Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·27d ago·5 min read
ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents
TL;DR
ActGuard is a pre-execution action auditing framework designed to protect large language model (LLM) agents from indirect prompt injection attacks. It effectively reduces attack success rates while maintaining task utility, achieving a favorable balance between security and usability.
✦ Why It Matters
Implement ActGuard to enhance the security of LLM agents against indirect prompt injection attacks today.
Key Takeaways
- Adopt ActGuard for enhanced security in LLM applications.
- Utilize pre-execution auditing to identify malicious content effectively.
- Balance security and usability in tool-using agent designs.
- Evaluate your current defenses against IPI attacks for potential improvements.
How It Works
ActGuard operates by predicting the tools that will be used in an upcoming action and creating a local tool prior. It then compares the candidate action against this prior to identify any deviations in tool selection and parameters.
This allows for a detailed contrastive analysis, which helps in pinpointing malicious content without overly restricting the agent's execution capabilities.