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Choosing a security scanner for an open-source project

AI audits, fuzzing and rule-based static analysis find different bugs. Most well-protected projects use more than one. Here is what each is best at.

ossscanner.org

independent

AI audit of any public repository with a threat model, an independent verifier agent, root cause, introducing commit, candidate patch and SARIF. Free quick scans and weekly deep scans for enrolled projects.

Best for: Getting verified, explained findings for any project in minutes, with no setup.
Limitations: Static reading only — doesn't build or run code. Uses publicly available models, not Claude Mythos. Model output can be wrong.

Anthropic OSS Scanner

Anthropic

Free, opt-in periodic scans by Anthropic's strongest models, including Claude Mythos, in offline sandboxes with your project built from a Dockerfile. Unreviewed reports with reproducers and patches by email.

Best for: Critical infrastructure projects that can triage a steady stream of high-quality reports.
Limitations: Accepted case by case; needs a pull request, a Dockerfile and manual maintainer verification. Email-only output.

OSS-Fuzz

Google

Continuous coverage-guided fuzzing for critical open-source projects, with automatic crash triage and bug filing.

Best for: Memory-safety bugs in parsers and libraries written in C, C++, Rust or Go; every crash is reproducible.
Limitations: Requires fuzz targets and build integration; finds what fuzzers can reach, rarely logic or auth bugs.

CodeQL

GitHub

Semantic code analysis with a query language and a large library of security queries. Free for public repositories via GitHub code scanning.

Best for: Known vulnerability patterns and data-flow issues in CI, on every pull request.
Limitations: Finds what its queries describe; novel logic bugs and project-specific issues need custom queries.

Semgrep

Semgrep, Inc.

Fast pattern-based static analysis with community and custom rules; the open-source engine runs locally or in CI.

Best for: Enforcing secure coding rules and catching common mistakes quickly.
Limitations: Rule-based: high recall on known patterns, little understanding of intent or context.

Claude Security

Anthropic

Anthropic's commercial product that helps enterprises find and fix vulnerabilities in their own code with Claude models.

Best for: Companies bringing AI security review into their software development lifecycle.
Limitations: Commercial; aimed at organisations rather than individual open-source maintainers.

AI audit vs fuzzing vs static analysis

Fuzzers execute code with millions of generated inputs and are unbeatable at reproducible memory corruption, but only where their harnesses reach. Rule-based analysers like CodeQL and Semgrep check every line against known patterns, cheaply and on every commit. AI auditors read code the way a reviewer does: they understand intent, can spot broken authorization or logic, and explain the root cause — but they are probabilistic and need verification.

That is why ossscanner.org runs a second agent to disprove every finding before showing it, and why we recommend combining it with CI analysis and fuzzing.

A practical setup for a small project

Turn on CodeQL code scanning (free for public repositories).
Run a free scan on ossscanner.org and enroll for weekly deep scans.
Upload our SARIF to GitHub so AI findings appear next to CodeQL alerts.
Add a threat model so every scanner knows what's in scope.
If you handle untrusted binary formats, add fuzz targets and consider OSS-Fuzz.
If your project is critical infrastructure, apply to Anthropic's OSS Scanner.