The video explains how Curl’s lead developer shut down their bug bounty program due to an overwhelming influx of low-quality, often AI-generated bug reports that created unsustainable work for the small team. It highlights that while AI can aid security research, indiscriminate use leads to noise and burnout, ultimately harming open-source projects and the effectiveness of bug bounty programs.
The video discusses the recent decision by Daniel Stenberg, the lead developer of the widely-used Curl project, to discontinue its bug bounty program on HackerOne. Curl and its associated library, libcurl, are foundational tools for making web requests across the internet. Stenberg and his team have faced a surge in low-quality, often AI-generated bug reports, especially since 2024, which has led to significant frustration and burnout. The overwhelming volume of these “slop” submissions, many of which are either irrelevant or fabricated, has made it unsustainable for the small, volunteer-driven team to continue managing the program.
Several examples are provided to illustrate the problem. Some bug reports submitted to Curl’s HackerOne page were either misdirected (such as reporting OpenSSL issues as Curl vulnerabilities) or demonstrated a fundamental misunderstanding of the code. Others cited potential vulnerabilities that were already guarded against in the code, but the submitters—often AI or AI-assisted—persisted in arguing their point despite clear evidence to the contrary. This pattern of stubborn, low-quality submissions, often written in a style reminiscent of AI-generated text, has made the triage process both time-consuming and demoralizing for maintainers.
The video then explores the broader context of bug bounty programs, explaining their original purpose: to incentivize security researchers to report vulnerabilities responsibly rather than selling them to malicious actors. Bug bounties have historically helped make software more secure by rewarding those who find and disclose bugs. However, the rise of AI tools has changed the landscape, making it easier for individuals to automate the generation of bug reports—many of which are false positives or misunderstandings—without any real downside for the submitter.
While some AI-driven security research platforms, like XPO, have demonstrated the ability to find legitimate vulnerabilities, the signal-to-noise ratio remains a significant challenge. Even skilled researchers using advanced AI prompts report that only a tiny fraction of AI-generated reports are valid, with the vast majority being noise. This creates an unsustainable burden for open-source maintainers, who must sift through dozens of false alarms for every real issue, often with little or no compensation for their time.
The video concludes by emphasizing that AI can be a valuable tool for security research when used thoughtfully and in good faith—as an assistant to help audit code, generate fuzzing harnesses, or clarify technical concepts. However, indiscriminately submitting every AI-suggested bug report is irresponsible and ultimately harmful, especially to small, underfunded projects like Curl. The speaker urges security researchers to use AI judiciously and to respect the time and effort of open-source maintainers, warning that the misuse of AI risks undermining the very programs designed to improve software security.