AI generated security reports about curl

Original Article ↗ Hacker News Discussion ↗

AI-generated vulnerability reports are beginning to flood bug bounty platforms, as shown by multiple bogus security claims against the widely used curl tool. Commenters note that large language models can produce plausible-sounding but incorrect analyses, wasting maintainers’ time, degrading the quality of CVE ecosystems, and incentivizing low-effort “beg bounty” behavior. Many argue that programs will need tighter access controls, reputation systems, or even submission fees, while still preserving paths for legitimate, non-native-English contributors to report real issues.

LLM-Generated Curl Vulnerability Report

  • Thread centers on a bogus security report for curl clearly (to many readers) produced or heavily assisted by an LLM.
  • The report looked superficially professional and detailed, but invented code, misread the real code, and then doubled down when challenged.
  • Some see it as a new form of “script kiddie” behavior: run a scanner or LLM, paste results, demand a bounty or résumé line.

Bug Bounties, CVEs, and Incentives

  • Curl’s bug bounty (up to ~$10k) and the prestige of filing CVEs are viewed as strong incentives for low-effort or automated submissions.
  • Commenters note a long history of trivial CVE attempts (e.g., “grep strcpy”) even before LLMs.
  • Concern that this trend undermines the CVE system’s credibility and wastes triage resources.

Technical Discussion of the Alleged Vulnerability

  • Multiple comments explain that the specific curl code is safe: fixed-size, compile-time-known buffers, and controlled data; no user input.
  • Debate over strcpy vs strncpy vs strlcpy vs snprintf and when each is appropriate.
  • Some acknowledge a static analyzer could flag the pattern because safety relies on a function’s contract (Curl_base64_encode), though the LLM’s explanation was wrong.
  • Side discussion about stack vs heap usage, memory cleanup patterns, and minor design alternatives.

Impact of LLM Spam on Maintainers and Ecosystems

  • Widespread frustration that a few cents of LLM output can consume hours of expert time; Brandolini’s law is invoked.
  • Seen as a denial-of-service on human attention, especially harmful to open source maintainers already time-constrained.
  • Fear that maintainers will become harsher, discouraging genuine newcomers.

Detecting and Interpreting LLM-Generated Text

  • People note recognizable patterns: formulaic structure, excessive politeness, constant apologizing, corporate tone.
  • Worry that as models improve or are customized (e.g., rude or slangy styles), detection will get harder.
  • Counterpoint: some non-native speakers legitimately use LLMs for grammar/translation, so “LLM voice” doesn’t automatically mean fake content.

Proposed Defenses Against LLM-Driven Bug-Bounty Spam

  • Suggestions include:
    • Application-only or “pool” models with vetted researchers.
    • Small submission fees or account deposits, refunded for non-spam reports.
    • Whitelists and reputation systems; fee waivers after a few good-faith submissions.
    • Professional triage services to filter junk (though this shifts cost).

Views on LLM Capabilities and Appropriate Use

  • Many describe LLMs as fluent “BS generators”: text has the right surface texture but often lacks substance.
  • Comparison to image models: impressive at first glance, but artifacts appear on close inspection.
  • Some argue LLMs are valuable as assistive tools (translation, summarization, drafting) but dangerous when used as independent “thinkers.”
  • Broader concern that LLM content will flood reviews, guides, legal filings, and technical forums, degrading trust and raising participation barriers.
  • Minority perspective expresses mild reassurance about job security, given current LLM limitations.