Research map / Adversarial robustness and security / Attacks on text and graphs
MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents
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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
- Undefended agents show 62.55% overall BCR on average while keeping 94.40% clean accuracy across ten models.
- The MIXed defense reduces overall BCR to 10.70% with 93.77% CleanAcc, but L3 BCR remains 14.16% and L2 11.64%.
- Write-time consistency check cuts L1 BCR to 4.77% yet leaves L2 at 22.54% and L3 at 27.80%.
Surprising findings
- Perplexity-based filtering (PPL) drops clean accuracy to 61.28% while barely reducing BCR (59.14%), so it is ineffective and harmful.
- Fact-store substrate is more robust than flat chunk because decomposition dilutes injected content.
Limitations
- Focuses only on three memory substrates and text-based poisoning via standard channels.
- L2 case count is small (253) due to construction difficulty; many L2 candidates were rejected.
- Parametric poisoning, side-channel attacks and weight tampering are excluded from scope.
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
Closest papers by meaning
- A-MemGuard: A Proactive Defense Framework For LLM-Based Agent Memory (ICML 2026 · similarity 0.92)
- Bits Leaked per Query: Information-Theoretic Bounds for Adversarial Attacks on LLMs (NeurIPS 2025 · similarity 0.87)
- Memory Injection Attacks on LLM Agents via Query-Only Interaction (NeurIPS 2025 · similarity 0.87)
- SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety (ICML 2026 · similarity 0.87)
- Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security (NeurIPS 2026 · similarity 0.86)
- Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents (NeurIPS 2026 · similarity 0.86)
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