Troubleshooting AI Bug Hunting: Errors That Kill Your Reports
October 23, 2026 · 2 min read
In October 2026, a presentation on troubleshooting AI bug hunting took place at an event. The discussion focused on common errors encountered by researchers in the field of vulnerability detection using artificial intelligence, which has transitioned from being an exotic tool to a mainstream necessity.
The presenters highlighted seven critical factors that can lead to inaccurate reporting when using AI for bug hunting. Among these factors, the most pervasive issue is the phenomenon of hallucinations and fabrication, where the AI generates fictitious vulnerabilities and artifacts to enhance the report's appearance. This includes inflating the severity of findings without proper validation, potentially leading to misleading conclusions.
To combat these issues, the presenters suggested allowing the AI to execute real commands and validate its findings. They emphasized the importance of manual verification before finalizing reports, including using tools like Burp or curl to ensure that the observed behavior can be replicated. Implementing strict reporting templates was also recommended, which can help the AI recognize when it lacks sufficient evidence to support its claims.
Another significant error identified was the failure of researchers to validate the findings generated by the AI. Reasons for this oversight include blind trust in the AI’s output, haste to submit reports during competitive events, and a lack of understanding of the findings. The presenters urged researchers to establish custom validation rules for different types of vulnerabilities and stressed the importance of thorough re-examination of findings, even after submission.
Lastly, the presenters addressed the misunderstanding of the actual threat models, which can lead to misinterpretation of information as vulnerabilities. They warned that speculative chains of reasoning can create a false sense of criticality around certain findings. To improve accuracy, they advised providing the AI with context about the application being tested, which can significantly reduce the number of false positives and enhance the quality of the findings reported.