AI-generated vulnerability reports are overwhelming security researchers and maintainers. The rise of AI slop—plausible-sounding but fake reports—has flooded platforms like HackerOne and Bugcrowd with noise. Instead of finding real flaws, security teams now spend hours triaging hallucinations.
These aren't spammy one-liners. They're polished, detailed, and often come with fabricated function names, fictional proof-of-concepts, and AI-written patches. Many pass the initial credibility check—until triagers realize they reference code that doesn't exist.
Projects like curl have documented multiple cases where maintainers wasted hours investigating what turned out to be AI fiction.
What makes AI slop dangerous isn’t quantity—it’s plausibility. These reports mimic human logic and structure, often referencing real software components mixed with invented issues. This blurs the line between valid bugs and deceptive noise.
Unlike earlier spam that was easily filtered, these reports undermine trust in the bounty system itself.
Platforms are starting to fight back with AI-powered filtering systems. HackerOne, for example, is testing Hai Triage—a semi-automated system that flags duplications and low-effort reports before they reach human analysts.
But trust in these tools remains shaky. Critics argue that automated moderation could dismiss novel vulnerabilities or reduce rewards for legitimate researchers.
Daniel Stenberg, maintainer of curl, publicly criticized the volume of “imaginary vulnerabilities” that reference non-existent code.
Small open-source maintainers without corporate backing are hit hardest, as they lack the resources to review slop at scale.
To fight back, platforms and security teams are redefining what counts as useful:
1. Require identity and reputation scores
Tie bounty submissions to verified researcher identities to reduce anonymous spam.
2. Deploy LLM hallucination checks
Use GPT detectors and token path validators to flag likely AI-generated artifacts.
3. Train on fake bug data
Intentionally seed platforms with fake bugs to monitor triage performance and tune filters.
4. Reward “proof + fix” combos
Offer bonus payouts for well-documented, reproducible, and responsibly disclosed vulnerabilities.
AI slop doesn’t just slow teams down—it erodes confidence in crowdsourced security. Genuine researchers are already walking away after seeing their high-quality reports get ignored or buried under AI-generated debris.
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If left unchecked, bug bounty programs could become unusable, defeating their core purpose: empowering white-hat hackers to strengthen public software.
| Challenge | Actionable Fix |
| Flood of hallucinated reports | Introduce trusted IDs + AI detection tools |
| Maintainer overload | Use hybrid triage + submission scoring |
| Eroding trust in bounty platforms | Restructure incentives around quality |
| Confused boundaries of reality | Build databases of real vs. fake functions |
What is AI slop in cybersecurity?
It refers to fake vulnerability reports generated by AI that appear legitimate but contain made-up functions or bugs.
Why is AI slop dangerous?
It wastes time, overwhelms real researchers, and erodes trust in bug bounty systems.
Can bug bounty platforms detect fake AI reports?
Some like HackerOne are trying AI-assisted triage tools, but results are mixed so far.
How can we stop AI slop in bug bounties?
Better identity systems, LLM detectors, triage calibration, and reward restructuring can help.
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