Retrieval Observability Bounds on Provenance Detection for Agent Memory Poisoning: Measured Coverage and a Falsified Standalone Detector
cs.CR, cs.LG
Submitted: 2026-06-29
Updated: 2026-09-27
Comments: 71 pages, 3 figures. Companion to arXiv:2605.08442. Retitled; major revision. v3 reverses v2's practical claim: the trajectory signature is not a deployable detector (benign FPR 24.7-57.6%, PPV <=3.85% at 1% prevalence; CV AUC reflects a 22-vector codebook). Withdrawn from v2: the pre-filter recommendation, the frontier-transfer result, and all claims of meeting the preregistered 0.10 bound
License: http://creativecommons.org/licenses/by/4.0/
The gist: Whether a trajectory-based detector can observe the retrieval provenance of an agent memory-poisoning attack is governed by one architectural variable: retrieval observability, whether the agent's
Terminology
Abstract
Whether a trajectory-based detector can observe the retrieval provenance of an agent memory-poisoning attack is governed by one architectural variable: retrieval observability, whether the agent's memory access produces a logged tool call. We formalise this as a framework-agnostic retrieval-to-action provenance graph, classify published attack families by their position relative to it, measure the resulting coverage property against collected agent traces, and then falsify the detector's standalone deployment, reversing our own earlier recommendation to use the signature as a pre-filter ahead of recipient-metadata gating. Under exclusive tool-mediated payload access a retrieval-before-exfiltration event is structurally forced. Across eight non-overlapping zero-event control cells we observe 0 violations in 289 eligible successes over 87 scenario configurations. The invariant is empirically unbroken, but on the clustering unit no registered cell certifies the preregistered <=0.10 upper bound (per-cell bounds 0.10-0.35); a post-hoc pool of two control arms nominally clears it but is not credited. When the scaffold delivers the payload implicitly the signature disappears; we had read this as agents declining an available retrieval, but a follow-up shows it tracks store content instead, and that behavioural reading is withdrawn. The detector is then falsified for standalone use. Benign memory-grounded sends are trajectory-isomorphic: false-positive rate 24.7-57.6% on a 13-model factorial (N=4,160), positive predictive value <=3.85% at 1% attack prevalence, and a recipient-metadata gate flags 129/129 legitimate external emails. Retrieval-before-send is an attack precondition, not a maliciousness predicate; its cross-validated AUC also reflects membership of a 22-vector feature codebook, not generalisation. Companion to arXiv:2605.08442; we release the corpus and scoring code.
Sources
- Enforcing Benign Trajectories: A Behavioral Firewall for Structured-Workflow AI Agents
- From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents
- PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications
- MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents
- Injection-Execution Dissociation: A Mechanistic Evaluation of Persistent Memory Attacks and Defenses in Stateful LLM Agents
- VIGIL: Runtime Enforcement of Behavioral Specifications in AI Agent Skills
- TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
- MemLineage: Lineage-Guided Enforcement for LLM Agent Memory
- Hidden in Memory: Sleeper Memory Poisoning in LLM Agents
- What If Prompt Injection Never Left? Rethinking Agent Security through Cross-Session Stored Prompt Injection
- Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections
- MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning
- Representation Engineering: A Top-Down Approach to AI Transparency
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