Recently, curl decided to shut down their bug bounty program due to an overwhelming influx of low-quality vulnerability reports generated by AI. This seems reminiscent of alert fatigue, where the sheer volume of submissions overwhelms human reviewers. With AI making it so easy to generate noise, it puts a strain on the people who need to sort through them, causing legitimate reports to get lost in the shuffle. How are you handling similar situations in your organization? Are there any effective filters or gates you've found useful in managing security or operations intake?
7 Answers
To mitigate this issue, they could set better criteria, such as requiring contributors to have a history of involvement with the curl codebase or demonstrating substantial experience. That could help reduce the amount of garbage submissions significantly.
It’s strange they didn’t just create an agent to filter out that slop. But I guess once people figure out there’s a bot, they’d just adapt and make the submissions avoid detection, leading to even bigger issues for the reviewers.
Exactly! We’re caught in a loop where adding more technology to resolve issues just complicates the results even further.
It’s amusing how this situation highlights a bizarre use of AI to make the problem worse. Curl ended up needing to implement something akin to a captcha for vulnerability submissions! Many organizations just accept that the majority of their intake will be junk and hire someone specifically to go through and delete the unnecessary reports—all while pretending it’s more than just security theater.
There are a few key factors that lead to low-quality AI outputs. For instance, people without proper knowledge can create misleading reports, and then there are context and compute limitations that can muddy the issue too. While the latter can be improved, the former is trickier. It’s similar to how Photoshop made it easier to fake images—now anyone can produce something that looks legit without understanding the tech behind it. The message is clear: the effectiveness of these tools depends heavily on the user’s knowledge. "Tools are only as smart as the people using them," just like my great grandfather used to say.
True, but I wonder how much of it is people trying to make a quick buck off these bounties. The internet makes it so easy to exploit such systems, just like scammers do with phone calls.
I see your point, but even knowledgeable folks can automate bug discovery, which complicates things. It’s not just about intelligence; it’s about how AI doesn’t think like humans.
I actually touched on this issue in my podcast recently. The discussion was all about the noise that bad submissions create and how it turns humans into the bottleneck in processing those reports. If anyone's interested, here’s the link to the episode!
AI should enhance skills, not replace human judgment. But when these queues are flooding with spam, that's a big problem. It’s like turning every intake process into a never-ending stream of nonsense.
Lol, thanks for the insightful tips, but I'm actually just trying to find a blueberry muffin recipe.
Absolutely! It’s like we need to cool it down and manage the flow.
It's really hard to filter out all the noise these days. In my experience with high-volume back-end systems, when it’s free to submit, you get bombarded with submissions. We tried using reputation scores for internal tools, but with public bounties, a 'proof of work' requirement is almost necessary to keep the spam in check. Otherwise, it just leads to burnout among those reviewing the reports!
Yeah, and the solution seems to just create more AI to fix the AI problems we have. It feels like humans are drifting towards becoming too reliant on machines.