Faced with the growing volume of submission to its bug bounty program, GitHub is replacing cash bounties with swag rewards for reports with low security impact — and asking researchers to stop submitting reports that are low quality or about things that aren’t its fault.
The cloud-based code repository platform has seen a sharp increase in submissions that don’t demonstrate real security impact over the past year due to newer tools such as generative AI.
“Not every valid submission represents a meaningful security risk. Some reports identify hardening opportunities or documentation gaps,” Jarom Brown, a senior security researcher at GitHub, wrote in a blog post.
On top of that, he said, many of the reports GitHub receives describe out-of-scope scenarios in which someone experiences an “undesirable” outcome after interacting with malicious content in GitHub.
“These reports are often well-written and technically accurate in their observations, but they misunderstand where the security boundary lies. When an ‘attack’ requires the victim to actively seek out and engage with attacker-controlled content (cloning a malicious repo, asking an AI tool to analyze untrusted code, opening a crafted file), the security boundary is the user’s decision to trust that content. These scenarios generally don’t represent a bypass of GitHub’s security controls,” he wrote.
Brown’s explanation also serves as a reminder to GitHub users of what the company expects them to do to protect themselves.
Although artificial intelligence has swollen the flood of bug reports, GitHub doesn’t want security researchers to stop using it. “We have no problem with researchers using AI tools. AI is a force multiplier, and we expect it to play an increasing role in security research. We use AI across our own internal security programs, and we’re seeing the best external researchers do the same. We welcome it,” Brown wrote.
But all AI-generated submissions must be reviewed and validated by a human first —a rule that has applied to the use of any tool to help with bug hunting.
In this way, GitHub hopes to screen out reports without a proof of concept, theoretical attack scenarios that don’t hold up under scrutiny, and others covered by its published list of those ineligible for rewards.
AI-generated noise is an industry problem
GitHub isn’t the only bug bounty provider struggling with the volume of submissions — although not all are as welcoming of AI.
Security vendors, open-source maintainers, and bug bounty platforms across the industry, analysts warned, have increasingly complained about a flood of low-quality, AI-assisted vulnerability reports that consume analyst time, slow incident response, and make it harder to identify legitimate threats amid growing volumes of automated noise.
Open-source project Curl has eliminated its bug bounty due to AI slop, and HackerOne paused payouts form its Internet Bug Bounty program because it couldn’t keep up with AI submissions. The Google Open-Source Software Vulnerability Reward Program is also restricting payouts.
And Linux creator Linus Torvalds recently warned that a “continued flood” of AI-generated vulnerability reports had made the Linux kernel security mailing list “almost entirely unmanageable” because of massive duplication from researchers using the same AI tools to find identical bugs.
Cutting off the security talent pipeline
Pareekh Jain, principal analyst at Pareekh Consulting, said GitHub’s switch from cash payouts to swag could reduce participation from new and independent researchers, many of whom rely on rewards from smaller findings to build credibility, sharpen their skills, and sustain their work financially.
That decline in participation at the lower end of the ecosystem could have longer-term consequences for the cybersecurity talent pipeline if fewer newcomers see bug bounty hunting as a viable path to learn, contribute, and grow within the security community, said Akshat Tyagi, associate practice leader at HFS Research.
On the flip side though, Tyagi pointed out that the move could be positive for experienced researchers: “Less queue noise means faster triage, faster payouts, and more program credibility.”
An open door, but not for everyone
Greyhound Research chief analyst Sanchit Vir Gogia expects platforms such as GitHub to respond to the AI deluge by introducing more explicit trust controls to contribution workflows.
“Some will be visible: permissions, rate limits, templates, identity verification, reputation scoring. Others will be less visible: ranking systems, automated pre-triage, AI-origin signals, behavioral scoring, and quiet prioritization of known-good contributors,” he said.
And Jain suggested GitHub could apply its recently introduced Stacked PRs code review tool to its bug bounty program. “Just like stacked PRs help developers review AI-generated code in smaller and more structured chunks, bug bounty platforms may introduce more structured vulnerability submissions with automated validation, reproducible exploit steps, deduplication, and AI-assisted triage,” he said. “Security reporting could start looking more like a CI/CD workflow instead of long text-based reports.”