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.
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. AVDH can also be used alongside CodeMender’s ongoing scanning to create a two-layered defense strategy.
Real-World Results
In the 10 months that we’ve been using AVDH, we’ve seen it have a significant impact. During a recent incident response investigation involving stolen corporate repositories, the harness discovered over 100 true-positive critical vulnerabilities in just two days — achieving results in a fraction of the time required for manual review.
This has greatly accelerated how Mandiant discovers vulnerabilities at scale. We have used it to analyze environments spanning tens of millions of lines of code, and execute thousands of pipelines to generate tens of thousands of findings. This rapid analysis has uncovered dozens of assignable flaws in widely used web extensions and open-source projects, resulting in 12 assigned CVEs, including CVE-2026-13242, CVE-2026-55803, and an additional dozen currently in active disclosure.
While fast, broad, high-precision scanning has been one of the key benefits of AVDH, it has also acted as a force multiplier during our targeted adversary simulation engagements. We recently processed a client’s web application source code through the harness, and quickly found a remote code execution (RCE) vulnerability that enabled initial access.
AVDH has repeatedly proven invaluable for navigating mature defenses and accelerating complex exploit chains.
Architecting the Pipeline
Harnesses have become a vital tool for cybersecurity uses of large language models (LLMs). They help mitigate much of the model’s unpredictability, driven by inherent, non-deterministic behavior, and dramatically improve their effectiveness at code analysis.
The programmatic infrastructure of a harness orchestrates agents in a strictly deterministic manner toward objective completion. For AVDH, we used the Google Agent Development Kit (ADK), an LLM framework that implements the most common agent orchestration patterns, and provides flexibility for configuring custom and third-party integrations. This approach aligns with the agentic orchestration capabilities now available in Google Antigravity, which provides a centralized workspace for builders to steer and manage these agentic workflows.
Our decades of frontline experience discovering and remediating vulnerabilities across every software domain helped us structure AVDH around the proven methodologies our consultants execute daily. AVDH chains specialized agents together in a sequential pipeline, much like the waterfall approach to software development: each phase is completed before the next begins. This pipeline yields a prioritized, risk-rated list of findings, primed for a human expert to review.
Just as frontline security experts rely on organizational context, an agentic harness requires rich environmental inputs — such as asset inventories, software bills of materials (SBOMs), architecture documentation, and threat intelligence. When fed into a distilled human knowledge base, this contextual data allows agents to dynamically select relevant skills, language rules, and vulnerability patterns for deep analysis.
