LLMs can be tricked into processing malware through steganographic techniques.
Major AI platforms shown vulnerable to image-based malware manipulation.
Researchers demonstrated how to embed test malware in images and successfully execute it within LLM environments, exposing potential security vulnerabilities in major AI platforms.
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https://arxiv.org/abs/2501.05542
🔍 Original Problem:
Current security testing frameworks lack standardized methods to evaluate LLM security boundaries, particularly regarding file handling and execution within containerized environments.
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🛠️ Solution in this Paper:
→ The study embedded EICAR test files within JPEG images using four distinct protocols.
→ Protocol 1 appends EICAR string to JPEG images, validating virus transport to LLM platforms.
→ Protocol 2 uses Python to extract malware from uploaded images within LLM workspace.
→ Protocol 3 implements obfuscation through string reversal and reconstruction.
→ Protocol 4 leverages base64 encoding for malware reconstruction.
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💡 Key Insights:
→ LLMs can assist in multi-stage manipulation of potentially malicious payloads
→ Current file inspection methods fail to detect sophisticated steganographic techniques
→ Vision-Language Models lack robust security measures for analyzing embedded content
→ Cross-platform vulnerabilities exist when processing files through multiple LLM services
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📊 Results:
→ Successfully tested on GPT-4o, Microsoft Copilot, Google Gemini 1.5 Pro, and Claude 3.5
→ Only 1/55 virus detectors flagged the embedded EICAR in image format
→ 4/55 detectors identified malware when extracted as standalone file
→ Base64 encoding achieved 100% bypass rate across tested platforms
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