Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
PIPES: Securing Agent Perception with Provenance and Priors
TL;DR
PIPES (Provenance-Informed, Prior-Enforced Screening) is a new framework designed to secure agent perception by screening data sources based on their trustworthiness. It significantly reduces attack success rates from 84.7% to 2.3% while maintaining high utility in benign scenarios.
✦ Why It Matters
Implement PIPES to enhance the security of your AI agents against data corruption attacks today.
Key Takeaways
- Integrate PIPES to secure agent data processing.
- Utilize semantic priors to evaluate data trustworthiness.
- Implement static field contracts for stable schemas.
- Monitor and respond to detected violations in real-time.
How It Works
PIPES screens response units by analyzing their semantic context and source provenance. It employs static field contracts for predictable data structures and assesses open-ended content based on trusted metadata.
Violations of expected semantics or provenance hierarchy trigger actions such as removal or alerts.