aiqbench takeAI Safety

This experiment demonstrates how local, uncensored generative AI models can rapidly iterate on offensive tooling to bypass Endpoint Detection and Response (EDR) systems, effectively lowering the barrier for custom malware development. The study highlights a critical shift where accessible AI accelerates the creation of stealthy credential-dumping utilities that evade standard security controls without extensive human engineering. For defenders, this underscores the urgent need to adapt detection strategies against AI-generated polymorphic code targeting LSASS memory.

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Local AI Model Modifies Windows Credential Dumper to Bypass EDR Detection

Cybersecurity NewsSeptember 26, 20263 min read

A locally hosted, uncensored artificial intelligence model modified a Windows credential-dumping utility until it evaded two Endpoint Detection and Response (EDR) products in a controlled lab, highlighting how accessible generative AI could accelerate custom offensive-tool development. The experiment, published by Project Black researcher Eddie Zhang, targeted the Local Security Authority Subsystem Service (LSASS), whose memory […] The post Local AI Model Modifies Windows Credential Dumper to Bypass EDR Detection appeared first on Cyber Security News.

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