cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun
A Developer's Story
When AI Drowns Out Real Security: The cURL Bug Bounty Collapse and What It Means for Open Source
Picture this: You're Daniel Stenberg, maintainer of cURL—one of the most ubiquitous pieces of software on the planet, quietly running on billions of devices. Your bug bounty program, meant to strengthen security by rewarding legitimate vulnerability discoveries, has become a nightmare. Instead of carefully researched security reports, you're drowning in AI-generated garbage—hallucinated vulnerabilities that don't exist, copy-pasted templates with zero understanding, and "security researchers" who can't even explain their own submissions. Last week, you finally pulled the plug. The bug bounty program that was supposed to make cURL safer had become a liability, buried under an avalanche of AI slop.
This isn't just another "AI is ruining everything" story. It's a canary in the coal mine for how generative AI is fundamentally breaking the social contracts and trust systems that underpin open source security. When cURL—a project that processes data transfers for everything from your PlayStation to NASA's Mars rovers—can't maintain a bug bounty program because of AI spam, we need to ask ourselves: what happens to security research when the signal-to-noise ratio approaches zero?
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The Rise and Fall of a Security Institution
To understand why this matters, you need to understand what cURL is and why its bug bounty program existed in the first place. cURL isn't just another command-line tool that developers use to test APIs. It's the Swiss Army knife of data transfer, supporting everything from HTTP and FTP to IMAP and MQTT. When you update your iPhone, when your smart TV streams Netflix, when your CI/CD pipeline pulls dependencies—there's a good chance cURL is involved somewhere in that chain.
The project, maintained primarily by Daniel Stenberg since 1998, has always taken security seriously. Over the years, cURL has had its share of vulnerabilities—memory leaks, buffer overflows, the kinds of issues you'd expect in C code handling untrusted network data. The bug bounty program, launched through platforms like HackerOne and later managed independently, was meant to incentivize security researchers to find these issues before malicious actors could exploit them.
For years, this worked reasonably well. Security researchers would spend hours, sometimes days, analyzing cURL's source code, fuzzing its parsers, and testing edge cases. When they found something legitimate, they'd write detailed reports explaining the vulnerability, how to reproduce it, and potential impact. The rewards weren't massive—typically ranging from a few hundred to a few thousand dollars—but they provided recognition and compensation for valuable security work.
Then came the AI gold rush of 2023-2024. Suddenly, anyone with access to ChatGPT or Claude could position themselves as a "security researcher." The barriers to entry for bug bounty hunting dropped to zero, and with it went the quality control. What followed was entirely predictable yet somehow still shocking in its scale.
The Anatomy of AI-Generated Security Theater
The problem isn't just volume—it's the particularly insidious nature of AI-generated security reports. These submissions often look legitimate at first glance. They use the right terminology, reference real CVEs, and follow standard vulnerability reporting templates. But dig deeper, and you find nothing but shadows and smoke.
Take a typical AI-generated cURL vulnerability report. It might claim to have discovered a "critical authentication bypass in cURL's TLS handling" complete with technical-sounding details about certificate validation and memory corruption. The report includes code snippets, suggests CVSS scores, and even provides "proof of concept" exploits. To a harried maintainer doing initial triage, it might look real enough to warrant investigation.
But here's where it falls apart: The code snippets reference functions that don't exist in cURL's codebase. The line numbers are wrong. The described behavior contradicts how TLS actually works. When challenged, the submitter can't answer basic follow-up questions because they don't understand what they've submitted—they're just a human proxy for an AI that's confidently hallucinating about security vulnerabilities.
Stenberg has shared examples of reports claiming SQL injection vulnerabilities in cURL—a tool that doesn't use SQL. Others describe elaborate authentication bypasses in protocols that cURL doesn't even implement. One particularly absurd case involved a "researcher" submitting multiple variations of the same non-existent vulnerability, each time with slightly different AI-generated explanations, apparently hoping that volume would substitute for validity.
The time cost is staggering. Each false report requires initial review, technical investigation, and often back-and-forth communication trying to get clarification from submitters who don't actually understand their own submissions. Multiply this by hundreds of reports, and you have a maintainer spending more time debunking AI hallucinations than actually improving security.
