I built a live honeypot that catches AI agents. Here's what happened (github.com)

A new AI honeypot tool has been developed to identify and analyze AI agents' vulnerabilities. By sending AI agents to a specified URL, the tool injects unique canary tokens—specific strings designed to detect data breaches—into its content. If the agent reproduces these tokens in its output, it reveals that it has been manipulated. The honeypot includes various traps that test for critical issues like prompt injection and unauthorized data submission, providing valuable insights into the behavior of AI agents as they interact with web content. This initiative is significant for the AI/ML community as it addresses pressing concerns about the security and reliability of AI applications. By exposing how agents respond to specific prompts and hidden dangers, developers can better anticipate vulnerabilities and improve safety measures in AI systems. The technical setup employs GitHub for tracking breaches while ensuring that no real user data is collected, focusing instead on fabricated scenarios for analysis. Through this innovative approach, the project encourages transparency and collaboration in understanding AI security risks, promoting safer AI deployment in real-world applications.