Full Report
Seth Larson is a security report triager for CPython, pip and many other open source projects mainly in the Python ecosystem. Recently, they got a large uptick in the amount of bad reports. These were either LLM-halluncinated, low quality and overall spammy. What's so hard is that many of these look legitimate at first glance! Responding to security reports is an expensive operation for projects. It requires a lot of time to understand an issue and to see if it's relevant or not. This leads to confusion, stress, and frustration. The author then goes through what programs, reporters and maintainers can do. For platforms, like HackerOne, it comes down to incentives. Being able to "name and shame" repeat offenders and banning folks with too many false positives. Additionally, removing public recognition can be helpful. They mention preventing new users from reporting issues but I disagree with this approach since some people just publish on the platform where they happen to find a bug at. The list for reporters is a lot longer. Effectively, it comes to stop being an idiot and make sure it's a real bug before reporting. The only thing of note is coming with a patch alongside a security issue. For maintainers, the author talks about putting the same amount of effort that the reporter put in. If you receive a report that is spam or AI generated, then give zero effort. If it's garbage then they won't respond. If it's real then admit your mistake and move on. When trying to audit whether it's going to be low-quality or not, look for a few things: If the account has no public identity, no public reports of value or multiple invalid/bad reports, then it's likely spam. Is the vulnerability in the code usage itself or does it even include a PoC? Most people are acting in good faith :) Some people are just new. Overall, a good post on responding to bug bounty programs.
Analysis Summary
# Best Practices: Managing Vulnerability Report "Slop"
## Overview
These practices address the rising trend of low-quality, AI-generated, or automated security reports (often termed "slop") that plague open source projects. These practices aim to reduce maintainer burnout, preserve project resources, and ensure that legitimate security vulnerabilities receive the attention they require without being buried by "spammy" submissions.
## Key Recommendations
### Immediate Actions
1. **Adopt a "Mirror Effort" Policy:** Respond to reports with the same level of effort provided by the reporter. Give zero or minimal effort to obvious AI-generated or "trash" reports.
2. **Verify PoCs First:** Before triaging the code, check if the vulnerability exists in the *project* or merely in the reporter's *usage* of the project (Proof of Concept).
3. **Check Reporter History:** Evaluate the reporter’s credibility (account age, public identity, and past history of valid vs. invalid reports) before deep-diving into the technical details.
### Short-term Improvements (1-3 months)
1. **Updated Reporting Guidelines:** Update `SECURITY.md` or platform templates to explicitly ban unverified AI-generated reports and require human-reviewed Proof of Concepts (PoCs).
2. **Implement Rate Limiting:** If using a custom platform or GitHub, utilize CAPTCHAs or rate-limiting features for security report creation to prevent automated tool-dumping.
3. **Encourage Patch-Inclusive Reports:** Prioritize and incentivize reports that include a proposed patch or fix along with the vulnerability description.
### Long-term Strategy (3+ months)
1. **Platform Incentive Re-alignment:** Lobby bug-bounty platforms (e.g., HackerOne, GitHub) to remove public "credits" or gamification metrics that reward quantity over quality.
2. **Community Defense Networks:** Establish a network of trusted peers to "tag in" for secondary reviews when a report’s legitimacy is questionable.
3. **Transparency and "Naming/Shaming":** Develop a process to make low-quality or fraudulent reports public (without publishing sensitive vulnerability data) to disincentivize repeat offenders.
## Implementation Guidance
### For Small Organizations (Open Source Maintainers)
- **Do not feel obligated to respond:** If a report is clearly automated or hallucinatory, ignore it or close it immediately without a detailed rebuttal.
- **Seek External Triage:** Lean on ecosystem-specific security experts (like the CPython triage team for Python projects) for high-stakes decisions.
### For Medium Organizations
- **Standardize Triage Workflows:** Create a checklist for triagers that includes checking for "AI hallmarks" (e.g., confident but incorrect explanations, tool-dumping without context).
- **Public Disclosures:** Use the "publish without vulnerability record" feature on platforms to flag low-quality reporters.
### For Large Enterprises (and Platforms)
- **Identity Verification:** Implement stricter hurdles for newly registered accounts attempting to report security issues.
- **Reputation Management:** Implement a system to "strip" credits or public status from users who consistently submit false positives.
## Configuration Examples
*While specific code snippets were not the focus, the following configuration "logic" applies:*
**Reporter Credibility Audit Checklist:**
text
[ ] Account Age > 30 days
[ ] Linked to a public identity (GitHub profile, Website, etc.)
[ ] Previous track record of "Credited" reports
[ ] Documentation includes a custom PoC (not just tool output)
[ ] Reporter has attempted to explain the BUSINESS logic impact
## Compliance Alignment
- **NIST SP 800-53 (RA-5):** Vulnerability monitoring and scanning (ensuring quality of incoming data).
- **ISO/IEC 29147:** Vulnerability disclosure (adopting guidelines for receiving and processing reports).
- **CVD (Coordinated Vulnerability Disclosure):** Aligning reporter behavior with established ethical norms.
## Common Pitfalls to Avoid
- **Falling for "AI Professionalism":** LLM reports often look legitimate at first glance because they use professional formatting; always verify the technical claims first.
- **Sunk Cost Fallacy:** Spending hours refuting a report that took the reporter 5 seconds to generate.
- **Isolated Triage:** Attempting to handle a flood of reports alone, leading to maintainer burnout.
## Resources
- **Seth Larson’s Blog:** hxxps[://]sethmlarson[.]dev/
- **Alpha-Omega Project:** hxxps[://]alpha-omega[.]dev/
- **Google/OpenSSF Triage Guidelines:** (Referenced via ecosystem context)
- **Daniel Stenberg (curl) on LLMs:** hxxps[://]daniel[.]haxx[.]se/blog/2024/01/02/the-i-in-llm-stands-for-intelligence/