AI Tool Poisoning: How Hidden Instructions Threaten AI Agents

AI tool poisoning presents a significant threat to AI agents by exploiting how they interpret tool descriptions, potentially leading to data breaches and unauthorized actions. This attack method involves embedding hidden instructions or malicious metadata within the descriptions of tools that AI agents use, which can cause agents to execute malicious actions like leaking sensitive data or running unauthorized code. Different forms of tool poisoning, such as hidden instructions, misleading examples, and permissive schemas, can manipulate AI agents' behavior, compromising their reliability and trustworthiness. To counter these threats, organizations are encouraged to implement security measures, including runtime monitoring, validation of tool descriptions, input sanitization, and strict identity and access controls. Understanding these risks and adopting effective security controls are crucial for protecting AI agents from such vulnerabilities.