mlsanityData to 14 Oct 2026

LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild

arXiv 2410.13919 ↗First posted 17 Oct 2024cs.CR cs.AIcited by 12

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

Abstract (arXiv)

Attacks powered by Large Language Model (LLM) agents represent a growing threat to modern cybersecurity. To address this concern, we present LLM Honeypot, a system designed to monitor autonomous AI hacking agents. By augmenting a standard SSH honeypot with prompt injection and time-based analysis techniques, our framework aims to distinguish LLM agents among all attackers. Over a trial deployment of about three months in a public environment, we collected 8,130,731 hacking attempts and 8 potential AI agents. Our work demonstrates the emergence of AI-driven threats and their current level of usage, serving as an early warning of malicious LLM agents in the wild.

Key facts. Cited by 12 papers 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 2 of its 8 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

2 of 8 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents19 Jun

Other references · 1

  1. 2023LLM in the Shell: Generative Honeypotscited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 10.

12 papers.

  1. 2025Demonstrating specification gaming in reasoning modelscited by 56
  2. 2026AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agentscited by 2
  3. 2025Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Modelscited by 1
  4. 2026Realistic honeypot evaluations for scheming propensitycited by 1
  5. 2026AdvancedShelLM: A Stateful Multi-Agent LLM Honeypot for SSH Deceptioncited by 1
  6. 2026Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Responsecited by 1
  7. 2026Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detectioncited by 0
  8. 2026Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agentscited by 0
  9. 2026Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackerscited by 0
  10. 2026Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progressioncited by 0
  11. 2026HoneyRoute: Honeypot-Model Routing for Adversarial LLM Servingcited by 0
  12. 2026Cheap to Hypothesize, Costly to Verify: The Defense Surface of Agentic Vulnerability Discoverycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026LLM-Based Penetration Testing in the Presence of Honeypots ↗
  2. 2026Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
  3. 2025From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows
  4. 2024LLM Agents can Autonomously Hack Websites
  5. 2025The Dark Side of LLMs: Agent-based Attack Vectors for System-level Compromise
  6. 2026Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing
  7. 2026LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity
  8. 2026On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting