Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 19·5 min read
Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agentic Systems
TL;DR
A novel authorization mechanism prevents memory leakage in personal language agents by ensuring that facts learned from one audience are not shared with another. This approach maintains audience boundaries and protects sensitive information during context assembly.
✦ Why It Matters
Implement audience membership checks in your language agents to prevent unauthorized information sharing.
Key Takeaways
- Implement audience membership tagging for facts in your systems.
- Ensure context assembly checks current viewer permissions.
- Test your systems against synthetic integrity suites for vulnerabilities.
- Consider the implications of memory poisoning in agentic systems.
How It Works
The mechanism relies on tagging each fact with its original audience and checking this against the current viewers before allowing access. If any viewer does not belong to the original audience, the fact is excluded from the context assembly.
This anti-monotone rule ensures that sensitive information does not leak across different audience groups.