AISafe Labs and GitHub CodeQL both scan your source code for vulnerabilities, but they work in fundamentally different ways:
GitHub CodeQL is a semantic code analysis engine that turns your code into a database you can query. It powers GitHub code scanning, and GitHub Code Security adds Copilot Autofix and an AI scan on top.
AISafe Labs is a state-of-the-art, autonomous and continuous application security platform, powered by code audits and penetration tests that follow a unique methodology built on hackers' expertise, at scale and machine speed.
In this post, we'll cover these differences in more detail, show what each tool found when we ran both against the same open-source application, and answer frequently asked questions about both tools.
This comparison was published on November 6, 2026 and reflects the tools as we tested them then. Pricing and features change often, so check each vendor’s current pages for the latest.
How is AISafe Labs different from GitHub CodeQL?
1. There are no queries to write
CodeQL turns your code into a database and runs queries against it. That gives you deep dataflow analysis, but it only finds what a query describes. GitHub now also offers an AI scan of pull requests (generally available since October 2026) and of whole repositories (in preview); our run used CodeQL alone.
AISafe Labs uses AI agents that read your entire application the way an attacker would, so there are no query suites to pick or extend.
2. It covers more vulnerability classes
In our benchmark, CodeQL found one low-severity vulnerability, and about 83% of its findings were false positives.
AISafe Labs found many more, in classes CodeQL does not look at: broken access control, authentication and session handling, token exposure, SSRF and race conditions.
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Comparing AISafe Labs and GitHub CodeQL
Verdict
Pick AISafe Labs if you:
- want to test your application from a real attacker's perspective
- expect a meticulous audit of your entire codebase, with no false positives to triage
- value a solution that not only reports issues but helps you remediate them and validate the fixes
- want security that runs continuously, reviewing every change across your CI/CD pipeline and developer workflow
Pick GitHub CodeQL if you:
- already use GitHub and want code scanning built into pull requests
- want deep, query-based dataflow analysis that you can extend with your own queries
- need a free option for open-source repositories
How we tested
We ran AISafe Labs and CodeQL against the same commit of an open-source application, then checked every finding against the source code. Every tool in the benchmark, including AISafe Labs, was scored with the same rules.
- Target: a production open-source web application written in TypeScript, about 310,000 lines in 2,368 TypeScript and JavaScript files. Both tools scanned the same unmodified commit.
- AISafe Labs: Code Scan (SAST) with default settings, run on 4 November 2026.
- CodeQL: CodeQL CLI 2.27.4 with the
javascript-security-extendedquery suite, without a build step, run on 5 November 2026. - Verdicts: we traced each finding through the code and gave it one of three verdicts. A real vulnerability is a flaw an attacker can exploit, rated with CVSS 3.1. An informational finding is accurate but not exploitable, such as hardening advice or behaviour the application intends. A false positive is a claim the code does not support.
Results
CodeQL reported 6 findings, of which 1 was real vulnerability. AISafe Labs reported 42, of which 22 were exploitable.
 | CodeQL | |
|---|---|---|
| Findings reported | 42 | 6 |
| Real vulnerabilities | 22 | 1 |
| Informational | 20 | 0 |
| False positives | 0 | 5 |
| Cost of this run | Free | Free |
Where GitHub CodeQL did well. CodeQL found something AISafe Labs did not report: an insecure temporary file, where the application writes to a fixed path that another local user could pre-create. It is a low-severity issue, but it is a real one, and it is exactly the kind of file-handling flaw a query-based engine is good at.
Where GitHub CodeQL tended to miss. Most of its other findings did not hold up, including a password-hashing warning on random API tokens and XSS reports on an image tag and a page that exists only in development. It reported nothing in the classes that dominate real incidents in this kind of application: broken access control between teams and recipients, authentication and session handling, token exposure, SSRF and race conditions.
Feature comparison
This table covers each product as its vendor describes it, including parts we did not run in the benchmark.
| Feature | | GitHub CodeQL |
|---|---|---|
| Source code audit | ✓ | Beta |
| AI SAST | ✓ | ✓ |
| Dependency scanning (SCA) | ✓ | ✓ |
| Secrets detection | ✓ | ✓ |
| IaC scanning | ✓ | Partial |
| Black-box pentest | Paid plans | ✗ |
| White-box pentest | Paid plans | ✗ |
| Continuous monitoring | Paid plans | ✗ |
| Proof of exploitation | Paid plans | ✗ |
| PR security review | Paid plans | ✓ |
| Fix suggestions | Paid plans | ✓ |
| Jira and Linear sync | Paid plans | Partial |
| Self-hosted option | ✓ | Paid plans |
✓ available, including on free plans · Paid plans: not on the free plan · ✗ not offered · Not stated: the vendor’s own pages do not say. Checked against each vendor's own pages on 10 November 2026.
Pricing comparison
The two products charge for different things, so the table compares how each one bills and works through two examples from list prices.
| GitHub CodeQL | |
|---|---|---|
| How it is billed | Credits at $1 each. A code audit is priced by its lines of code and tokens, and the exact cost is shown before it starts | Per active committer a month for Code Security, on top of GitHub Team or Enterprise seats. AI features use credits |
| Free plan | Basic: SAST, AI SAST, SCA, secrets, licence risk and IaC, no card required | Yes for public repositories: CodeQL code scanning, Copilot Autofix, secret scanning and push protection. Dependabot alerts on all repositories |
| Paid plans | Pro: $40, $100, $250 or $500 a month ($30, $75, $200 or $400 billed yearly), with the same number of credits | Code Security $30 and Secret Protection $19 per active committer per month, as add-ons to GitHub Team ($4 per user per month) or Enterprise. AI Scan and agentic autofix use metered AI credits |
| Enterprise | Custom credit volumes | GitHub Enterprise from $21 per user per month, plus the security add-ons; Enterprise Server for self-hosting |
| Example: 10 developers, 20 private repositories, one month | $40 a month on Pro ($30 billed yearly), including 40 credits. Code audits of the repositories are extra and priced by their size | 10 × $4 (Team) + 10 × $30 (Code Security) = $340 a month |
| Example: one full scan of a 300,000-line codebase | Priced by code size; the exact cost is shown before you start | $0 for a public repository. For a private one it is covered by the per-committer fee; the AI Scan credit cost per scan is not published |
List prices in US dollars, before discounts and tax, from each vendor's own pricing page on 10 November 2026. The examples use list prices only.
FAQ
GitHub CodeQL is a rule-based scanner. AISafe Labs uses AI agents that validate each finding before reporting it, so its results are real vulnerabilities, including access-control and business-logic defects.
For this comparison, both were free. AISafe Labs Code Scan is included in the free Basic plan, and our GitHub CodeQL run cost nothing either. Paid plans differ, so compare the pricing pages for what you need beyond scanning.
For finding security vulnerabilities in your own code, AISafe Labs covers more classes in our benchmark. GitHub CodeQL still does things AISafe Labs does not, as listed in the verdict above, so many teams keep both.
Yes. CodeQL gives you native GitHub integration and strong file-handling and injection coverage. AISafe Labs covers the access-control and business-logic defects that sit outside its queries.
Yes. CodeQL is free for public repositories and available to private repositories through GitHub Code Security.
