Your Agent Guardrails Have a Blind Spot: Tool-Output Injection and How to Fix It
LLM agents possess a significant security vulnerability where malicious code can be injected through the outputs of tools they utilize. This 'tool-output injection' bypasses standard input and output guardrails because the malicious data enters the model's context window directly from the tool's response. To mitigate this, security measures must be implemented at the 'PostToolUse' stage, intercepting and sanitizing tool outputs before they are processed by the agent. AI
IMPACT Highlights a critical security gap in LLM agent development, necessitating new defense mechanisms to prevent malicious code execution.