Local AI Model Modifies Credential Dumper to Bypass EDR Detection

Key Takeaways

• A researcher utilized a locally hosted, uncensored AI model to modify a Windows credential dumper.
• The AI-generated modifications successfully bypassed detection by two Endpoint Detection and Response (EDR) products in a controlled lab environment.
• This experiment underscores the potential for accessible generative AI to accelerate the development of custom offensive tools and evade security solutions.
• The findings highlight the evolving threat landscape and the need for multi-layered defenses beyond EDR.

AI Model Evades EDR by Modifying Credential Dumper

In a recent experiment, a locally hosted, uncensored artificial intelligence model successfully altered a standard Windows credential-dumping utility, enabling it to bypass detection by two distinct Endpoint Detection and Response (EDR) products within a controlled laboratory setting. This demonstration underscores a concerning trend: the potential for readily available generative AI to significantly expedite the creation of custom offensive tools.

The research, conducted by Project Black researcher Eddie Zhang, focused on the Local Security Authority Subsystem Service (LSASS). LSASS memory is a prime target for attackers, as it frequently stores authentication material that can be leveraged for lateral movement once administrative access to a system has been achieved.

The Challenge: AI-Driven Evasion

The project commenced with a demanding objective: to determine if an AI could autonomously generate an executable capable of dumping LSASS memory without triggering modern EDR systems, all while requiring minimal human intervention. This is a critical area of study, as MITRE ATT&CK identifies LSASS memory dumping as T1003.001, a specific Credential Access sub-technique. Attackers with administrative or SYSTEM-level privileges can extract credentials from LSASS memory for subsequent authentication to other systems.

Initially, Zhang attempted to utilize commercial AI models for this task. According to Zhang reported, attempts to generate the dumper using Claude Opus 5, Opus 4.8, and Sonnet 5 were met with immediate refusals, despite his organization’s participation in Anthropic’s Cyber Verification Program, which typically allows for such research.

From Censor to Stealth

Undeterred, the researcher then pivoted to the open-weight DeepSeek v4 Flash 0731 model. After several carefully crafted prompts, DeepSeek successfully produced a functional executable. This initial tool was designed to accept a process identifier, create a suspended clone of the target process via reflection, generate an in-memory minidump, XOR-encrypt it, and then write the encrypted output to disk.

Zhang validated the integrity of the resulting dump using pypykatz, confirming that the extracted data could be parsed. However, this initial executable still triggered EDR detection. When prompted to enhance its stealth capabilities, DeepSeek invoked its internal safety guardrails, refusing to proceed with modifications aimed at evasion.

This led Zhang to transition the code to an uncensored, community-modified Qwen 3.8 27B model. This model was run locally on a dedicated password-cracking rig, equipped with two Nvidia RTX 4090 graphics cards, ensuring full control and bypassing any external content moderation.

Local AI Bypasses EDR

With a simple request to make the executable “more stealthy,” the locally hosted Qwen model returned a revised build. This updated version generated no detections across either of the two EDR platforms present in the lab environment, Zhang reported.

A detailed code review revealed the modifications implemented by Qwen. These included alterations to the process-spawning behavior, a reduction in the access masks requested against the target process, the insertion of randomized delays during minidump construction, changes to the output filename and path, and the scrubbing of embedded strings from the binary itself.

These modifications are significant because many defensive mechanisms rely on correlating recognizable artifacts. These include suspicious process ancestry, requests for high-privilege handles to LSASS, known strings within binaries, the creation of dump files, and tightly timed operational behaviors. For instance, Elastic provides a tool-agnostic rule to monitor LSASS handle requests that utilize access masks commonly associated with dumping utilities. Similarly, MITRE outlines detection strategies based on sequences of abnormal process access followed by memory dumping or file creation.

While the findings are notable, their scope remains constrained. The specific EDR vendors involved were not identified, comprehensive configuration details were not published, and success against two laboratory products does not definitively establish a universal bypass capability. Nevertheless, this research clearly demonstrates how local, guardrail-free AI models can iteratively refine known offensive code without transmitting prompts or source code to external hosted services. This capability significantly reduces the expertise and time required to develop environment-specific variants of attack tools.

What You Should Do

• Implement Multi-Layered Defense: Recognize that EDR is one layer of defense, not an infallible guarantee. Combine EDR with other security controls.
• Enable LSASS Protection: Microsoft recommends activating the LSASS credential-stealing Attack Surface Reduction (ASR) rule with tamper protection enabled.
• Protect LSASS Process: Configure LSASS to run as a Protected Process Light (PPL) and deploy Credential Guard to further secure credentials.
• Restrict RDP Administration: Limit Remote Desktop Protocol (RDP) administration access to only necessary personnel and implement strong authentication.
• Disable WDigest Caching: Ensure that WDigest credential caching is disabled to prevent credentials from being stored in an easily recoverable format.
• Minimize Local Administrator Rights: Strictly enforce the principle of least privilege by minimizing local administrator rights across all endpoints.
• Separate Privileged Accounts: Maintain distinct, dedicated accounts for privileged tasks and avoid using them for routine activities.
• Use Unique Credentials: Implement unique, strong credentials for all accounts and enforce regular password rotations.
• Monitor LSASS Access: Actively monitor for unusual or suspicious access patterns to the LSASS process.
• Rapid Incident Response: Develop and practice rapid incident response procedures to quickly isolate hosts exhibiting credential-dumping behavior.

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