AI-assisted vulnerability research has exploded in recent months, unleashing a firehose of low-quality security reports on overworked software maintainers. Instead of focusing on fixing real flaws, developers are now spending hours sifting through noise—duplicate findings, theoretical attack scenarios without proof, and submissions padded with AI-generated jargon. The crisis has drawn sharp criticism from industry leaders and sparked urgent changes in how bug bounty programs and open source projects handle submissions.
Linus Torvalds, the creator of the Linux kernel, voiced his frustration in a note accompanying the latest kernel release candidate. He stated that the project's security mailing list has become "almost entirely unmanageable, with enormous duplication due to different people finding the same things with the same tools." His comment highlights a core issue: AI tools, often trained on the same datasets, tend to identify identical vulnerabilities, leading to redundant reports from multiple researchers. Torvalds urged those using AI to go beyond automated scanning: "If you found a bug using AI tools, the chances are somebody else found it too. If you actually want to add value, read the documentation, create a patch too, and add some real value on top of what the AI did. Don't be the drive-by 'send a random report with no real understanding' kind of person."
The problem is not limited to the Linux kernel. Jarom Brown, Senior Product Security Engineer at GitHub, acknowledged last week that while AI lowering the barrier to entry for security research is a welcome development, his team is being inundated by submissions that fail to demonstrate any real security impact. These include reports without a proof of concept, theoretical attack scenarios that don't hold up under scrutiny, and findings already covered by GitHub's published ineligible list. GitHub is not alone—many programs across the industry are grappling with the same challenge, and some have shut down entirely. To counter the tide, GitHub now requires submitters to validate AI-assisted findings before sending them in. A complete submission must include a working proof of concept demonstrating exploitation potential and concrete security impact. Additionally, reports covering known ineligible categories will be closed as Not Applicable, which may affect the submitter's HackerOne Signal and reputation. Brown also urged researchers to be concise, noting that bloated, AI-padded reports slow down triage and waste everyone's time.
The collateral damage extends beyond the programs themselves. Shubham Shah, co-founder of Assetnote and a respected security researcher, says organizations are now taking far longer to review legitimate reports and act on real flaws. This slowdown is killing the feedback loop that keeps top researchers engaged. While bug bounty platforms like HackerOne and Bugcrowd are trying to fight the onslaught of AI-created spam reports with AI and added controls, Shah notes that "the joy of reporting vulnerabilities to bug bounties is quickly dissipating"—not just for him. He added: "Hopefully the platforms actually work this out, but until then, I can't see myself continuing to report high quality original research to certain programs where I have meaningfully contributed for a decade when they fail to understand the difference between myself and a researcher that doesn't have any credibility." In the near term, some experienced researchers may retreat to private vulnerability research and invite-only bounties, further reducing the talent pool available for public bug bounty programs.
The AI-powered "industrialization" of vulnerability discovery is currently a much bigger problem for open source projects than for large organizations like Microsoft or Google. Open source projects rely on volunteer maintainers, whose number and time is limited. Those limitations have led some projects to drastic measures. For example, the cURL project, led by Daniel Stenberg, initially stopped accepting HackerOne submissions and eliminated monetary rewards for security reports. Stenberg hoped that removing the financial incentive would reduce the amount of AI slop, believing that "the best and our most valued security reporters still will tell us when they find security vulnerabilities." The project switched to welcoming reports via GitHub or email, but a month later reverted to using HackerOne because those two avenues proved less effective for reporting vulnerabilities. However, the project stuck with its decision not to offer bounties for bug reports. According to Stenberg in April, the nature of submissions changed dramatically after that. The slop situation is no longer a problem. The number of reports rose, their quality was higher (even if they were compiled with the help of AI), and the rate of confirmed vulnerabilities surpassed the 2024 pre-AI level. Yet Stenberg warns that the raised influx of "good" vulnerability reports will present a different problem for open source projects. "This avalanche is going to make maintainer overload even worse. Some projects will have a hard time to handle this kind of backlog expansion without any added maintainers to help," he pointed out.
In the wake of cURL's departure and return, HackerOne acknowledged the problem AI slop may represent for under-resourced organizations. Michiel Prins, Co-founder & Senior Director of Product Management at HackerOne, advised customers to refine the scope and submission guidelines to reduce noise, use AI-assisted triage tools, and pair that automation with human oversight. "As AI makes it easier to automate submissions, preserving signal quality becomes critical so open source maintainers can stay focused on fixing real issues," Prins said. "Our focus is helping programs manage that shift with workflows that filter noise early, surface credible reports, and keep vulnerability management sustainable, so open source communities can maintain the transparency and resilience they're known for."
The Open Source Security Foundation's Vulnerability Disclosures Working Group is also seeking community feedback as it works to help open source maintainers tackle AI-generated junk reports. Its goals include compiling best practices, creating policy templates, and developing guidance to help maintainers spot and handle AI-assisted submissions. Meanwhile, the broader bug bounty ecosystem is adjusting. Crowd-sourced security platforms are investing in machine learning models to detect low-quality submissions, but the arms race between spammers and filters continues.
The rise of AI in security research is a double-edged sword. On one hand, it democratizes vulnerability discovery, enabling more people to contribute to software security. On the other, it creates a flood of noise that threatens to drown out valuable work. The Linux kernel, GitHub, cURL, and many other projects are now at a crossroads. They must find ways to leverage AI without being overwhelmed by it. The challenge is especially acute for small, volunteer-run open source projects that lack the resources to triage hundreds of duplicate reports. As AI tools become more sophisticated, the volume of submissions is likely to increase further. Without systemic changes—such as standardized reporting formats, mandatory proof-of-concept validation, and better triage automation—the burden on maintainers may become unsustainable. The community is still searching for the right balance, but one thing is clear: the era of AI-driven vulnerability research is here, and the software industry must adapt quickly to avoid drowning in its own noise.
Source: Help Net Security News