SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
SAFE-Mem introduces fine-grained access controls and governance-aware retrieval for LLM agent memory, mitigating a poisoning risk where untrusted external claims persist and corrupt future autonomous reasoning.
Summary written by editorial AI · Source link below
arXiv:2610.22959v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected from external sources can introduce security risks when untrusted claims (i.e., atomic statements) are later treated as authorized preferences, policies, or permissions. Existing defenses typically rely on content-based filtering or coarse-grained provenance tracking, which either fai
Editorial Analysis
Persistent memory in LLM agents creates a hidden trust boundary: untrusted data ingested once can silently influence all future agent decisions without re-validation.
Audit the memory-persistence mechanisms of deployed LLM agents for untrusted-data contamination paths and implement provenance tagging.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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