Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agentic Systems
Researchers have proposed a solution to prevent personal language agents from leaking unauthorized information between different audiences. They suggest applying an audience-membership rule at the point where memory is used to assemble context for a new query. This rule checks if every current viewer belonged to the original audience, ensuring that sensitive information doesn't reach a broader audience. The approach is model-neutral and invariant on the assembled context. A s
Researchers have proposed a solution to prevent personal language agents from leaking unauthorized information between different audiences. They suggest applying an audience-membership rule at the point where memory is used to assemble context for a new query. This rule checks if every current viewer belonged to the original audience, ensuring that sensitive information doesn't reach a broader audience. The approach is model-neutral and invariant on the assembled context. A synthetic test suite showed promising results, but the evidence is preliminary.
---
Why it matters: This matters because it addresses a critical issue in agentic systems: preventing cross-audience memory leakage. Engineers working on personal language agents will be interested in this solution as it provides a way to ensure that sensitive information remains confidential.
Source: https://arxiv.org/abs/2608.17148
This article was originally published at: https://arxiv.org/abs/2608.17148