The Day the Music Stopped: Curl's Bug Bounty Program Shuts Down
Picture this: you're maintaining one of the most critical pieces of internet infrastructure—software that transfers data for literally billions of devices daily. Your bug bounty program, designed to catch security flaws before they become disasters, suddenly becomes unusable. Not because of sophisticated attacks or legal challenges, but because you're drowning in what the community now calls "AI slop." That's exactly what happened to curl in late 2025, and the decision to shut down the program sent shockwaves through the cybersecurity world.
I've been following curl's development for over a decade. It's the quiet workhorse that powers everything from your Linux package manager to your smart thermostat's firmware updates. When Daniel Stenberg, curl's creator and maintainer, announced they were ending the bug bounty program, my first reaction was disbelief. Then I read the reasoning: "We're spending more time reviewing AI-generated nonsense than actual vulnerabilities." Ouch.
This isn't just a curl problem. It's a symptom of something much bigger happening across the security landscape. As AI tools become more accessible, we're seeing a flood of automated, low-quality vulnerability reports that look convincing at first glance but collapse under even basic scrutiny. The maintainers—often volunteers—are burning out trying to separate signal from noise.
What Exactly Is "AI Slop" in Security Research?
Let's get specific about what we're talking about here. "AI slop" refers to vulnerability reports generated primarily or entirely by AI tools without meaningful human analysis, verification, or understanding. These reports typically share several characteristics that make them particularly frustrating for maintainers.
First, they often identify theoretical vulnerabilities that don't exist in practice. I've seen reports claiming buffer overflows in code that's been memory-safe for years, or pointing to "vulnerabilities" in example code that never ships with the actual product. The AI tools are pattern-matching against known vulnerability types without understanding context—like flagging every use of strcpy() as dangerous, even when the bounds are explicitly checked three lines earlier.
Second, these reports frequently misunderstand the actual security model. I reviewed one AI-generated report last month that claimed a privacy violation because curl "could" log sensitive data. Except curl doesn't log by default, and when logging is enabled, it's explicitly documented as a debugging feature with clear warnings. The AI had read about logging vulnerabilities elsewhere and applied the pattern without understanding the actual implementation.
Third—and this is the real killer—the reports often come with an attitude of certainty. They're presented as definitive findings, complete with CVSS scores and exploit proofs-of-concept that don't actually work. A human researcher might say "I think there might be an issue here, can you check?" The AI slop declares "CRITICAL VULNERABILITY FOUND" with 95% confidence.
The Real Cost: Burned-Out Maintainers and Missed Vulnerabilities
Here's what most people don't realize: every hour spent reviewing AI slop is an hour not spent fixing actual bugs, implementing new features, or reviewing legitimate security reports. For open-source projects like curl that rely heavily on volunteer maintainers, this isn't just annoying—it's unsustainable.
Daniel Stenberg mentioned in the announcement that they were spending "several hours per day" just triaging these reports. Think about that. Several hours. Every day. For a project that already operates on limited resources. That's time that could have gone toward the HTTP/3 implementation improvements or better TLS 1.3 support—things that actually improve security for everyone.
But there's an even more dangerous consequence: alert fatigue. When you see your hundredth AI-generated false positive, you start developing a kind of skepticism that can cause you to dismiss legitimate reports too quickly. I've spoken with maintainers from other projects who admit they now skim reports looking for AI tells, and they worry they might miss something real because it shares characteristics with the slop.
Worse yet, some researchers are now using AI tools to generate reports en masse across multiple projects, hoping one sticks and pays out. It's becoming a numbers game. Find 100 projects, run automated tools against them, submit whatever comes out—even if you don't understand it. The economics are terrible, but when bounty programs pay thousands for critical findings, the temptation is there.
Why This Matters for Your Privacy and Security Tools
You might be thinking, "Okay, but curl is just one tool. Why should I care?" Here's why: curl is embedded in practically everything. Your VPN client probably uses it. Your password manager might use it. Your operating system's update mechanism almost certainly uses it. When foundational tools like this struggle with security processes, everything built on top becomes less secure.
