AGentShield – Open benchmark of 6 AI agent security tools (537 test cases) (github.com)

AgentShield has launched the first open benchmark aimed at evaluating the security effectiveness of commercial AI agent protection tools against real-world attacks. This benchmark suite assesses six provider tools using a comprehensive suite of 537 test cases categorized into areas like prompt injection, data exfiltration, and multi-agent security. Utilizing the Commit-Reveal Integrity Protocol, AgentShield ensures that proprietary models can participate without disclosing their implementations, while maintaining the integrity of the results for independent verification. The significance of AgentShield lies in its potential to enhance the security of AI systems, as it provides a structured and transparent way for developers to understand how different security tools perform under various attack scenarios and identifies any trade-offs regarding latency and false positives. With its open-source framework, the community is encouraged to contribute new test cases and provider adapters, fostering collaboration and improvement in the AI/ML security arena. By holding security providers accountable and providing a robust testing platform, AgentShield sets a new standard for evaluating the resilience of AI systems against emerging threats.