curl Maintainers Fed Up With AI-Generated Vulnerability Reports
▼ Summary
– Daniel Stenberg highlighted the issue of AI-generated vulnerability reports, noting their polished but misleading nature and their inability to reliably find security flaws.
– Stenberg observed a recent surge in AI-generated reports, identifying them by their overly formal tone and perfect English, unlike typical human submissions.
– Some AI reports are easily detected, such as one that included the prompt instructing it to “make it sound alarming.”
– Stenberg called on HackerOne to take stronger action against AI-generated reports and improve tools to combat this behavior.
– Experts like Tobias Heldt and Seth Larson suggested solutions like requiring bonds for report reviews and warned of the growing trend’s impact on open-source projects.
Maintainers of the widely-used curl tool are pushing back against a flood of low-quality, AI-generated vulnerability reports clogging their security channels. Daniel Stenberg, curl’s creator, recently called attention to the issue after noticing multiple suspicious submissions that followed an unmistakable pattern—overly polished language, improbable technical claims, and an uncanny uniformity that human researchers rarely produce.
Stenberg described how these reports often arrive with flawless formatting, unnaturally polite phrasing, and bullet-pointed perfection—hallmarks of automated content generation. One particularly telling example accidentally included the AI’s original prompt, ending with instructions to “make it sound alarming.”
The problem isn’t just about wasted time—it risks drowning legitimate security research in noise. While the volume remains manageable for now, Stenberg warns the trend is worsening. He’s urging platforms like HackerOne, which hosts curl’s bug bounty program, to implement stricter safeguards. “We need better tools to filter this out,” he emphasized, suggesting measures like requiring reporters to stake a small bond for review consideration.
Security experts across open-source projects are noticing similar patterns. Seth Larson from the Python Software Foundation has documented comparable AI-generated reports, warning that if smaller projects like curl are affected, the issue likely runs much deeper. “This isn’t isolated—it’s a systemic problem,” Larson noted late last year.
Stenberg’s public post sparked widespread discussion, with over 200 comments and hundreds of shares. Many echoed concerns that AI tools, while useful in other contexts, are being misused to flood maintainers with unreliable claims—sometimes in pursuit of reputation points or bounty payouts.
For now, curl’s team continues manually filtering submissions, but the solution may require broader changes. As Stenberg put it, “If platforms and researchers don’t adapt, we’ll keep wasting time on reports that don’t help anyone.”
(Source: Ars Technica)
