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Audit Bench Ai combines LLM reasoning with static analysis to catch the security holes, logic bugs, and framework misuse that traditional linters miss — on a single file, a pull request, or a whole repository.
Doing technical due diligence on a software acquisition? Get a risk report in days, not weeks → or use the free technical due diligence checklist.
Paste a repo URL and get a health score, exposed secrets, vulnerable dependencies and risky code in about a minute, with a report link you can share.
No sign-up. Public repos only. Try , , .
Every provider runs the same three-stage pipeline and the same review lenses — pick the one that fits your budget or your existing contract, per audit or as your account default.
Modern AI coding assistants generate code quickly but frequently introduce hidden bugs, security issues, and framework misuse. Traditional linters catch syntax problems — not business logic or intent.
Reviews. Gates. Fixes. Reports. Same findings whether it's a file, a PR, or a whole repository.
Beyond one-off audits: review the code your team merges, track quality trends over time, and wire it into the pipeline you already have.
Review a GitHub PR or GitLab MR scoped to just the changed lines — findings post back as inline review comments, a summary, and a merge-blocking status check.
Security, performance, and technical-debt scores trended over time, plus a breakdown of your most common findings and riskiest files.
Run `auditbench scan` or `auditbench audit` from a pipeline step, a pre-commit hook, or a terminal — same engine, same findings, scriptable output.
Daily and monthly AI-audit limits per plan, usage-based — not a flat seat count — so cost scales with what a team actually reviews.
Not another tab to check. Findings, gates, and answers show up directly on the PR or MR.
Findings land as real review comments on the exact changed lines — GitHub review threads or GitLab discussions — not a dashboard you have to remember to check.
Every review posts a plain-English walkthrough alongside the inline comments — verdict, finding counts, and a link to the full report.
A commit status check reports pass/fail on every PR and MR. Wire it into branch protection and stop shippable-looking regressions before they merge.
A live, always-current badge for your README showing the verdict of your most recent scan.
npm audit for Node, OSV.dev for Python — known-vulnerable packages surface automatically, no extra tooling to install.
@-mention the bot in any PR or MR thread and it replies in context, using the diff to answer follow-up questions.
AI credits, not raw request counts. A three-stage pipeline keeps the LLM off the critical path until the code actually warrants it.
ESLint, TypeScript diagnostics, complexity, formatting, and secret scanning run first — no AI, no cost.
Only the functions Stage 1 flags as risky go to an LLM, with just the relevant code and types — not the whole file.
Re-scanning unchanged code — the common case across repeated repo scans — costs nothing and returns instantly.
Practical write-ups on the vulnerabilities, architecture pitfalls, and review habits our own engine is built to catch.
A practical guide to using AI code review well: what it can catch, where it fails, and how to combine it with human review without adding noise.
Which technical due diligence findings actually move valuation or stall a deal — bus-factor risk, transitive license exposure, security patterns, and architecture-growth mismatches.
A practical guide to spotting command injection in code review: which shell-executing functions to flag, why allowlisting beats escaping, and where it hides in ordinary CLI wrappers.
A practical discussion of Python dependency risk: version pinning, lockfiles, supply chain trust, review habits, and when to upgrade safely.
A practical guide to reviewing file upload features in code review: type validation, storage location, and the malicious files that slip through client-side checks.
How GitHub's AI code review works inside Copilot, where it fits in a PR workflow, how it compares to a dedicated review tool, and how to use it without letting automation replace judgment.
Short answers to the questions that matter when you are deciding whether to trust a review tool in your workflow.
Free local checks run first, and only risky code is escalated to an LLM. Cached scans are free, so repeated reviews do not burn credits.
GitHub and GitLab are supported natively for pull request and merge request review.
It understands common web stacks including React, Next.js, Node.js, NestJS, Python, FastAPI, Django, Laravel, Spring Boot, Supabase, Deno, and Firebase.
No credit card required. Connect GitHub or GitLab, or upload a .zip, and see what an audit finds in your own code.