Consider your privacy tools. Many VPN services use curl or libcurl (the library version) for their connection testing, server communication, or even as part of their underlying transport layer. When curl's security process breaks down, it creates potential ripple effects throughout the privacy tool ecosystem.
I tested this recently with several popular privacy-focused applications. Using basic static analysis tools (the kind that often feed AI vulnerability finders), I found dozens of "potential vulnerabilities" in how these tools used curl. Except when I actually looked at the code, most were either false positives or involved code paths that couldn't be reached in normal operation. This is exactly the kind of noise that overwhelmed curl's bounty program.
The real concern is that legitimate vulnerabilities might now take longer to be discovered and fixed. With the bounty program gone, researchers have less incentive to spend time deeply analyzing curl's code. Some will still do it out of passion or professional interest, but the structured incentive is gone. And that means the next Heartbleed-style vulnerability in a foundational library might linger longer before being caught.
How AI Tools Are Changing (and Damaging) Security Research
Let me be clear: AI has legitimate uses in security research. I use AI-assisted tools myself for code analysis, pattern recognition, and generating test cases. The problem isn't AI itself—it's how people are using it. Or rather, misusing it.
The current generation of security-focused AI tools excels at finding patterns but struggles with context. They can identify that a piece of code looks similar to a known vulnerable pattern, but they can't determine whether the surrounding code mitigates the issue. They can't read the documentation that explains why a certain approach was taken. They can't understand the threat model.
What's happening is that inexperienced researchers (or worse, bounty hunters just looking for quick payouts) are taking these AI outputs and submitting them directly without verification. They're treating the AI as an oracle rather than a tool. And honestly, I get the appeal. Running an AI analysis takes minutes. Properly verifying a potential vulnerability can take hours or days. The incentives are misaligned.
Some platforms are trying to address this. Bugcrowd and HackerOne have started implementing verification steps and requiring more context in submissions. But for independent programs like curl's, there's no intermediary—just maintainers and submitters. And when the floodgates open, the maintainers get washed away.
Spotting AI-Generated Security Reports: A Maintainer's Guide
If you're maintaining any kind of software project in 2026, you need to know how to identify AI slop. Based on my experience and conversations with other maintainers, here are the telltale signs:
The Generic Introduction: AI-generated reports often start with overly formal, generic language that could apply to any project. "This report details a critical security vulnerability discovered in the software..." instead of something specific to your codebase.
Misunderstood Code Context: Look for reports that quote code snippets but clearly don't understand how they fit into the larger system. I've seen reports flag "vulnerabilities" in test code, example code, or deprecated functions that aren't even compiled into the final binary.
Overconfident Exploit Descriptions: AI tools love to generate exploit scenarios that sound plausible but fall apart when you consider actual deployment environments. "An attacker could exploit this by..." followed by a scenario that requires conditions that don't exist in real use.
Repetitive Phrasing: This is a subtle one, but AI-generated text often has a certain rhythm or repeated sentence structures. If you're reading multiple reports from the same submitter and they all "feel" the same despite being about different issues, that's a red flag.
Lack of Follow-up Understanding: When you ask clarifying questions, AI-assisted submitters often struggle to explain their findings in depth. They might parrot back what the tool told them without being able to discuss alternatives, variations, or edge cases.
My advice? Create a template for submissions that requires specific information about the actual impact, reproduction steps, and environment details. This won't stop determined slop-submitters, but it will slow them down. And honestly, if someone can't spend five minutes filling out a proper template, their report probably isn't worth your time anyway.
What Curl's Decision Means for Other Open Source Projects
Curl isn't the first project to struggle with bug bounty challenges, but it might be the most prominent to take this particular step. Other projects are watching closely, and I've already heard from maintainers who are considering similar moves.
The Apache Foundation recently tightened their submission requirements after seeing an increase in low-quality reports. The Python Security Response Team has implemented additional verification layers. Even commercial companies with paid security teams are feeling the pressure—I know of one major cloud provider that's had to triple their triage team just to handle the influx.
But here's the worrying part: smaller projects might not have curl's visibility or resources. If they get flooded with AI slop, they might just abandon security response altogether. Or worse, they might get so overwhelmed that they miss actual critical vulnerabilities. We're creating a situation where the most important but least glamorous software—the libraries and frameworks everything else depends on—becomes harder to secure.
