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ai security
Staying Ahead of Adversarial AI Through Agentic Source Code Review
Mandiant describes the Agentic Vulnerability Discovery Harness (AVDH), an LLM-based system that chains specialized AI agents to identify vulnerabilities in source code through threat modeling, entry point discovery, data flow analysis, and human validation. In ten months, AVDH discovered over 100 critical vulnerabilities during incident response and has contributed to 12 assigned CVEs across web extensions and open-source projects. The framework combines multi-agent orchestration with human expertise to accelerate vulnerability discovery at scale while maintaining low false positive rates through human-in-the-loop validation and distilled knowledge injection from security consultants.
Why it matters: Security teams responding to stolen code repositories or conducting proactive assessments need faster vulnerability discovery tools; this approach demonstrates how LLM-based harnesses can augment human experts to match or exceed the speed of AI-driven adversarial attacks. Practitioners building AI-enabled security tools can reference the architecture and benchmarking methodology for their own implementations.
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ai security
Staying Ahead of Adversarial AI Through Agentic Source Code Review
No summary had been written when this copy was frozen.
- First seen by Cybersecurity Tracker
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Correction
Correction recorded as of .
ai security
Staying Ahead of Adversarial AI Through Agentic Source Code Review
Over a ten-month period Mandiant's Agentic Vulnerability Discovery Harness (AVDH) uncovered more than 100 true-positive critical vulnerabilities in stolen corporate repositories and produced twelve assigned CVEs, including CVE-2026-13242 and CVE-2026-55803. The harness also accelerated analysis of tens of millions of lines of code, enabling thousands of pipelines that yielded tens of thousands of findings and facilitated detection of remote-code-execution flaws during adversary-simulation engagements.
Why it matters: Security teams overseeing large codebases can reduce the chance of undetected critical flaws by adopting or evaluating agentic discovery harnesses like AVDH.
- Source published
- First seen by Cybersecurity Tracker
Source attribution
Correction
Correction recorded as of .
ai security
Staying Ahead of Adversarial AI Through Agentic Source Code Review
Over a ten-month period Mandiant's Agentic Vulnerability Discovery Harness (AVDH) uncovered more than 100 true-positive critical vulnerabilities in stolen corporate repositories and produced twelve assigned CVEs, including CVE-2026-13242 and CVE-2026-55803. The harness also accelerated analysis of tens of millions of lines of code, enabling thousands of pipelines that yielded tens of thousands of findings and facilitated detection of remote-code-execution flaws during adversary-simulation engagements.
Why it matters: Security teams overseeing large codebases can reduce the chance of undetected critical flaws by adopting or evaluating agentic discovery harnesses like AVDH.
- Source published
- First seen by Cybersecurity Tracker