Utility Under Attack: Agent Memory Poisoning and the Limits of Content Screening and Provenance Ranking
Not on the map. Why?
Abstract (arXiv)
Persistent memory makes false information durable: once a false statement is stored, it can be retrieved into future sessions that match it. We measure the cost of this failure mode using plainly worded false assertions generated in a single pass, with no instruction, trigger, or retriever optimization. Poisoning 1.2% of a LongMemEval corpus reduces accuracy from 0.850 to 0.300. A four-stage write-time screening pipeline that reaches 0.832 recall on indirect prompt injection while flagging 1.5% of trigger-word-laden benign text rejects 0 of 360 poisoned memories. We argue this exposes a boundary of content-only screening: distinguishing a false assertion from a true one generally requires external grounding beyond the text itself. We then evaluate provenance-weighted retrieval. The shipped weight is statistically indistinguishable from no defense (p=0.80), while a stronger weight recovers utility only by excluding untrusted content. In a mixed-provenance corpus where untrusted content is mostly benign, accuracy rises from 0.3167 to 0.7000; when the answer-bearing evidence itself arrives untrusted, evidence recall falls to zero and accuracy to 0.0417. Under the measured similarity regime, the additive provenance term has no usable setting: a weight strong enough to resist query-shaped poison is also strong enough to suppress legitimate untrusted evidence. We therefore argue for bounded occupancy constraints at retrieval rather than additive provenance penalties, and release the harnesses, corpora, and aggregate run reports.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 21 Aug 2026.
Builds on
14 of 19 references.
References · 14
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 538
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
- 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
- 2025Defeating Prompt Injections by Designcited by 297
- 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agentscited by 145
- 2025Design Patterns for Securing LLM Agents against Prompt Injectionscited by 73
- 2026Memory Poisoning Attack and Defense on Memory Based LLM-Agentscited by 44
- 2024InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Modelscited by 42
- 2026From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agentscited by 33
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation
- 2026SMSR: Certified Defence Against Runtime Memory Poisoning in Persistent LLM Agent Systems
- 2026PACMI: Provenance-Aware Cascading Memory Invalidation for Long-Term LLM Agents
- 2026In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning
- 2026When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense
- 2026PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents
- 2026Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts
- 2026Eywa: Provenance-Grounded Long-Term Memory for AI Agents