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Project ideas from Hacker News discussions.

cURL removes bug bounties

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

3 Most Prevalent Themes

  1. AI-Generated "Slop" Overwhelming Security Programs The primary driver of the discussion is the flood of low-quality, AI-generated vulnerability reports. Users express frustration that these reports are often nonsensical, misinformed, and waste valuable maintainer time. Many commenters believe this is a deliberate tactic to exploit bug bounty systems.

    • mirekrusin: "Some (most?) are llm chat copy paste addressing non existing users in conversations like [0] - what a waste of time."
    • golem14: "I looked at two reports, and I can’t tell if the reports are directly from an ai or some very junior student not really understanding security. LLms to me sound generally more convincing."
    • plastic041: "In the second report, Daniel greeted the slopper very kindly and tried to start a conversation with them. But the slopper calls him by the completely wrong name. ... It must have been extremely tiring."

  2. Proposed Solutions: Entry Fees and Financial Disincentives A prominent theme is the suggestion to combat slop by introducing a financial cost for submitting bug reports. The idea is that a small entry fee, reimbursed only for valid reports, would deter bad-faith actors. This sparked debate about the practicality and fairness of such a system.

    • dlcarrier: "An entry fee that is reimbursed if the bug turns out to matter would stop this, real quick."
    • fredrikholm: "When Notion first came out, it was snappy and easy to use. Creating a page being essentially free of effort, you very quickly had thousands of them, mostly useless. Confluence ... is offlessly slow. The thought of adding a page is sufficiently demoralizing that it's easier to update an existing page ... Consequently, there's some ~20 pages even in large companies." (Using an analogy to argue that "trivial inconveniences" curb low-effort contributions).
    • bawolff: "Bug bounties often involve a lot of risk for submitters. ... A pay to enter would increase that risk."

  3. Debate Over LLMs' Role in Both Creating and Solving the Problem Users are grappling with the paradoxical role of AI. While LLMs are the source of the spam, there is a parallel discussion about whether they could also be used to filter it. This led to arguments about the reliability of AI for judgment tasks and the risk of AI being used to generate increasingly deceptive spam.

    • colechristensen: "I prompted Opus 4.5 'Tell me the reasons why this report is stupid' on one of the example slop reports and it returned a list of pretty good answers. ... If you give them the right structure I've found LLMs to be much better at judging things than creating them."
    • imiric: "Trusting the output of an LLM to determine the veracity of a piece of text is a baffilingly bad idea."
    • f311a: "How would it work if LLMs provide incorrect reports in the first place? ... The problem is the complete stupidity of people. They use LLMs to convince the author of the curl that he is not correct about saying that the report is hallucinated."

🚀 Project Ideas

[AI Slop Detector for Bug Bounty Platforms]

Summary

  • A browser extension or API integration that scans incoming bug bounty reports for AI-generated "slop" characteristics before they reach human triagers.
  • The core value proposition is to save maintainers' time by automatically flagging low-effort, likely fraudulent reports, allowing them to focus on genuine vulnerabilities.

Details

Key Value
Target Audience Security teams managing bug bounty programs (e.g., at companies like cURL, or platforms like HackerOne/Intigriti).
Core Feature Multi-factor analysis of reports: linguistic patterns, structural inconsistencies, lack of reproducible evidence, and cross-referencing with known hallucination vectors.
Tech Stack Python (FastAPI), LLMs for classification (or lightweight ML models), browser extension (JS/Chrome API), database for report fingerprinting.
Difficulty Medium
Monetization Revenue-ready: Freemium SaaS model for platforms; enterprise license for self-hosted security teams.

Notes

  • HN commenters like worldsavior explicitly stated: "All of those reports are clearly AI and it's weird seeing the staff not recognizing it as AI and being serious." This tool addresses that specific frustration by automating the detection.
  • High practical utility in the current landscape where, as noted by colechristensen, LLMs can actually be effective at judging code quality if prompted correctly, suggesting this is a solvable technical challenge.

[Entry Fee Reimbursement Platform for Bounties]

Summary

  • A managed escrow system for bug bounty programs where researchers pay a small, refundable entry fee that is returned only if the report is deemed legitimate and actionable by the project maintainers.
  • The core value proposition is to introduce a "trivial inconvenience" (as described by TeMPOraL) that raises the cost of spamming, filtering out automated slop without deterring serious researchers.

Details

Key Value
Target Audience Open source projects with high traffic (like cURL) seeking to reduce noise without lowering bounty payouts.
Core Feature Secure payment processing for entry fees, automated refund logic based on maintainer status (e.g., "Resolved" or "Triaged" states), and a dashboard to manage funds.
Tech Stack Stripe Connect for payments, Next.js (React), PostgreSQL for ledger tracking, integration with HackerOne API.
Difficulty Low
Monetization Revenue-ready: Transaction fee (e.g., 5%) on processed entry fees; potential sponsorship from bug bounty platforms to subsidize the barrier to entry.

Notes

  • dlcarrier directly proposed this: "An entry fee that is reimbursed if the bug turns out to matter would stop this, real quick."
  • The discussion highlights that manual triage is exhausting (plastic041: "It must have been extremely tiring"), and a financial friction model is a pragmatic way to automate the initial filter.

[Open Source Reputation Oracle]

Summary

  • A decentralized, queryable database that tracks reporter reputation across multiple open source projects and bug bounty platforms to identify "slop-mongers" (andrewflnr) and repeat offenders.
  • The core value proposition is to prevent banned spammers from simply creating new accounts to continue flooding projects with AI-generated noise.

Details

Key Value
Target Audience Open source maintainers and security teams who need a holistic view of a reporter's history beyond a single platform.
Core Feature API to check a reporter's global score based on report acceptance rates, project flags (e.g., "spam", "invalid"), and cross-platform consistency.
Tech Stack Go (backend), GraphQL API, IPFS or distributed ledger for immutable reputation storage, browser extension for integration.
Difficulty Medium
Monetization Hobby: Open source project initially, with potential for grants from foundations (like OpenSSF) or optional donations for API access.

Notes

  • smusamashah suggested a "Youtube strikes like system" where PRs are tied to people. This tool would provide the backend infrastructure for that "collective tagging" system.
  • mostafa noted that while worldsavior finds the reports clearly AI, some might be human students lacking English skills. A reputation oracle helps distinguish genuine low-skill attempts from pure AI spam by tracking consistency across multiple submissions.

[Report Contextualizer & Verification Sandbox]

Summary

  • A secure, isolated execution environment that automatically runs the reproduction steps provided in a bug report and validates the existence of the claimed vulnerability before a human sees it.
  • The core value proposition is to eliminate the manual labor of trying to reproduce AI-generated hallucinations (golem14: "I can’t tell if the reports are directly from an ai or some very junior student"), providing immediate evidence of validity.

Details

Key Value
Target Audience Security triagers and open source maintainers drowning in unverified claims.
Core Feature Automated containerized testing of submitted code snippets/exploits, generating a "Pass/Fail" report with logs and diffs.
Tech Stack Docker (sandboxing), Rust/Go for orchestration, CI/CD pipelines (GitHub Actions/Jenkins), integration with HackerOne/Jira.
Difficulty High
Monetization Revenue-ready: Enterprise SaaS for large open source foundations or companies with dedicated security teams; free tier for small projects.

Notes

  • ChrisRR mentioned that when AI reports are mixed with genuine ones, "it's not so simple and very time consuming" to tell the difference. Automated reproduction solves this ambiguity by providing objective proof.
  • colechristensen showed that LLMs can analyze reports if given the right context. This tool provides that context (actual execution results) to humans or other AI triage systems.

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