Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
TL;DR
PACE (Provenance-Aware Capability Enforcement) is a security framework designed for tool-using large language model agents, ensuring safe execution by verifying capabilities and effects before tool calls. It significantly reduces attack success rates while maintaining utility.
✦ Why It Matters
Implement PACE to enhance security in your LLM applications and mitigate risks from tool misuse.
Key Takeaways
- Adopt PACE to secure tool-using LLM agents against malicious inputs.
- Utilize capability verification to ensure authorized actions are executed.
- Implement path confinement to limit the influence of tool calls.
- Regularly evaluate security benchmarks to assess the effectiveness of defenses.
How It Works
PACE operates by mediating every tool call before execution, employing path confinement to restrict the influence of actions and capability verification to ensure that the effects align with authorized requests. This dual approach allows for a robust security mechanism that can adapt to potential threats while maintaining operational integrity.