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MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents

NeurIPS 2026 · Adversarial robustness and security › Attacks on text and graphs · approach: LLM jailbreak and injection defense · 0 citations (OpenAlex)

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Introduces MemPoison, a 1,227-case benchmark and Mechanistic Influence Decomposition (MID) diagnostic for persistent memory poisoning in LLM agents, showing write-time defenses suppress single-record (L1) attacks but leave compositional (L2) and context-triggered (L3) attacks largely unsuppressed.

Problem

Persistent external memory in LLM agents creates durable attack surfaces; existing evaluations focus on single-record injection and top-line metrics, leaving unclear why write-time defenses fail under compositional and dormant poisoning.

Approach

A three-tier taxonomy (L1 direct, L2 compositional, L3 context-triggered dormant) with a hand-validated benchmark across memory substrates and channels, plus counterfactual influence decomposition (single-record influence, pairwise interaction, activation shift) to explain defense blind spots.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: ToolBench BFCL SWE-bench Terminal-Bench TheAgentCompany tau-bench AssistantBench OSWorld

Benchmarks: MemPoison-Bench

Metrics: Clean Accuracy Behavioral Corruption Rate Ambiguous Rate Unclear Rate P2C rate poisoned-match score reduction

Baselines: Source Reliability Retrieval Write-time consistency check Memory novelty anomaly filter MIXed PromptGuard LLMJudge Write EraseAndCheck SmoothLLM PPL Input Moderation Memory Sanitization

Builds on: PromptGuard SmoothLLM EraseAndCheck

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