mlsanityData to 7 Oct 2026

Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution

arXiv 2603.23064 ↗First posted 24 Mar 2026cs.CR cs.AI cs.SIcited by 5

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Abstract (arXiv)

We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory and subsequently influence user-facing behavior without the user's awareness. This vulnerability arises from an architectural design shared across the Claw ecosystem: heartbeat background execution runs in the same session as user-facing conversation, so content ingested from any external source monitored in the background (including email, message channels, news feeds, code repositories, and social platforms) can enter the same memory context used for foreground interaction, often with limited user visibility and without clear source provenance. We formalize this process as an Exposure (E) rightarrow Memory (M) rightarrow Behavior (B) pathway: misinformation encountered during heartbeat execution enters the agent's short-term session context, potentially gets written into long-term memory, and later shapes downstream user-facing behavior. We instantiate this pathway in an agent-native social setting using MissClaw, a controlled research replica of Moltbook. We find that (1) social credibility cues, especially perceived consensus, are the dominant driver of short-term behavioral influence, with misleading rates up to 61%; (2) routine memory-saving behavior can promote short-term pollution into durable long-term memory at rates up to 91%, with cross-session behavioral influence reaching 76%; (3) under naturalistic browsing with content dilution and context pruning, pollution still crosses session boundaries. Overall, prompt injection is not required: ordinary social misinformation is sufficient to silently shape agent memory and behavior under heartbeat-driven background execution.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 24 Mar 2026.

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11 of 40 references.

References · 11

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  3. 2025Memory Injection Attacks on LLM Agents via Query-Only Interactioncited by 127
  4. 2025MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrievalcited by 65
  5. 2026Memory Poisoning Attack and Defense on Memory Based LLM-Agentscited by 40
  6. 2026From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agentcited by 27
  7. 2026"Humans welcome to observe": A First Look at the Agent Social Network Moltbookcited by 25
  8. 2026A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)cited by 15
  9. 2026Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agentscited by 14
  10. 2025Context manipulation attacks : Web agents are susceptible to corrupted memorycited by 10
  11. 2026The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societiescited by 9

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Citing papers per year: 2026: 5.

5 papers.