Staying Ahead of Adversarial AI Through Agentic Source Code Review
Brief
Written by: Alex Tselevich, Michael Maturi
Introduction
Adversarial misuse of AI has increased the risk of data theft and extortion events, because when proprietary source code is exposed, defenders must scramble to identify and patch vulnerabilities while attackers deploy machine-speed AI tools against them.
By structuring the analysis process, enforcing skeptical validation steps, and injecting domain-specific human expertise directly into the pipeline, we’ve achieved a leap in efficacy. Combining AI models with a deeply structured, human expert-driven orchestration layer to tip the scales so that defenders can beat adversaries to the punch.
Today, we use the Agentic Vulnerability Discovery Harness (AVDH) to rapidly analyze code and find exploit paths during proactive reviews, penetration tests, red team operations, and incident response engagements.