mlsanityData to 14 Oct 2026

Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation

arXiv 2605.06393 ↗First posted 7 May 2026cs.CRcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Self-hosted computer-use agents (SHCUAs), such as OpenClaw, combine natural-language interaction with direct access to host-side resources, including browsers, files, scripts, system commands, and external communication channels. While useful for automating real tasks, this capability also creates a host-level abuse surface: a legitimately deployed agent may be steered toward unsafe operations through malicious messages, indirect prompt injection, unsafe skills, or tampering along the host-side control path. We argue that such risks cannot be addressed by ad hoc blocking rules alone, because the security criticality of an operation depends jointly on its action type, target object, execution context, and potential effect. This paper presents an operation-centric model for risk-based confinement of SHCUA operations. The proposed design keeps ordinary functionality on the constrained REE path, while protecting security-critical classification, authorization, binding, evidence generation, and selected execution-control decisions inside a cloud-native TEE-backed trusted operation plane. We instantiate the architecture on OpenClaw using Intel TDX as the primary trusted backend, with remote terminal-side trusted components verifying TDX-audited commands before constrained local execution. The evaluation shows that the design can block unsafe or policy-disallowed operations before execution, preserve ordinary functionality for allowed workloads, and provide auditable evidence with deployment-dependent overhead.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 36 of its 53 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

36 of 53 references.

Closest ancestors · 31

Same direction, newest first

  1. 2026ClawLess: A Security Model of AI Agents7 Apr
  2. 2026From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI6 Apr
  3. 2026Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw6 Apr
  4. 2026AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents3 Apr
  5. 2026Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents3 Apr
  6. 2026ClawSafety: "Safe" LLMs, Unsafe Agents1 Apr
  7. 2026"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agents30 Mar
  8. 2026Evaluating Privilege Usage of Agents with Real-World Tools30 Mar
  9. 2026SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants28 Mar
  10. 2026Runtime Governance for AI Agents: Policies on Paths17 Mar
  11. 2026Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw13 Mar
  12. 2026Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents25 Feb
19 more
  1. 2026ICON: Indirect Prompt Injection Defense for Agents based on Inference-Time Correction24 Feb
  2. 2026Formal Policy Enforcement for Real-World Agentic Systems18 Feb
  3. 2026The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis11 Feb
  4. 2026Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis of Vulnerabilities in Skills, Tools, and Protocol Ecosystems24 Jan
  5. 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents14 Jan
  6. 2026Memory Poisoning Attack and Defense on Memory Based LLM-Agents9 Jan
  7. 2025Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent8 Oct
  8. 2025MI9: An Integrated Runtime Governance Framework for Agentic AI5 Aug
  9. 2025ProbGuard: Proactive Runtime Monitoring for LLM Agent Safety via Probabilistic Prediction1 Aug
  10. 2025OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents17 Jun
  11. 2025VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents3 Jun
  12. 2025Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Execution1 Jun
  13. 2025AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents9 May
  14. 2025WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks22 Apr
  15. 2025Progent: Securing AI Agents with Privilege Control16 Apr
  16. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  17. 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
  18. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  19. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 5

  1. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
  2. 2026Don't Let the Claw Grip Your Hand: A Security Analysis and Defense Framework for OpenClawcited by 29
  3. 2026Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agentscited by 13
  4. 2026A Systematic Security Evaluation of OpenClaw and Its Variantscited by 13
  5. 2026Security Considerations for Artificial Intelligence Agentscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Security Analysis of the OpenClaw AI Agent Framework
  2. 2026OpenClaw PRISM: A Zero-Fork, Defense-in-Depth Runtime Security Layer for Tool-Augmented LLM Agents
  3. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
  4. 2026SIR: Self-improving Red-teaming for Compute Use Agents
  5. 2026Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures
  6. 2026Security Engineering of OpenClaw: Analyzing Attack Surface Expansion and Trust-Boundary Violations
  7. 2026Agent Privilege Separation in OpenClaw: A Structural Defense Against Prompt Injection
  8. 2026Local LLM Agents as Vulnerable Runtimes:A Source-Code Audit of the Agent Runtime Layer