AI agents are evolving from passive LLMs to autonomous systems that can actively secure CI/CD pipelines through threat modeling and self-healing capabilities. However, they introduce new security risks including indirect prompt injection, excessive privileges, and non-deterministic failures. Organizations can harden their pipelines through sandboxed runners, policy-as-code enforcement, human-in-the-loop workflows for high-impact actions, zero trust principles, and layered security defenses across user, LLM, agent, and orchestration layers. A risk-based threat modeling approach helps determine when autonomous action is safe versus requiring human intervention.
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