DeepTrap automates discovery of contextual vulnerabilities in OpenClaw agents via trajectory optimization, showing that unsafe behavior can be induced while preserving task completion and that final-response checks are insufficient.
Clawdrain: Exploiting tool-calling chains for stealthy token exhaustion in open- claw agents.arXiv preprint arXiv:2603.00902
5 Pith papers cite this work. Polarity classification is still indexing.
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Routine user chats can unintentionally poison the long-term state of personalized LLM agents, causing authorization drift, tool escalation, and unchecked autonomy, as measured by a new benchmark and reduced by the StateGuard defense.
Claw AI agents' heartbeat background execution shares memory context with user sessions, allowing ordinary social misinformation to silently pollute long-term memory and shape behavior at rates up to 76% across sessions.
The survey organizes security threats and defenses in autonomous LLM agents into four layers and identifies that risks can propagate across layers from inputs to ecosystem impacts.
A systematic review of resource consumption threats in LLMs that organizes the problem along the full pipeline from threat induction to mitigation.
citing papers explorer
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Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw
DeepTrap automates discovery of contextual vulnerabilities in OpenClaw agents via trajectory optimization, showing that unsafe behavior can be induced while preserving task completion and that final-response checks are insufficient.
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When Routine Chats Turn Toxic: Unintended Long-Term State Poisoning in Personalized Agents
Routine user chats can unintentionally poison the long-term state of personalized LLM agents, causing authorization drift, tool escalation, and unchecked autonomy, as measured by a new benchmark and reduced by the StateGuard defense.
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Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution
Claw AI agents' heartbeat background execution shares memory context with user sessions, allowing ordinary social misinformation to silently pollute long-term memory and shape behavior at rates up to 76% across sessions.
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Security Attack and Defense Strategies for Autonomous Agent Frameworks: A Layered Review with OpenClaw as a Case Study
The survey organizes security threats and defenses in autonomous LLM agents into four layers and identifies that risks can propagate across layers from inputs to ecosystem impacts.
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Resource Consumption Threats in Large Language Models
A systematic review of resource consumption threats in LLMs that organizes the problem along the full pipeline from threat induction to mitigation.