What's particularly galling is that these AI-wielding bounty hunters aren't just wasting time—they're actively degrading security. Real vulnerabilities might get lost in the noise. Maintainer burnout increases. The community's trust in bug bounty programs erodes. And perhaps most dangerously, the constant false alarms create a "boy who cried wolf" situation where legitimate security concerns might be dismissed as just more AI garbage.
The Broader Implications for Open Source Security
The cURL situation isn't an isolated incident—it's a preview of a crisis facing the entire open source ecosystem. Bug bounty programs have become a critical part of how we secure the software supply chain. Companies like Google, Microsoft, and Meta pour millions into these programs, not out of altruism, but because it's far cheaper to pay researchers than to deal with the aftermath of exploited vulnerabilities.
But what happens when these programs become unusable? We're already seeing other projects report similar problems. The Python Software Foundation has noted an uptick in low-quality security reports. The Node.js security team has implemented stricter verification requirements. Even large corporate bug bounty programs are struggling with AI-generated submissions, though they have more resources to throw at the problem.
The incentive structure is completely broken. Bug bounty platforms often reward researchers based on volume and velocity—metrics that favor AI-generated spam over careful analysis. Some platforms have implemented "reputation systems," but these are easily gamed by submitting a mix of copied legitimate findings and AI-generated padding. The economic incentives all point toward more automation, not less.
For open source maintainers, this is catastrophic. Unlike Microsoft or Google, most open source projects don't have dedicated security teams. They rely on volunteer maintainers who are already stretched thin. When bug bounty programs—supposedly a way to crowdsource security help—become another source of work rather than a solution, something has to give.
The trust model of open source is also under attack. The social contract has always been that while anyone can contribute, contributions should be made in good faith. Code contributions go through review. Documentation updates get vetted. But security reports often get priority attention because of their potential impact. When that channel gets polluted with AI spam, it breaks the fundamental assumption that people reporting security issues actually understand what they're reporting.
We're also seeing a skill degradation in the security research community. Why spend weeks learning about memory management and buffer overflows when you can just prompt an AI to generate plausible-sounding vulnerability reports? The next generation of security researchers might never develop the deep technical skills needed to find real vulnerabilities because the short-term incentives all point toward automation over understanding.
What Comes Next: Adapting to the AI Flood
So where do we go from here? The cURL project's decision to end its bug bounty program is both understandable and troubling. It's a rational response to an irrational situation, but it also means one less avenue for legitimate security researchers to help secure critical infrastructure.
Some projects are experimenting with technical solutions. Requiring proof-of-concept code that actually compiles and demonstrates the vulnerability. Implementing "reverse bug bounties" where researchers must first fix the issue they've found. Using cryptographic challenges that require actual interaction with the codebase. But all of these add friction for legitimate researchers while only slightly raising the bar for AI-assisted spam.
The bug bounty platforms themselves need to take responsibility. HackerOne, Bugcrowd, and others have built businesses on connecting researchers with programs. If their platforms become conduits for AI spam, they risk killing the very ecosystem they depend on. We're starting to see some movement here—stricter verification, better filtering, reputation systems with real teeth—but it's not enough.
There's also a growing conversation about legal remedies. Should there be penalties for knowingly submitting false vulnerability reports? Some argue this could chill legitimate research, but others point out that fraud is fraud, whether it's generated by AI or not. The Computer Fraud and Abuse Act (CFAA) could theoretically apply, but enforcement would be challenging and potentially counterproductive.
The most promising direction might be a return to smaller, more trusted communities. Instead of open bug bounty programs, projects might work with vetted security teams or trusted researchers. This sacrifices the "wisdom of crowds" approach but ensures quality over quantity. Some projects are already moving in this direction, creating invitation-only security programs or working directly with established security firms.
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Whatever solutions emerge, they need to happen fast. The AI tools are only getting better at generating plausible-looking content, and the economic incentives for abuse aren't going away. If we can't figure out how to maintain signal in the noise, we risk losing one of the most effective security tools we've developed. The cURL bug bounty program's death might be just the beginning of a larger collapse in crowdsourced security research—and that's a future none of us can afford.
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Written by Andrew • January 25, 2026
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