AI might cut false positives, but it won’t stop the slop 

AI might cut false positives, but it won’t stop the slop 

https://cyberscoop.com/ai-vulnerability-reporting-bug-bounty-noise/

Publish Date: 2026-05-18 16:45:38

Source Domain: cyberscoop.com

The article discusses the growing impact of advanced AI models like Anthropic’s Mythos and OpenAI’s Daybreak on cybersecurity and vulnerability reporting. Organizations and bug bounty programs are receiving a surge in reports, often due to the use of open-source AI tools and models capable of automating the detection of bugs. GitHub has noticed a sharp increase in AI-generated submissions, leading it to revise its definition of a complete bug report. While AI is seen as a powerful tool to amplify security efforts, there is concern over the quality of submissions, with many lacking proof of concept and realistic attack scenarios. Companies like GitHub and Cloudflare emphasize the need for validation, reproduction, and working proof of concept to differentiate actionable findings from noise.

Key issues highlighted include difficulties in triaging and validating vulnerabilities, an increased risk of false positives in languages lacking memory safety, and AI’s tendency to deliver results based on user queries, regardless of their feasibility. Cloudflare’s tests with Mythos showed some improvements in reducing false positives for more intricate exploit scenarios and generating proof-of-concept code. Conversely, some developers argue that the performance gap between newer frontier AI models and older versions may not be as significant as originally touted, noting a decline in the quality of AI-generated reports. Daniel Stenberg of curl noted that while Mythos found some vulnerabilities, most flagged issues were false positives, leading him to conclude that the hype around the model might be more marketing than substance.

Key Points:
– Increasing reliance on AI tools for bug detection is leading to a surge in reported vulnerabilities, causing concerns about the quality and validity of these reports.
– There is a need for higher standards of validation and proof of concept to ensure actionable intelligence in vulnerability reports.
– Cloudflare’s experience with Mythos showed some improvements over other models, but the gap in performance compared to older models is not as large as anticipated by some.
– While AI offers powerful cybersecurity capabilities, the challenge remains in differentiating real threats from noise and false positives.
– The effectiveness of AI in vulnerability detection is also hampered by AI’s tendency to provide speculative findings that often require human verification.