The Day the Music Died: cURL's Bug Bounty Program Shuts Down
If you've been in the open source world for more than a minute, you know cURL. It's the silent workhorse that powers everything from API calls to file transfers—a tool so ubiquitous that it's practically internet infrastructure. So when Daniel Stenberg, cURL's creator and maintainer, announced in early 2026 that they were shutting down their bug bounty program, the programming community collectively did a double-take.
But here's the kicker: they didn't shut it down because they ran out of money or found all the bugs. They shut it down because they were drowning in what the community now calls "AI slop"—dozens, sometimes hundreds, of AI-generated vulnerability reports that were mostly nonsense, occasionally dangerous, and always time-consuming to triage.
I've been following cURL's development for years, and I've never seen Stenberg this frustrated. In his announcement, he described spending "hours each week" sorting through reports that clearly came from ChatGPT or similar tools—reports that misunderstood basic C concepts, suggested vulnerabilities that didn't exist, or worse, proposed "fixes" that would introduce actual security holes.
This isn't just a cURL problem. It's a warning sign for every open source project that relies on community contributions. When AI tools make it trivial to generate plausible-looking bug reports, how do maintainers separate signal from noise? And what happens to legitimate security researchers when their reports get lost in the AI-generated flood?
What Exactly Was Happening? The AI Report Tsunami
Let me paint you a picture of what cURL maintainers were dealing with. Imagine you're a maintainer of a critical piece of internet infrastructure. You've set up a bug bounty program because you want to encourage responsible disclosure and reward people who find real issues. Then the reports start coming in.
At first, it's manageable—maybe one or two questionable reports per week. But by late 2025, it had become a deluge. Stenberg described receiving reports with telltale AI signatures:
- Reports that cited "buffer overflow vulnerabilities" in code that didn't use buffers
- Suggestions to "sanitize input" in functions that didn't accept external input
- Confusion between different memory management functions (malloc vs calloc, anyone?)
- References to CVE numbers from completely unrelated projects
- Identical phrasing across multiple reports from different "researchers"
The worst part? These weren't just low-quality—they were actively harmful. I spoke with several open source maintainers who've experienced similar issues, and they all mentioned the same pattern: AI tools would sometimes suggest "fixes" that were objectively wrong. One maintainer told me about a report that suggested replacing a perfectly safe strncpy() with strcpy()—a change that would have introduced an actual buffer overflow.
And here's what really grinds my gears: these AI-generated reports weren't coming from malicious actors trying to sabotage projects. They were coming from well-meaning but inexperienced developers who thought they were helping. They'd feed cURL's source code into an AI tool, ask "find vulnerabilities," and submit whatever came out without understanding it.
The Real Cost: Maintainer Burnout and Lost Legitimate Reports
You might be thinking, "So what? Just ignore the bad reports." But that's easier said than done. Every report—even an obviously AI-generated one—requires investigation. Someone has to:
- Read the report (which can be pages long, thanks to AI verbosity)
- Locate the referenced code
- Understand what the reporter is claiming
- Determine if it's a real issue
- Write a response explaining why it's not a vulnerability (if it isn't)
- Potentially engage in follow-up discussion
For a small team like cURL's, this becomes unsustainable fast. Stenberg estimated he was spending 10-15 hours per week just triaging AI-generated reports. That's time that could have been spent fixing actual bugs, implementing new features, or reviewing legitimate security reports.
But there's an even bigger problem: the signal-to-noise ratio gets so bad that legitimate reports get lost. I've heard from security researchers who've had their valid bug reports initially dismissed because maintainers assumed they were more AI slop. One researcher told me, "I spent weeks crafting a detailed report with proof-of-concept code, only to get a form response thanking me for my 'AI-generated submission.'"
This creates a vicious cycle: legitimate researchers get frustrated and stop reporting, leaving only AI-generated reports, which makes maintainers more skeptical of all reports, which drives away more legitimate researchers.
Why This Is Bigger Than Just cURL
cURL's situation isn't unique—it's just the most visible example. I've talked to maintainers of everything from small JavaScript libraries to major Linux distributions, and they're all seeing similar patterns. The common thread? AI tools have lowered the barrier to entry for bug reporting to near zero, but they haven't lowered the barrier to useful bug reporting.
Here's what's happening across the ecosystem:
Copy-paste vulnerability hunting: Developers with minimal C experience (or no C experience) are using AI tools to analyze codebases they don't understand. The AI identifies patterns that look like vulnerabilities based on training data, but without context about how the code actually works.
The "shotgun" approach: Some reporters are literally submitting every potential issue the AI identifies, hoping that one will stick and earn a bounty. This is the bug reporting equivalent of spam.
Misunderstanding of scope: AI tools often flag issues that are technically true but not security-relevant in context. For example, they might flag a potential integer overflow in code that only handles trusted data or runs in a sandboxed environment.
The expertise gap: This is the most concerning part. Bug hunting used to require deep understanding of both the codebase and security principles. Now, anyone can generate reports that look professional but lack substance. The problem isn't that AI is finding bugs—it's that AI is finding things that look like bugs to an AI but aren't actually bugs to a human expert.
How to Submit a Useful Bug Report (The Right Way)
If you want to contribute to open source security—and I hope you do—here's how to do it right. These guidelines come straight from conversations with maintainers who still welcome quality reports.
