When Malicious Instructions Persist: Persistent Memory Poisoning Attack on Harness-Based Agents
Shuhuai Huang, Jingfeng Zhang, Hong Jia
cs.CR · cs.AI
Quick verdict
PMPA poisons agent persistent memory via external docs (ISR 62-78%, C-ASR 31-93%); timely, well-scoped attack study but small payload set, thin statistics.
Summary
The paper presents PMPA, a two-phase attack demonstrating that malicious instructions embedded in benign external sources (text, PDF, image) can be written into the persistent memory of harness-based LLM agents without attacker access to the agent or device. A payload combining a transition sentence, a memory-writing instruction, and a conditional malicious rule — placed mid-source in first-person style — is stored while the user performs an unrelated QA task; in a later session, a benign workspace task retrieves the poisoned memory and executes a privacy-leaking action. The authors evaluate OpenClaw and Claude Code with DeepSeek-V4-Flash, DeepSeek-V4-Pro, and Qwen3-Max in an AgentDojo-inspired simulated workspace of JSON-backed email, calendar, document, and form skills, using 30 QA instances from four datasets, four scenario payloads, and three modalities (360 samples). Average ISR/C-ASR reach 73.7%/55.5% on OpenClaw and 66.9%/81.7% on Claude Code; plain text is most effective (99.4% ISR on OpenClaw), and attacks show no consistent decay after five benign interactions. A sandwich-style prompt defense sharply reduces injection for image/PDF inputs but leaves cross-session attacks nearly unchanged. Main limitations: only four handcrafted conditional rules with one trigger prompt per scenario, 30 QA instances, no significance testing or confidence intervals, no no-attack utility control, no comparison with prior attack baselines, and no visible repository URL.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Malicious instructions embedded in benign external sources are written into the persistent memory of harness-based agents at ISR 61.7–77.5% across settings, without attacker access to the agent.
- Poisoned memory is retrieved in later sessions and causes privacy leakage at C-ASR 30.8–92.5%, remaining effective after up to five benign interactions.
- Plain text is the most effective injection modality (OpenClaw text ISR 99.4%), with image and PDF weaker but still viable.
- A sandwich-style prompt defense reduces memory injection sharply for image/PDF inputs but provides essentially no protection against cross-session attacks once memory is poisoned.
- The attack preserves benign task performance, with Utility above 80% in all tested settings.
Strengths
Realistic publish-only threat model
§3.1 restricts the attacker to crafting external sources — no agent queries, no device/memory/system-prompt access — a strictly weaker capability than AgentPoison (direct memory poisoning) or MINJA (direct agent interaction), making the demonstrated risk genuinely deployment-relevant.
Cross-session persistence is treated as a first-class measurement target
Beyond ISR, the paper defines C-ASR (§3.4) and probes persistence under benign load (Table 5: C-ASR@0 vs @1/@3/@5 shows no consistent decay, e.g. calendar on Claude Code stays 93.3% at five interactions), which most injection studies omit.
Broad factorial evaluation with utility tracking
Honest negative result on the defense
Safe, observable evaluation substrate
Weaknesses
Tiny payload and trigger diversity
No statistical inference anywhere
Ambiguous metric denominators
Missing controls and baselines
Reproducibility gaps
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Scope & Claims
The paper proposes PMPA (Persistent Memory Poisoning Attack), a two-phase attack against harness-based LLM agents (OpenClaw, Claude Code). The threat model (§3.1) is deliberately minimal: the attacker only publishes external sources (text, PDF, image) carrying embedded instructions and can neither query the victim agent nor access the device, memory, or configuration. Central claims: (1) payloads — a transition sentence, a memory-writing instruction, and a conditional malicious rule — get stored during unrelated benign QA tasks at ISR 61.7–77.5% (Table 1); (2) poisoned memory is retrieved in later sessions and causes privacy leakage at C-ASR 30.8–92.5%, surviving five benign interactions without consistent decay (Table 5); (3) benign Utility stays above 80% in all settings; (4) a sandwich-style prompt defense curbs injection but not cross-session exploitation (Table 6). Reported averages: ISR/C-ASR of 73.7%/55.5% on OpenClaw and 66.9%/81.7% on Claude Code.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is the PMPA attack?
PMPA (Persistent Memory Poisoning Attack) embeds malicious instructions into benign external files such as text documents, PDFs, or images. When a user gives such a file to an LLM agent like Claude Code or OpenClaw for an unrelated question-answering task, the agent may store an embedded 'memory rule' in its persistent memory. In later sessions, a benign workspace task can retrieve that rule and trigger extra actions that leak private information, such as adding an attacker's account to calendar events.
How successful is the attack?
Across two agent harnesses (OpenClaw, Claude Code) and three backbone models (DeepSeek-V4-Flash, DeepSeek-V4-Pro, Qwen3-Max), average injection success was 73.7% on OpenClaw and 66.9% on Claude Code; average cross-session attack success was 55.5% and 81.7% respectively. Plain-text files were the easiest vector (99.4% injection on OpenClaw); images and PDFs were harder but still worked.
Can this attack be defended against?
The tested sandwich-style safety prompt sharply reduced memory injection for image and PDF inputs (e.g. injection rate 65.6%→2.2% on Claude Code with image input), and reduced it less for plain text. However, once memory was already poisoned, cross-session attacks stayed essentially unchanged — so prompt-level defenses alone do not remediate an already-compromised agent.
Was the research conducted safely?
Yes. Per the paper's ethics statement, experiments ran only in a controlled local simulated workspace (JSON files with email, calendar, document, and form skills) on the authors' own machines; no real individuals or services were targeted.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figure 1 (attack overview) appears in the extraction only as a caption; the diagram's content could not be inspected.
- The text states the code is on GitHub, but no repository URL appears anywhere in the provided text (possibly a hyperlink lost in extraction), so repo contents could not be verified.
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