agent-egress-bench: An Open-Source Testing Framework for AI Agent Data Leakage Risk Assessment

agent-egress-bench is an open-source tool focused on security egress (data leakage) testing for AI Agents. It provides real attack scenario simulation, automated testing workflows, and detailed report generation to help developers and security teams assess the data security protection capabilities of AI systems. Its core goal is to identify and fix data leakage vulnerabilities in AI systems, filling the gap in the field of AI Agent security assessment.

With the widespread application of Large Language Models (LLMs) and AI Agents, enterprises rely on them to process sensitive data, call external APIs, and perform automated tasks. However, their autonomy brings data leakage risks—attackers can induce the leakage of confidential information through prompt injection, or use internal tool permissions for unauthorized data transmission. Traditional security testing tools target web applications or network layer attacks and are difficult to cover the unique attack surfaces of AI Agents. Thus, agent-egress-bench was developed to fill this gap.

Maintained by bilkulsahi1235, agent-egress-bench is an open-source AI Agent security testing tool whose core goal is to identify and fix data leakage vulnerabilities. It focuses on AI Agent-specific risks:

• Confidential information leakage (API keys, database credentials, etc.)
• Prompt injection attacks (ability to resist malicious instructions)
• SSRF (unauthorized access to internal resources when calling external tools)
• Data leakage attempts (simulating the transmission of sensitive data to external servers)

agent-egress-bench is designed to be out-of-the-box, with key functional modules:

1. Pre-built attack scenario library: Built-in multiple real attack cases, no need to design attack vectors yourself;
2. Automated test execution: Select Agent and scenarios via a graphical interface, automatically execute and record interactions;
3. Detailed report generation: Structured reports mark risk points, attack paths, and repair suggestions, using non-technical language for easy understanding;
4. Extensible framework: Supports adding custom test cases to adapt to different industry needs.

agent-egress-bench is suitable for multiple scenarios:

• Pre-launch security review of enterprise AI systems: Discover vulnerabilities in advance to avoid post-launch incidents;
• Security due diligence of third-party AI services: Evaluate the protection capabilities of vendor products;
• Continuous security monitoring: Integrate into CI/CD workflows, automatically test when changes occur;
• Capacity building for security teams: Low-risk learning environment to understand attack surfaces and protection strategies.

agent-egress-bench adopts a modular architecture, with components including a test engine (executes scenarios, manages interactions), a scenario library (stores pre-built/custom test cases), a report generator (converts results into readable reports), and configuration management (customizes test behaviors). Installation is simple, supporting Windows 10+, requiring 4GB of disk space, 8GB or more of memory, and a 2GHz or higher processor.

agent-egress-bench has limitations:

1. Only targets egress-related risks, not covering model bias, adversarial sample attacks, etc.;
2. Highly customized AI Agents require additional custom test cases;
3. Need to pay attention to project updates to get the latest test scenarios and suggestions. It is recommended to use it as part of a comprehensive security testing strategy, not as the only dependency.

agent-egress-bench is an important step in the evolution of AI security tools, providing a practical tool for current security testing and laying the foundation for future AI security standards. It is recommended that teams deploying or planning to deploy AI Agents incorporate it into their security testing workflows to proactively identify and fix data leakage risks, ensuring the security and reliability of AI systems.