Community-Driven Security for AI Agents: Evolution of an Adversarial Testing Framework

The proliferation of autonomous AI agents has exposed critical security gaps, from tool poisoning to supply chain attacks, as exemplified by CVE-2026-25253. This paper traces the evolution of the Agent Security Harness, an open-source adversarial testing framework, from its initial 209 tests to a community-enhanced suite of 342 tests, culminating in a perfect 10/10 evaluation score. We detail the challenges of integrating community plugins, which initially dropped the score to 6.5/10, and the subsequent recovery through manifest-based integrity checks, trust tiers, and hardening protocols. Building on our prior work in Decision Load Index (DLI) and Constitutional Self-Governance (CSG), we propose a sustainable model for open contributions, including bounties and good-first issues. The framework's journey demonstrates how collaborative red-teaming can mitigate agent risks, aligning with AIUC-1 standards and offering a blueprint for enterprise-grade security. We outline the v4.0 roadmap and invite further participation to foster a robust, collective defense against emerging threats.