AI agents aim to solve complex tasks by combining text-based reasoning with external tool calls.Unfortunately, AI agents are vulnerable to prompt injection attacks where data returned by external tools hijacks the agent to execute malicious tasks.To measure the adversarial robustness of AI agents, we introduce AgentGym, an evaluation framework for agents that execute tools over untrusted data.To capture the evolving nature of attacks and defenses, AgentGym is not a static test suite, but rather an extensible environment for designing and evaluating new agent tasks, defenses, and adaptive attacks.We populate the environment with 97 realistic tasks (e.g., managing an email client, navigating an e-banking website, or making travel bookings), 629 security test cases, and various attack and defense paradigms from the literature.We find that AgentGym poses a challenge for both attacks and defenses: state-of-the-art LLMs fail at many tasks (even in the absence of attacks), and existing prompt injection attacks break some security properties but not all. We hope that AgentGym can foster research on new design principles for AI agents that solve common tasks in a reliable and robust manner.