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Hooking Coding Agents with the Cedar Policy Language describes how to secure autonomous AI coding agents using a reference monitor and the Cedar Policy Language. It focuses on mitigating vulnerabilities and ensuring deterministic control over agent actions.
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This article details a method for securing autonomous AI coding agents by implementing a reference monitor based on a trajectory event model. It highlights the increasing autonomy of coding agents and the associated security risks, proposing the Cedar Policy Language as a robust solution for adjudicating agent actions. The approach emphasizes building layered defenses at event boundaries, ensuring the monitor is always invoked, tamper-proof, and verifiable. The article covers mapping threat models like the lethal trifecta and OWASP Top 10 for Agentic Applications to the trajectory model, and how Cedar policies can formalize security intent, block destructive commands, and prevent data exfiltration. It also discusses the architecture of a hook-based harness and the future direction of agent security, including policy generation scalability and multi-turn, stateful policies.
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