mlsanityData to 14 Oct 2026

A Blind Trust, the Bloody Thrust: When Attacker-Controlled Hook Updates Steer AI Agent Harnesses towards Malicious Behaviors

arXiv 2609.03884 ↗First posted 3 Sep 2026cs.CR cs.AIcited by 0

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

Abstract (arXiv)

Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These commands run with host privileges yet ship as lifecycle-hook configuration and may fire at times the LLM never observes. We identify the lifecycle-hook update path, which harnesses trust blindly, as a new attack surface. Under a supply-chain threat model in which an attacker controls only plugin metadata and lifecycle-hook configuration, a benign versioned plugin can be trojanized by an update that silently binds attacker-chosen commands to benign events, yielding malicious host-side behavior such as privilege escalation. We propose HookPry, an open-source and fully automated attack framework that systematically exploits this vulnerability across heterogeneous AI agent harnesses. HookPry realizes ten attack objectives; across 25 combinations of harnesses and backends in 1,000 end-to-end runs, it compromises all seven evaluated harnesses, with per-harness success rates reaching 92.5%. Representative defenses remain insufficient: Microsoft Defender has 0% recall, and the union of three static defenses misses 47.5% of malicious artifacts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 55 references that are in the map. First posted on arXiv 3 Sep 2026.

Builds on

12 of 55 references.

Closest ancestors · 10

Same direction, newest first

  1. 2026Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens29 Jun
  2. 2026Dynamic Malicious Skills in Agentic AI15 Jun
  3. 2026Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems3 Apr
  4. 2026Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance20 Mar
  5. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats12 Mar
  6. 2026From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agent9 Feb
  7. 2026SMCP: Secure Model Context Protocol1 Feb
  8. 2025MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents14 Oct
  9. 2025"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors26 Sep
  10. 2025Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE19 Sep

Other references · 2

  1. 2026ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watcherscited by 20
  2. 2026Model Context Protocol Threat Modeling and Analyzing Vulnerabilities to Prompt Injection with Tool Poisoningcited by 10

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety
  2. 2026AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use
  3. 2026From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
  4. 2026Setup Complete, Now You Are Compromised: Weaponizing Setup Instructions Against AI Coding Agents
  5. 2026AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
  6. 2026AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents
  7. 2026HARDE: Optimizing Agent Harnesses for Runtime Risk Detection and Execution Control
  8. 2026ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection