What Are AI Slop Bug Bounty Reports?
AI slop bug bounty reports are vulnerability submissions that appear legitimate but contain false or fabricated information, often generated by large language models like ChatGPT. These reports are typically well-written and formatted, using technical terms and plausible reasoning, but they describe issues that do not actually exist.
They often include imaginary function names, incorrect memory references, or misinterpretations of how a system works. Reviewers may spend significant time trying to reproduce a bug, only to realize it was made up entirely.
Why AI Slop Reports Are a Growing Concern
Generative AI tools make it easy to produce content that looks technically sound. As a result, individuals seeking rewards from bug bounty programs can flood projects with reports that sound credible but lack substance.
These AI-generated reports create several challenges:
- They increase the workload on developers and maintainers.
- They delay the triage of legitimate vulnerabilities.
- They reduce trust in bounty platforms and user-submitted reports.
Some projects are now receiving more AI slop than authentic submissions, making it difficult to maintain efficient and secure workflows.
The cURL Case Study: A Real-World Impact
The open source project cURL offers one of the clearest examples of how AI slop bug bounty reports affect teams. Daniel Stenberg, its lead developer, reports that about 20 percent of incoming bug bounty reports are now AI-generated and mostly invalid.
Key data:
- cURL has awarded over $90,000 for 81 valid reports since 2019.
- Nearly all recent AI-generated reports were false.
- Each invalid report can take one to three hours to review.
In one example, a report claimed an HTTP/3 vulnerability but included fake function calls and behaviors that did not exist in the codebase. The time wasted on such reports is significant, especially for volunteer-led projects.
How Bug Bounty Platforms and Projects Are Reacting
Bug bounty platforms such as HackerOne are starting to adapt. They now recognize the issue of AI-generated submissions and are developing filters and policies to address the problem.
Changes in progress:
- Requiring AI use disclosure in report submissions.
- Building triage tools to identify likely AI-generated content.
- Flagging suspicious or repetitive submission patterns.
Open source projects like Django have also updated their vulnerability policies to explicitly reject hallucinated reports and require human-verified details for any submission to be accepted.
Risks for Developers and the Security Ecosystem
AI slop bug bounty reports create a number of serious issues for the software development and security communities:
Time loss: Reviewing a fake report can be as time-consuming as reviewing a real one.
Loss of trust: Repeated false alarms reduce the credibility of bounty programs.
Missed vulnerabilities: High noise levels can bury real, dangerous issues.
Volunteer burnout: Open source maintainers often work in their free time and are now spending much of that time debunking fake bugs.
This trend harms not only individual projects but also the broader cybersecurity ecosystem.
Best Practices for Bug Bounty Management
To reduce the impact of AI slop bug bounty reports, maintainers and bounty programs can implement several protective strategies:
Set clear policies: Require submitters to disclose whether AI was used in writing the report.
Use filters: Develop AI-assisted triage tools to detect low-quality or repetitive submissions.
Apply reputation thresholds: Prioritize reports from trusted or verified users.
Discourage abuse: Consider banning users who repeatedly submit hallucinated reports without proper disclosure.
Introduce friction: Some suggest adding small submission fees or requiring proof-of-concept steps before full triage.
These methods can help protect teams while preserving legitimate research contributions.
How to Use AI Responsibly in Security Research
AI can support security researchers, but it must be used responsibly. Here’s how to avoid contributing to AI slop:
- Always verify: Don’t submit claims unless you’ve personally tested and validated the vulnerability.
- Disclose AI assistance: Transparency helps reviewers understand the context.
- Provide proof: Include reproduction steps, logs, and source code snippets to show your work.
- Use AI to assist, not fabricate: AI can help you draft clear write-ups, but the findings should be your own.
Bug bounty programs reward genuine discoveries, not synthetic fiction. Being transparent and ethical builds your credibility and contributes positively to security.
Conclusion: Keeping Bug Bounties Effective in the AI Era
AI slop bug bounty reports are one of the first examples of AI-generated noise disrupting open source development. Projects like cURL and Django have taken the lead in defining how to respond to these challenges by setting policies and educating the community.
Platforms like HackerOne are also adapting by building smarter triage systems and requiring AI usage disclosures. As AI tools continue to evolve, the security community must stay focused on separating valuable insights from artificial noise.
When used responsibly, AI can support ethical hacking and software security. But to preserve the integrity of bug bounty programs, developers and researchers must work together to filter out the slop.
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