LLM Agent Honeypot
Unveiling Real-World AI Threats
Project Overview
The LLM-Hack Agent Honeypot is a project designed to monitor, capture, and analyze autonomous AI Hacking Agents in the real world.
How It Works:
- Simulation: We deploy a simulated "vulnerable" service to attract potential threats.
- Catching Mechanisms: This service incorporates specific counter-techniques designed to detect and capture AI-Hacking Agents.
- Monitoring: We monitor and log all interactions, waiting for potential attacks from LLM-powered agents.
- Capture and Analysis: When an AI agent engages with our system, we capture the attempt and their system prompt details.
Why?
Our objectives aim to improve awareness of AI Hacking Agents and their current state of risks by understanding their real-world usage and studying their algorithms and behavior in the wild.
Total Interactions
3710355
Attempts to engage with our honeypot
AI Agents
6
Potential AI-driven hacking attempts
Weekly Attack Distribution
Top Threat Origins
- 159.203.11.24 156368 attempts
- 94.156.8.237 63543 attempts
- 157.173.196.166 49810 attempts
- 43.239.111.78 49145 attempts
- 5.75.192.181 48150 attempts
- 197.155.74.150 47157 attempts
- 176.32.152.53 45457 attempts
- 20.102.89.253 44265 attempts
- 93.188.83.96 43605 attempts
- 145.239.255.60 42015 attempts
Global Threat Distribution
- China 16.37%
- United States 11.17%
- Singapore 10.28%
- Hong Kong 8.7%
- Canada 8.46%
- India 5.42%
- Germany 4.03%
- Russia 2.66%
- The Netherlands 2.46%
- Brazil 2.45%
Top AI Threat Origins
- 195.158.248.232 4 attempts
- 195.158.248.230 2 attempts
Global AI Threat Distribution
- India 100.0%
Ongoing Research
Our project continues to evolve as we gather more data on real-world AI threat actors. We're constantly refining our methods to stay ahead of emerging attack vectors and contribute valuable insights to the cybersecurity community.
By studying these AI agents in action, we're not just theorizing about potential risks—we're documenting and analyzing actual threats as they unfold. This real-time approach allows us to develop more effective defenses and push the boundaries of AI security research.