Staying Ahead of Adversarial AI Through Agentic Source Code Review

The useful part

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.

How it works

• By combining multi-agent orchestration with our frontline subject-matter expertise, this framework helps to augment the discovery and validation of routine vulnerabilities, enabling humans to focus their...
• To help defenders implement similar approaches for their own environments, we are sharing the details of this internal, point-in-time architecture for the first time.
• 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...
• AVDH can also be used alongside CodeMender's ongoing scanning to create a two-layered defense strategy.