SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
Factual evidence
What the source reports
Researchers introduced SAFE-Mem, a framework to secure agentic LLM memory using fine-grained authority and governance-aware retrieval.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 19 October 2026
- Collected by OneBench
- 20 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2610.22959v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Persistent memory in agentic systems risks unauthorized policy or permission escalation from untrusted external sources.
Do what
Review agentic memory architecture designs with the AI security team to ensure untrusted context cannot alter access policies.