Understand the code first: Before you report anything, make sure you actually understand what the code does. Read the documentation. Look at similar issues in the tracker. If you're using AI to help you understand, that's fine—but verify everything yourself.
Create a minimal reproduction: This is the gold standard. Can you write a small program that demonstrates the issue? If you can't reproduce it, it's probably not a real bug. And no, "the AI said it's vulnerable" doesn't count as reproduction.
Explain the impact: What can an attacker actually do with this vulnerability? Is it theoretical or practical? Can it be exploited remotely? Does it require special conditions? Be specific.
Suggest a fix (if you can): If you understand the issue well enough to suggest a fix, include it. But—and this is crucial—make sure your fix actually works and doesn't break anything else. Test it.
Be patient and responsive: Maintainers are volunteers. They might take time to respond. When they ask questions, answer them thoroughly. Don't get defensive if they disagree with your assessment.
One more thing: if you're new to security research, consider starting with projects that have mentorship programs or clearer guidelines. Don't jump straight to critical infrastructure like cURL.
The Future of Bug Bounties in the AI Age
So where do we go from here? cURL's decision to end their bug bounty program is a wake-up call, not just for maintainers but for the entire open source ecosystem. Here are some approaches I'm seeing emerge:
Stricter submission requirements: Some projects are now requiring proof of concept code for all submissions. Others are implementing "knowledge checks"—simple questions about the codebase that filter out people who haven't actually looked at it.
Tiered bounty programs: Instead of open submissions, some projects are moving to invite-only programs for critical vulnerabilities, with separate channels for less experienced researchers.
AI detection tools: Ironically, some projects are using AI to detect AI-generated reports. While not perfect, these tools can flag reports with characteristic AI patterns for closer review.
Community moderation: Larger projects are creating triage teams specifically to handle initial report screening, freeing maintainers to focus on confirmed issues.
Education and guidelines: The most successful approach I've seen is projects that provide clear, detailed guidelines for what constitutes a valid report—and what doesn't. When researchers know what's expected, they're less likely to submit low-quality reports.
Personally, I think we need a fundamental shift in how we think about bug bounties. They shouldn't be seen as a way to crowdsource security testing to anyone with an AI tool. They should be partnerships between projects and skilled researchers. Quality over quantity, every time.
What This Means for You as a Developer
Whether you're a maintainer, a security researcher, or just someone who uses open source software, this situation affects you. Here's what you should take away:
For maintainers: Consider implementing some of the filtering mechanisms I mentioned earlier. Be proactive about setting expectations. And most importantly, don't be afraid to say no to low-quality submissions—your time is valuable.
For researchers: Develop actual expertise. Learn the languages and frameworks you're testing. Understand the difference between a theoretical vulnerability and a practical one. And for heaven's sake, test your findings before submitting them.
For everyone else: Support the open source projects you rely on. That doesn't just mean money (though that helps). It means being thoughtful about how you interact with these projects. Don't use AI tools as a shortcut to pretend expertise you don't have.
And here's something I've learned from years in this space: the best bug reports come from people who actually use the software. If you're trying to find vulnerabilities in cURL, start by using cURL for real projects. Understand how it works in practice, not just how it looks in source code.
Common Questions (And Real Answers)
"Can't AI tools actually find real bugs?"
Absolutely—but they find potential bugs, not confirmed vulnerabilities. The difference matters. An AI might flag 100 potential issues, of which 5 are real bugs, and only 1 is a security-relevant vulnerability. A human expert needs to verify each one. The problem isn't that AI finds issues; it's that non-experts treat AI output as verified truth.
"Why not just use the bounty money to hire more maintainers?"
Most bug bounty programs don't have that much money. cURL's program was funded by donations and corporate sponsorships, but even that wasn't enough to hire full-time staff just for triage. And honestly, throwing money at the problem doesn't fix the fundamental issue: maintainer time is finite, and AI-generated reports consume it inefficiently.
"What about false positives from human researchers?"
Human researchers make mistakes too—but there's a qualitative difference. A human mistake usually comes from misunderstanding something specific. An AI-generated false positive often comes from pattern matching without understanding. Human mistakes are educational opportunities; AI mistakes are just noise.
"Will other projects follow cURL's lead?"
Some already have. I know of at least three major projects that have quietly scaled back their bounty programs or made them invitation-only. Others are watching cURL's experience closely. The trend is definitely toward more restrictive programs, not more open ones.
The Path Forward: Quality Over Quantity
cURL's decision to end its bug bounty program is disappointing, but understandable. When a well-intentioned system gets gamed—even unintentionally—it stops serving its purpose. The flood of AI-generated reports wasn't just annoying; it was actively undermining the program's goal of improving cURL's security.
What gives me hope is that the community is having this conversation openly. We're recognizing that AI tools, for all their benefits, come with costs. We're learning that making something easier doesn't always make it better. And we're rediscovering the value of actual expertise.
If you care about open source security—and you should—here's my challenge to you: develop real skills. Learn to read code, not just feed it to AI. Understand security principles, not just vulnerability patterns. And when you find something, approach it with humility and thoroughness.
The future of open source depends on engaged, knowledgeable contributors, not AI-powered spam. cURL's experience is a warning, but it's also an opportunity—to build better systems, develop better practices, and remember why we do this work in the first place.
Because at the end of the day, open source isn't about finding bugs. It's about building software that works—for everyone.
