Daniel Stenberg has maintained curl for over 30 years. It's the HTTP client embedded in nearly every operating system, router, and embedded device on Earth. And in January 2026, he did something he never expected to do: he shut down the project's bug bounty program — the one he'd run since 2019, the one that had confirmed 87 vulnerabilities and paid out over $100,000.
Not because the program failed. Because it was being DDoSed by AI.
The Numbers
Before the AI wave, curl received roughly one security report per week. The confirmed-vulnerability rate ran north of 15% — healthy, manageable, sustainable for a volunteer team.
Then LLMs made it trivial to generate a plausible-looking vulnerability report in seconds.
- 2024: The rate began climbing. Stenberg started tracking AI-assisted submissions.
- Mid-2025: Volume spiked to 8× the normal rate. About 20% of submissions were AI slop. The confirmed rate plummeted to below 5%.
- January 2026: In the first 21 days alone, curl received 20 AI-generated vulnerability reports. Seven arrived in a single 16-hour window.
- June 2026: The pace hit one report every 18 hours — 4–5× the 2024 rate, double the 2025 pace.
And here's the part that should make you uncomfortable: in six years of tracking AI-generated reports, not a single one — zero — discovered a genuine vulnerability on its own.
The signal went down. The volume went up. That's the worst possible combination for an unpaid security team.
The Hidden Cost Nobody Sees
Stenberg describes the toll as "primarily mental." His wife has voiced concerns about his work hours for the first time in his life.
Each AI-generated report isn't just "read and dismiss." It's:
- Read a 2,000-word report with confident, overly polite language and zero substance.
- Reproduce the claimed vulnerability (or prove it doesn't exist).
- Trace the code path to confirm or debunk.
- Write back with an explanation, because that's what you do.
A single debunk can take hours. Multiply that by 20 reports in three weeks, and you've lost a month of security review capacity — capacity that was already stretched across 12 confirmed CVEs pending and a projected 30+ for 2026.
And the slop is getting better. In early 2025, most AI reports were hallucinated — they referenced functions that didn't exist. By 2026, the models improved. The reports are now technically accurate, which means they take longer to debunk, not shorter. Stenberg called it "high-quality chaos."
The irony is not lost on anyone: AI made finding and reporting vulnerabilities dramatically cheaper. Triage remains expensive human work. The cost was transferred entirely to the maintainer.
This Is a Funding Problem, Not a Tooling Problem
The obvious response is "build a better filter." Rate-limit AI submissions. Add a CAPTCHA. Require a proof-of-concept video. GitHub is literally considering a PR kill switch because ~14% of all pull requests on the platform now involve AI tooling, and ~17 million robot PRs land per month.
But filters are a band-aid on a structural problem.
The real issue: AI collapsed the cost of producing a security report to near zero, while the cost of verifying it stayed the same. Every project with a bug bounty, a public issue tracker, or a security@ email address is now a target. curl just happened to have the most visible one.
Stenberg's solution — killing the bounty — removes the financial incentive for slop. But it also removes the incentive for good researchers. The 87 confirmed vulnerabilities and $100K in payouts were real security value. You can't have one without the other, and you can't triage the difference without funded human attention.
A funded maintainer can:
- Hire a part-time security triage engineer to handle the first pass, filter the obvious slop, and route only credible reports to Stenberg.
- Invest in automated triage tooling — not to replace human judgment, but to reduce the "read and dismiss" cycle from hours to minutes.
- Fund community management — clear contribution guidelines, response templates, and a public "AI slop" catalog (which Stenberg already maintains as a gist) so submitters self-correct before they waste anyone's time.
None of that is possible on goodwill and weekends.
What You Can Do
Stenberg's position on AI-generated code is pragmatic: "I don't want to read your AI explanation. I want to read your human explanation of why you'd need that code, and then you can write the code however you want."
The same principle applies to funding. You don't need to be a security researcher to help. You need to be a person who uses curl — which, if you've ever made an HTTP request, you are.
Sponsor the maintainer. curl doesn't have a traditional GitHub Sponsors page, but the Sovereign Tech Fund model (which backed FFmpeg) shows that structured, recurring funding works. If you work at a company that ships curl in production, write a one-paragraph memo to your CTO: "We depend on curl. Its bug bounty was shut down because AI slop made triage unsustainable. Here's what a part-time security reviewer costs. Here's what a curl outage costs."
If you're a security researcher: file better reports. Include a reproducer. Scope your findings. Acknowledge AI assistance. A report that costs a maintainer minutes instead of hours is a gift.
If you're a platform: GitHub, HackerOne, Bugcrowd — the friction on the submission side needs to scale with volume. Rate limits, proof-of-work, signal requirements (Node.js already implemented one). The cost of producing a report can't be zero while the cost of verifying it stays the same.
curl didn't break. It's still the most battle-tested HTTP client in existence. But the person keeping it safe is one exhausted human, and the flood isn't slowing down.
The next project to shut down its security program won't have 30 years of track record to fall back on.
Top comments (0)