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ARXIV:2605.03482 · AGENTS · SUBMITTED 06 MAY · 20:27 UTC · FRESHNESS STALE
ARXIV:2605.03482AGENTSSUBMITTED 06 MAY · 20:27 UTCFRESHNESS STALEIshrith Gowda · arXiv
A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection.
Opportunity summary
Pain A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three…
Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized.
Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
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A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection.
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10.48550/arXiv.2605.03482A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection.
Abstract
Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three attack classes with escalating access assumptions. Correcting an evaluation protocol inconsistency in the triggered-query specification of Chen et al. (2024), we show faithful evaluation increases measured attack success by $4\times$ (ASR-R: $0.25 \to 1.00$). Our primary contribution is MEMSAD (Semantic Anomaly Detection), a calibration-based defense grounded in a gradient coupling theorem: under encoder regularity, the anomaly score gradient and the retrieval objective gradient are provably identical, so any continuous perturbation that reduces detection risk necessarily degrades retrieval rank. This coupling yields a certified detection radius guaranteeing correct classification regardless of adversary strategy. We prove minimax optimality via Le Cam's method, showing any threshold detector requires $Ω(1/ρ^2)$ calibration samples and MEMSAD achieves this up to $\log(1/δ)$ factors. We further derive online regret bounds for rolling calibration at rate $O(σ^{2/3}Δ^{1/3})$, and formally characterize a discrete synonym-invariance loophole that marks the boundary of what continuous-space defenses can guarantee. Experiments on a $3 \times 5$ attack-defense matrix with bootstrap confidence intervals, Bonferroni-corrected hypothesis tests, and Clopper-Pearson validation ($n=1{,}000$) confirm: composite defenses achieve TPR $= 1.00$, FPR $= 0.00$ across all attacks, while synonym substitution evades detection at $Δ$ ASR-R $\approx 0$, exposing a gap existing embedding-based defenses cannot close.
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PROBLEM
A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three att...
METHOD
Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized.
WHY NOW
Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three attack classes with escalating access assumptions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We formalize memory poisoning attacks on retrieval-augmented agents as a Stackelberg game with a unified evaluation framework spanning three attack classes with escalating access assumptions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A theoretical framework for detecting memory poisoning attacks in retrieval-augmented agents using gradient-coupled anomaly detection.
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3.0/10 public viability
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