TECH NEWS

Staying Ahead of Adversarial AI Through Agentic Source Code Review

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. 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 impact.

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AirPods with cameras could not arrive at a worse time &mdash; and I'll just say no

Source: Nvidia has discussed an investment in Mercor as part of a round valuing the startup at $20B; Nvidia paid Mercor tens of millions of dollars last quarter

Researchers say OpenAI revoked their access to limited cyber program

It Begins: Google Starts Pushing Out Strict New Sideload Rules

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