cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun - A Developer's Story
When Bug Bounties Break: The cURL Crisis That Signals AI's Dark Side for Open Source Security
The Breaking Point
What happens when a security feature designed to protect millions of users becomes a vector for attack—not against the software, but against the maintainers themselves?
This week, Daniel Stenberg, the creator and lead maintainer of cURL, announced something that should make every developer pause: after nearly five years, cURL is shuttering its bug bounty program.
The reason? An overwhelming flood of AI-generated security reports that are technically gibberish but consume massive amounts of human time to review and reject.
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This isn't just another "AI ruins everything" story.
It's a canary in the coal mine for open source security, exposing a fundamental vulnerability in how we've structured bug bounty programs—and revealing what happens when the economics of AI-generated content collide with the realities of volunteer-maintained critical infrastructure.
When one of the internet's most essential tools—used in everything from your car's infotainment system to Mars rovers—can't maintain a bug bounty program because of AI spam, we need to ask hard questions about the future of crowdsourced security.
The Foundation Under Everything
To understand why this matters, you need to appreciate cURL's position in the modern tech stack. Created by Daniel Stenberg in 1996, cURL is the Swiss Army knife of data transfer.
It speaks practically every protocol you've heard of—HTTP, HTTPS, FTP, SFTP, MQTT, and dozens more.
When developers joke about cURL being everywhere, they're not exaggerating: it's estimated to be running on over 10 billion devices worldwide.
The project's bug bounty program, launched through the Internet Bug Bounty initiative in 2019, seemed like a natural fit.
Here was critical infrastructure that needed security scrutiny, paired with a mechanism to compensate researchers for their time.
The program offered rewards ranging from $500 to $2,500 for legitimate security vulnerabilities—modest by big tech standards, but meaningful for an open source project.
For years, it worked. Real vulnerabilities were discovered, patched, and disclosed responsibly.
The program contributed to cURL's impressive security track record—a necessity for software that handles sensitive data transfers across the internet. But something changed in 2024.
The trickle of reports became a flood, and the flood became a deluge of what Stenberg calls "AI slop"—lengthy, technical-sounding reports that ultimately describe non-issues or fundamental misunderstandings of how cURL works.
The numbers tell a stark story. In recent months, the signal-to-noise ratio collapsed.
Where once maintainers might spend time evaluating nuanced security concerns, they found themselves wading through reports that claimed basic features were vulnerabilities, or that described attack scenarios that violated the laws of physics, let alone computer science.
Each report, regardless of quality, demanded careful review—because what if this one time, buried in the AI-generated verbosity, there was a real issue?
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The Anatomy of an AI Flood
The crisis facing cURL isn't unique, but it's particularly acute. Security reports require careful analysis—you can't just skim them.
When an AI generates a 2,000-word report claiming a buffer overflow in code that doesn't manipulate buffers, a human still needs to read enough to understand that it's nonsense.
Multiply this by dozens of reports per week, and you begin to see the problem.
These AI-generated reports share telltale characteristics. They're verbose where clarity would suffice, using security jargon like a cargo cult—present in form but absent in understanding.
They often describe theoretical vulnerabilities that would require an attacker to already have compromised the system in ways that would render the "vulnerability" moot.
One report might claim a "critical authentication bypass" in a function that doesn't handle authentication.
Another might describe elaborate attack chains that fundamentally misunderstand how memory management works in modern systems.
The sophistication is what makes them dangerous—not to systems, but to maintainers' time. These aren't obviously bogus reports with broken English and clear tells.
They're fluent, technical, and just plausible enough that they can't be dismissed without investigation.
They reference real CVEs, cite actual security principles, and include code snippets that look legitimate at first glance.
What's particularly insidious is that the generators of this content have figured out the sweet spot: make the reports just technical enough that they can't be auto-filtered, just plausible enough that they can't be instantly dismissed, and just lengthy enough that reviewing them becomes a significant time investment.
It's a DDoS attack on human attention, weaponizing the responsible disclosure process against the very people it's meant to help.
The business model is straightforward and cynical. Generate hundreds of reports across dozens of bug bounty programs.
Even if 99% are rejected, the occasional payout makes it worthwhile—especially if the content generation costs approach zero. For the perpetrators, it's a numbers game.
For maintainers like Stenberg, it's a crisis that makes the bug bounty program untenable.
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The Deeper Implications
The cURL decision exposes uncomfortable truths about open source sustainability and security.
Bug bounty programs were supposed to be a win-win: researchers get compensated, projects get security review, users get safer software.
But this model assumes good faith participation and human-scale interaction. When AI can generate plausible-looking reports at near-zero marginal cost, the economics break down entirely.
This isn't just about one project or one maintainer.
It's about the fundamental assumption that crowdsourced security review can work when the crowd includes an infinite number of artificial participants.
Every open source project with a bug bounty program is vulnerable to this same attack.
The smaller the project, the more devastating the impact—most don't have cURL's resources or Stenberg's decades of experience to weather the storm.
The security implications are sobering. Without bug bounty programs, open source projects lose a valuable source of security review.
But maintaining programs that drain maintainer time without providing value is arguably worse—it creates a false sense of security while exhausting the very people responsible for fixing real vulnerabilities.
We're watching the security-industrial complex eat itself, with AI-generated reports drowning out legitimate security research.
For the broader developer community, this represents a new kind of technical debt.
Every system that accepts user-generated content—from bug bounty platforms to code review systems to documentation wikis—now needs to account for the possibility of AI-generated noise.
The cost isn't just in filtering; it's in the erosion of trust.
When you can't distinguish between a human researcher and an AI pretending to be one, how do you maintain the human relationships that make open source work?
There's also a chilling effect to consider.
Legitimate security researchers might find their reports scrutinized more heavily or dismissed more quickly as maintainers develop hair-trigger responses to AI-style reports.
The boy who cried wolf is now an algorithm, and it's crying wolf thousands of times per day.
What Comes Next
The immediate future likely holds more of the same. Other projects will face similar decisions, and we'll see a fracturing of approaches.
Some will try technical solutions—AI detection, stricter submission requirements, proof-of-work systems. Others will retreat to invitation-only programs or abandon public bug bounties entirely.
The era of open, accessible bug bounty programs might be ending before it really began.
Platform providers like HackerOne and Bugcrowd will need to evolve rapidly. Their value proposition depends on connecting legitimate researchers with projects that need review.
If they become conduits for AI spam, they risk losing both sides of their marketplace.
Expect to see investment in detection systems, reputation mechanisms, and possibly human verification requirements that would have seemed draconian just a year ago.
The longer-term implications are more profound. We're witnessing the first waves of what happens when AI-generated content meets systems designed for human-scale interaction.
Bug bounties are just the beginning. Code contributions, documentation, issue reports, and even social coding interactions could all face similar challenges.
The transaction costs of verifying humanity might become a permanent tax on all digital collaboration.
For cURL specifically, Stenberg has indicated that security remains a top priority, just without the formal bug bounty structure.
The project will continue to accept and review security reports through traditional channels, relying on the reputation and relationships built over decades.
It's a return to an older model of open source security—one that worked before bug bounties and might have to work again after them.
The optimistic view is that this crisis will spur innovation in human verification and contribution quality assessment.
The pessimistic view is that we're watching the beginning of the end for open, permissionless collaboration—that the future involves increasingly closed gardens where participation requires proof of humanity and established reputation.
The reality, as always, will likely fall somewhere in between, but the cURL crisis makes clear that the status quo is unsustainable.
The question isn't whether we'll adapt, but how much we'll lose in the process.
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