About Best LLM Scanners

Comparing LLM security scanners and detection tools.

What this site covers

Best LLM Scanners compares the tools that probe language models for security defects. The comparisons are drawn from each project's documentation, probe catalogue and public issue tracker, because for scanners the interesting question is coverage, and coverage is something you can read.

• Pre-deployment scanners: garak, promptmap and comparable probe suites
• Red-team frameworks: PyRIT, Promptfoo and the open-source field around them
• Runtime detectors and guardrails: Rebuff, Vigil, LLM Guard, Llama Guard, NeMo Guardrails
• Model-artefact and supply-chain scanning, because weights load before prompts arrive
• Probe coverage and where each scanner is structurally blind
• False-positive behaviour reported by maintainers and users
• Integrating a scanner into CI as a release gate

21 articles are published so far. New ones are announced on the RSS feed. If you are new here, the category map explains how these tools divide the work, and the scanner picker turns your own constraints into a ranked shortlist.

How these articles are produced

Articles here are researched from primary sources: vendor and project documentation, published standards and specifications, research papers and preprints, and measurements published by whoever took them. Drafts are produced with AI assistance and then edited against those cited sources before anything is published.

No article on this site is based on first-hand testing in a private lab, and nothing here should be read as a measurement report of its own. Where a number appears, it comes from a source that is named, so you can check the original instead of taking this site's word for it.

Everything is published under a single editorial byline. That byline is a publishing identity for the site, not a claim about a named individual, and it does not carry professional credentials.

Corrections

Corrections are welcome. If something here is wrong, out of date, or attributed to the wrong source, email hello@bestllmscanners.com with the page address and what it should say. Substantive corrections are made in the article itself rather than quietly dropped.

How this site is funded

This site currently runs no affiliate links, sponsored posts, display advertising or paid placements. If that changes, the disclosure page will say so.

Contact

Email: hello@bestllmscanners.com
Site: bestllmscanners.com
Published by: Best LLM Scanners Editorial

See also the privacy policy, the terms of use, and the editorial disclosure.