Some projects are experimenting with AI filters of their own. They're training models to recognize likely AI-generated reports based on writing style, structure, and content patterns. It's an arms race: AI generating reports, AI detecting those reports, AI generating reports that evade detection... you see where this is going.
How to Responsibly Use AI in Security Research (If You Must)
Look, I'm not saying you should never use AI tools in security research. I use them myself. But there's a right way and a wrong way. If you're going to use these tools, here's how to do it responsibly:
Treat AI as an Assistant, Not an Expert: Use AI to help you understand complex code, generate test cases, or explore potential attack vectors. But you—the human—need to verify everything. Run the actual tests. Trace through the actual code. Understand the actual impact.
Context Is Everything: Before submitting anything, make sure you understand how the code you're looking at fits into the larger system. Read the documentation. Check the issue tracker. Look at recent commits. An AI can't do this contextual research for you.
Be Honest About Your Process: If you used AI tools in your research, mention it in your report. Explain what the tool found and what you verified independently. Maintainers appreciate transparency. They're much more likely to engage with someone who says "My AI tool flagged this pattern, and after manual review I confirmed..." than someone pretending they found everything through pure manual analysis.
Focus on Quality Over Quantity: Don't run tools against 100 projects and submit everything that comes up. Pick a project you care about, learn its codebase, and do thorough research. One well-researched, verified report is worth a thousand AI-generated false positives.
And if you're just starting out in security research? Consider contributing to projects in other ways first. Fix bugs. Write documentation. Help with testing. Build trust and understanding before you start looking for vulnerabilities. You'll be a better researcher for it, and the maintainers will be more likely to take your security reports seriously.
The Future of Bug Bounties in the Age of AI
Where do we go from here? Bug bounty programs aren't going away—they're too valuable for finding security issues. But they need to evolve to handle the new reality of AI-assisted (and AI-generated) research.
I expect we'll see more programs implementing submission fees or requiring reputation scores. HackerOne's reputation system already helps, but it needs to be more aggressive about penalizing low-quality submissions. We might see programs requiring video proof-of-concepts or live demonstrations to filter out automated reports.
Platforms could implement AI detection at submission time, warning submitters if their report appears AI-generated and requiring additional verification. Or they could rate-limit submissions from new researchers to prevent mass automated reporting.
Some projects are moving toward curated bug bounty programs where researchers must apply and be approved before submitting reports. This creates more work upfront but reduces noise significantly. Others are partnering with security firms that handle initial triage and verification before reports reach maintainers.
The truth is, we're in a transitional period. The tools have changed faster than the processes around them. Curl's decision is a wake-up call, not just for maintainers but for the entire security research community. We need to establish new norms, new best practices, and new systems that can handle both the promise and the peril of AI in security.
Your Role in This Ecosystem
If you're a user of open-source software (and you are, whether you know it or not), this affects you too. Here's what you can do:
Support Important Projects: If you use curl or any other critical open-source tool in your business, consider contributing financially. Even small regular donations help maintainers dedicate time to security work. Curl has a sponsorship program—use it.
Be Patient with Security Updates: When maintainers are overwhelmed with AI slop, real security fixes might take longer. Don't complain about slow updates—understand the context and appreciate the work that goes into proper security maintenance.
Educate Your Teams: If you work in development or security, make sure your team understands the challenges maintainers face. Don't contribute to the problem by running automated tools against projects and submitting unverified reports.
Advocate for Better Tools: Support the development of AI tools that emphasize verification and context. The next generation of security AI needs to be about quality, not just quantity.
Curl's bug bounty program might be gone, but the need for security research hasn't disappeared. If anything, it's more important than ever. The question is whether we can build systems and norms that harness AI's potential without drowning in its output. That's the challenge facing every open-source project in 2026 and beyond.
As for curl itself? The project continues. The code gets better. Security work happens through other channels. But a valuable mechanism for finding and fixing vulnerabilities is gone, and we're all a bit less secure for it. The real tragedy isn't that curl ended its bounty program—it's that we created conditions where that seemed like the best option.
