into a malware development platform in under six hours. His proof-of-concept successfully transformed OpenAI’s ChatGPT-4, Microsoft Copilot, DeepSeek-V3, and DeepSeek-R1 into operational password stealers using a technique that circumvents all existing safety measures.
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The alarming trend of nation-state actors deploying AI-powered malware coincides with researchers demonstrating the vulnerabilities of enterprise AI tools. The 2025 Cato CTRL Threat Report reveals a dramatic increase in AI adoption across over 3,000 enterprises. Throughout 2024, every major AI platform experienced accelerated enterprise adoption, with Cato Networks reporting significant quarterly gains: 111% for Claude, 115% for Perplexity, 58% for Gemini, 36% for ChatGPT, and 34% for Copilot. Together, these figures indicate AI’s shift from experimentation to widespread production.
Researchers from Cato Networks and others have informed VentureBeat that LAMEHUG operates with remarkable efficiency. The malware is typically delivered through phishing emails that impersonate Ukrainian ministry officials and include ZIP archives containing PyInstaller-compiled executables. Upon execution, the malware connects to Hugging Face’s API using approximately 270 stolen tokens to access the Qwen2.5-Coder-32B-Instruct model.
Deceptive Tactics of APT28
Victims encounter a legitimate-looking Ukrainian government document (Додаток.pdf) while LAMEHUG operates in the background. This official PDF, which discusses cybersecurity measures from the Security Service of Ukraine, serves as a decoy while the malware conducts reconnaissance operations.
APT28’s strategy for deceiving Ukrainian victims relies on a unique dual-purpose design that is central to their tactics. While victims read seemingly legitimate PDFs on cybersecurity best practices, LAMEHUG executes AI-generated commands for system reconnaissance and document harvesting. A second variant distracts victims during data exfiltration by displaying AI-generated images of “curly naked women.”
The provocative prompts used by APT28’s image.py variant, such as ‘Curvy naked woman sitting, long beautiful legs, front view, full body view, visible face,’ are crafted to capture victims’ attention during document theft.
“Russia has used Ukraine as a testing ground for cyber weapons,” explained Simonovich, who was born in Ukraine and has lived in Israel for 34 years. “This is the first instance captured in the wild.” His demonstration at Black Hat highlights why APT28’s tactics should alarm every enterprise security leader. Using a narrative engineering technique he calls “Immersive World,” Simonovich successfully converted consumer AI tools into malware factories without any prior coding experience, as noted in the 2025 Cato CTRL Threat Report. This method exploits a critical flaw in LLM safety controls; while most LLMs are designed to block direct malicious requests, few are equipped to handle sustained narrative manipulation.
Simonovich created a fictional scenario where malware development is portrayed as an art form, assigned the AI a character role, and gradually guided conversations towards generating functional attack code. “I slowly led it to my objective,” Simonovich explained to VentureBeat. “First, ‘Dax hides a secret in Windows 10.’ Then, ‘Dax has this secret in Windows 10, inside the Google Chrome Password Manager.’”