ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection
Critical security research for LLM agent deployment; addresses real vulnerability in production AI systems using tools.
AI Summary
ClawGuard is a runtime security framework designed to protect tool-augmented LLM agents from indirect injection attacks. The paper identifies vulnerabilities where adversaries embed malicious instructions in tool-returned content that agents treat as trusted observations, exploiting three primary attack channels including web content, local content, and MCP server injection.
Excerpt
Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect prompt injection. Adversaries exploit this weakness by embedding malicious instructions within tool-returned content, which agents directly incorporate into their conversation history as trusted observations. This vulnerability manifests across three primary attack channels: web and local content injection, MCP server inj