How to Secure AI Agents Against Model Theft and API Key Leaks

AI agents pose expanded security risks because they interact with tools, databases, external APIs, and code execution environments, creating multiple pathways for credential leakage and model exfiltration. Attackers can exploit these systems by injecting malicious instructions to extract system prompts, access tokens, or confidential data, while API keys embedded in prompts or logs remain especially vulnerable. Security best practices recommend storing credentials in dedicated secrets managers, using short-lived tokens with least-privilege permissions, and isolating prompt inputs and retrieved documents as untrusted data. Graph-based tools like the open-source TrustGraph project can help security teams map trust relationships across agents, tools, and datasets to detect transitive exposures and excessive permissions. Preventive measures must be combined with continuous runtime monitoring to flag unusual query volumes, suspicious tool sequences, or unexpected access behavior.