1. **Analyze the Request:** * **Role:** News summarization expert. * **Task:** Summarize the provided article text (which appears to be just a title in the prompt, but implies the context of the discussion around AI-generated content or "slop" in open source). * **Constraint 1:** Concise, 2-4 sentences. * **Constraint 2:** Focus on most important information. * **Constraint 3:** DO NOT copy/paste. * **Constraint 4:** DO NOT simply restate the title. * **Constraint 5:** Add new information beyond the title (This is tricky because there is no article text provided, only the title and source "hackernews". I must infer the likely content based on the title "AI-SLOP: Develop Best Current Practises for Open Source Maintainers"). * **Language:** English. 2. **Analyze the Input Data:** * **Title:** "AI-SLOP: Develop Best Current Practises for Open Source Maintainers" * **Source:** hackernews * **Article Text:** [Missing/Empty] The prompt says "Article Text:" followed by the title again. This suggests I need to simulate the summary based on the *topic* implied by the title, or I need to point out that the text is missing. However, usually in these prompts, if the text is missing or repeated, it implies the user wants a summary *of the topic* based on the title provided, treating the title as the subject matter. Or, it's a trick. * *Correction:* Looking closely at the prompt: "Article Text: AI-SLOP: Develop Best Current Practises for Open Source Maintainers". It seems the user might have pasted the title twice, or the article content is actually just that headline. But
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AI-SLOP: Develop best current practises for Open Source maintainers #178 Description Open source projects are increasingly facing a wave of low-quality, AI-generated vulnerability reports and contributions—commonly referred to as "AI-slop." This issue aims to develop best current practices for open source maintainers to help them detect, manage, and mitigate the impact of AI-slop on their projects while still benefiting from legitimate AI-assisted security research. Problem Statement The rise of AI tools has created a significant challenge for open source maintainers: - Volume & Quality Issues: Projects are receiving high volumes of low-quality vulnerability reports that appear to be generated by AI with minimal or no human review, creating a "DDoS-like situation" for maintainers. - Maintainer Burden: Validating these reports can take significant time, consuming valuable volunteer time and resources. Halfway through 2025, curl reported that only ~5% of bug bounty submissions were genuine vulnerabilities, with around 20% appearing to be AI-generated slop. - Bug Bounty Impact: Some projects have been forced to discontinue bug bounty programs entirely (e.g., curl ended their bug bounty program in January 2026), while others like Node.js have implemented stricter signal requirements on HackerOne. - Detection Difficulty: There is no reliable technical indicator for AI-generated content: detection is often based on "vibes" and maintainer intuition. - Burnout & Mental Health: The constant stream of low-quality reports contributes to stress, frustration, and burnout - especially for unpaid volunteer maintainers. Node.js mentioned receiving over 30 AI-slop reports during major holidays for the maintainers as a key reason for raising their H1 signal requirements. - Social Pressure: Maintainers who reject AI-slop reports may face personal attacks and pushback. Goals - Document the Problem: Collect and (where possible) anonymize data on the scope and impact of AI-slop across the open source ecosystem. - Develop Detection Guidance: Provide recommendations on identifying potential AI-generated submissions, acknowledging that detection is imperfect. - Create Policy Templates: Develop example AI contribution policies that projects can adapt, inspired by existing efforts. - Best Practices for Maintainers: Provide actionable guidance maintainers can reference to reduce personal attacks and provide consistent responses. - Balance Good vs. Bad AI Use: Acknowledge that AI tools can find valid vulnerabilities. The goal is to reduce slop, not ban AI entirely. Key Themes from Existing Public Discussions What Projects Are Doing | Approach | Examples | |---|---| | Ending bug bounties | curl/curl#20312 | | Requiring higher HackerOne signal | Node.js announcement | | AI contribution policies | LLVM, Selenium#17043, Django | | Requiring PoC videos | Various projects | | Banning repeat offenders | Under discussion | | Cataloging slop examples | curl AI slop gist | Policy Elements from Existing Projects Key principles emerging from LLVM, Selenium, and Django policies: - Human-in-the-loop accountability: A human must review, understand, and be able to explain all AI-generated content - Disclosure requirements: Substantial AI assistance should be disclosed (tool used, what was generated) - No autonomous agents: AI tools should not autonomously open PRs or push commits - Quality bar unchanged: AI-assisted contributions must still meet the same standards - Contributor remains responsible: Copyright and quality responsibility remains with the human contributor - "Good first issues" protection: AI tools should not be used for issues meant to help humans learn the project Recommendations for Platforms Platforms accepting vulnerability reports should consider: - Implement systems to prevent automated or abusive reporting (CAPTCHAs, rate limits, etc.) - Allow for public visibility of reports without labeling them as vulnerabilities - Enable community feedback mechanisms for low-quality reporters - Remove credit for abusive reporters - Strongly encourage that only thoroughly reviewed, human-verified reports be submitted - What else? Open Questions - How do we survey the community on AI-slop impact? - What tools can help flag probability of AI-generated content? - How can we make project documentation "LLM-friendly" to reduce false positives (e.g., explicit threat models, scope definitions)? - How do we help security researchers who find valid bugs but may not be qualified to create patches? - How do we distinguish "yesterday's problem" (current slop) from "tomorrow's problem" (increasing AI coding assistance)? Related Efforts - OpenSSF AI/ML Working Group: Has AI security on their roadmap—potential collaboration opportunity - DARPA/ARPAH AI Hacking Competition: Tools being donated could help both researchers create better reports and projects analyze submissions - Cyber Reasoning SIG: Working on leveraging DARPA tooling for finding vulnerabilities