Mythos Finds a Curl Vulnerability
An experimental run of Anthropic’s unreleased Mythos AI model against the curl codebase found just one low‑severity vulnerability, leading many developers to question claims that the system represents a revolutionary leap in automated security analysis. Commenters contrast this modest result with earlier AI-assisted audits that already uncovered hundreds of bugs in curl, and with Mozilla’s report that Mythos helped identify hundreds of issues in Firefox, suggesting that prompt design, tooling, and marketing all heavily shape perceived impact. The thread highlights a broader shift: large language models are becoming powerful, widely accessible vulnerability-finding tools, but their real advantage may be incremental efficiency and scale rather than the apocalyptic “too dangerous to release” narrative pushed in some publicity.
Role and impact of Mythos on curl
- Many note that curl had already been heavily scanned with other AI tools, fuzzers, and audits; expectations of a “tsunami” of new bugs from Mythos were therefore unrealistic.
- Mythos reportedly found five plausible issues, of which one will become a low‑severity CVE. Commenters see this as evidence it’s an incremental improvement, not a revolution.
- Some argue the result mainly reflects curl’s unusually high security maturity; others counter that curl still gets new CVEs regularly, so a truly game‑changing tool should find more.
Comparison with other AI tools and workflows
- Previous AI-based tools (AISLE, ZeroPath, Codex Security, etc.) have already led to hundreds of bugfixes and around a dozen CVEs in curl.
- Several suggest Mythos looks like “Opus plus a harness/prompt,” with the real gains coming from good pipelines, adversarial review, and dynamic validation rather than the raw model.
- Others say even current public models can already find bugs and generate PoC exploits in large codebases; Mythos mostly lowers the skill required.
Firefox / broader ecosystem vs curl
- Firefox’s use of Mythos reportedly surfaced hundreds of vulnerabilities, far more than earlier experiments with prior models. Some view this as strong evidence of a step change.
- Skeptics note differences in harness maturity, scope of scanning, and token budgets; they argue you can’t directly compare Firefox “first-time large-scale AI scan” numbers with curl’s “post-many-AI-passes” state.
- Unclear: exact false-positive rates and how much of Firefox’s haul truly required Mythos versus any strong model plus a good pipeline.
Danger, accessibility, and attacker economics
- One camp stresses that Mythos (and similar models) make high‑end vulnerability discovery and exploitation accessible to non-experts, which meaningfully changes attacker economics.
- Others argue this capability already existed with prior models; Mythos primarily makes it easier for people who “don’t know what they’re doing.”
- There’s concern that defenders are now racing against automated scanning of vast software ecosystems, particularly for medium/low‑scrutiny projects.
Marketing, hype, and co‑marketing concerns
- Many see Mythos framing (“too powerful to release,” “reshaping cybersecurity”) as aggressive marketing, likened to earlier “too dangerous to release” claims about past models.
- Some suspect co‑marketing arrangements with browser vendors and governments, or at least mutually beneficial publicity.
- Others push back, saying the capabilities are real, and that using vivid language to get C‑suites to fund security work is pragmatically helpful, even if dramatized.