arXiv Machine Learning June 25, 2026 By Jun Wen Leong

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

Read the original on arXiv Machine Learning →

arXiv:2605. 08442v3 Announce Type: replace-cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models.

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Related stories

Injection-Execution Dissociation: A Mechanistic Evaluation of Persistent Memory Attacks and Defenses in Stateful LLM Agents

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

The Price of Safety: Benign-Case Utility and Token Overhead of Memory-Poisoning Defenses in LLM Agents

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

SMSR: Certified Defence Against Runtime Memory Poisoning in Persistent LLM Agent Systems

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering