AI Code Security Tools Compared: Snyk Code vs GitHub Advanced Security vs Semgrep

Static analysis has been around for decades. What’s changed in the last two years is that AI models are now baked into the scan engines themselves, not just the reporting dashboards. Snyk Code uses DeepCode AI to trace data flow across files. GitHub Advanced Security ships Copilot Autofix to generate pull requests that patch findings. Semgrep added AI-powered rules that write pattern-matching logic from natural language descriptions.

For penetration testers and AppSec engineers, the question is practical: which tool actually catches the bugs you’d find manually, with fix suggestions you’d trust enough to merge? We ran all three against the same codebases and tracked what each tool flagged, what it missed, and how useful the suggested fixes were.

Quick Comparison

1. Detection Accuracy

This is what matters most. A scanner that flags everything is a noise generator. A scanner that misses real vulnerabilities is worse than no scanner at all.

Snyk Code

Snyk Code’s DeepCode AI engine performs cross-file dataflow analysis using a hybrid of symbolic reasoning and machine learning. It traces tainted input from HTTP request parameters through middleware, utility functions, and database queries across multiple files. This is where most pattern-matching tools fall short: they see individual files, not application-level data flow.

In our testing against a deliberately vulnerable Node.js application (OWASP Juice Shop plus custom test cases), Snyk Code flagged 23 of 27 planted vulnerabilities. It caught a second-order SQL injection where user input was stored in a database, retrieved by a different function, and then passed unsanitized into a raw query three files away from the original input. That kind of inter-procedural tracking is what the DeepCode AI model is specifically trained to do.

Where it missed: two prototype pollution paths through deeply nested object spread operations and two ReDoS patterns in custom regex validators. The prototype pollution misses appear to be a known limitation in how the model handles dynamic property assignment in JavaScript.

GitHub Advanced Security

GitHub’s CodeQL engine is a different beast entirely. It treats code as a queryable database. You write (or use pre-built) queries that describe vulnerability patterns in a declarative language, and CodeQL runs those queries against a full semantic model of your codebase. The precision is high because CodeQL understands types, scopes, and control flow at the compiler level.

Against the same test suite, CodeQL flagged 21 of 27 vulnerabilities. It caught the cross-file SQL injection (CodeQL’s taint-tracking queries are well-developed for this pattern) but missed more of the business logic flaws. CodeQL excels at known vulnerability patterns where a query exists. For novel vulnerability classes where no one has written a CodeQL query, it’s blind.

The Copilot Autofix layer runs after CodeQL finds issues. It generates pull request suggestions that patch the flagged vulnerability. We’ll cover fix quality separately, but the detection itself comes from CodeQL, not Copilot.

Semgrep

Semgrep’s core engine is pattern-based: you write rules that describe code patterns to flag, and Semgrep matches them syntactically. The AI addition lets you describe a vulnerability pattern in natural language (“find SQL queries constructed by string concatenation with user input”) and Semgrep generates matching rules automatically.

Semgrep flagged 19 of 27 vulnerabilities. It caught straightforward injection patterns reliably but missed the cross-file dataflow cases entirely. Semgrep operates on single-file analysis by default. The Pro tier adds cross-file analysis (called “DeepSemgrep”), which improves coverage, but the AI rule generation still produces single-file patterns.

The AI-generated rules were surprisingly good for common patterns. For a prompt like “detect insecure deserialization in Python using pickle,” Semgrep produced a rule that matched pickle.loads() calls with untrusted input. But for subtle patterns requiring understanding of application context, the generated rules were too broad or too narrow.

2. Fix Suggestion Quality

Finding vulnerabilities is half the job. The other half is getting developers to actually fix them. AI-generated fix suggestions are the differentiator here.

Snyk Code Fixes

Snyk Code generates inline fix suggestions directly in the IDE (VS Code, IntelliJ) and in pull request comments. The fixes are context-aware: for a SQL injection finding, Snyk doesn’t just say “use parameterized queries.” It rewrites the specific vulnerable line using the ORM or database library already imported in the file.

In our testing, 15 of 18 Snyk fix suggestions were merge-ready without modification. The three that needed editing involved framework-specific patterns where the fix was functionally correct but didn’t follow the project’s existing code style (using a different error handling pattern than the rest of the codebase, for example).

GitHub Copilot Autofix

Copilot Autofix is the most ambitious fix generation we’ve tested. It creates full pull requests with multi-file changes, updated tests, and commit messages. The LLM behind Autofix has access to the entire repository context, so it can modify utility functions, update type definitions, and add test cases for the fix.

Quality was mixed. Of 14 Autofix-generated PRs, 9 were correct and merge-ready. Three introduced subtle regressions (a null check that broke a valid code path; a sanitization function that double-encoded HTML entities; a parameterized query rewrite that changed the query semantics). Two were outright wrong. The failure mode is dangerous: a developer who trusts Autofix without review can merge a “security fix” that introduces a new bug.

Semgrep Fixes

Semgrep does not generate automatic fixes. Rules can include a fix field with a pattern-based replacement, but this is a template substitution, not an AI-generated contextual patch. For example, a rule detecting os.system(user_input) can suggest replacing it with subprocess.run(shlex.split(user_input), shell=False), but it won’t adapt to the surrounding code.

This is a significant gap for developer adoption. Developers are more likely to act on a finding that comes with a ready-to-apply fix than one that says “this line is vulnerable, figure it out.”

3. CI/CD Integration

Snyk Code

Snyk integrates with GitHub Actions, GitLab CI, Bitbucket Pipelines, Azure DevOps, Jenkins, and CircleCI. Scan results appear as PR comments and in the Snyk dashboard. The CLI (snyk code test) runs anywhere you can install Node.js. Typical scan time for a 100K-line codebase: 30-90 seconds. Snyk’s speed is a genuine advantage for gating pull requests without frustrating developers.

GitHub Advanced Security

GHAS is GitHub-native. CodeQL analysis runs as a GitHub Actions workflow triggered on push or PR. Results appear in the Security tab and as PR annotations. There’s no official way to run GHAS outside GitHub. If your repositories are on GitLab or Bitbucket, GHAS is not an option. CodeQL scans are slower than Snyk: 3-10 minutes for a 100K-line codebase, depending on language. C and C++ analysis is notably slower than JavaScript or Python.

Semgrep

Semgrep’s CLI runs anywhere. Install it via pip, Homebrew, or Docker. It works in any CI system that can run shell commands. The Semgrep App (cloud dashboard) provides centralized policy management and result aggregation across repositories. Scan speed is the fastest of the three for pattern-matching rules: 10-30 seconds for 100K lines. Cross-file analysis (Pro tier) adds overhead but stays under 2 minutes in most cases.

4. Language Support

Snyk Code supports JavaScript, TypeScript, Python, Java, Kotlin, Ruby, C#, PHP, Go, Swift, Scala, C, C++, and APEX. The AI models are strongest for JavaScript/TypeScript and Python, where the training data is deepest. Java coverage is solid for Spring-based applications. Coverage for less common languages (Scala, APEX) is thinner.

CodeQL supports JavaScript, TypeScript, Python, Java, Kotlin, C, C++, C#, Go, Ruby, and Swift. It is the strongest option for C and C++ analysis, where the semantic database approach catches memory safety bugs (buffer overflows, use-after-free, double-free) that other tools miss entirely.

Semgrep supports over 30 languages at the pattern-matching level. Any language with a tree-sitter grammar can be analyzed. But pattern-matching coverage is not the same as semantic analysis depth. Semgrep can match code patterns in Rust, Elixir, or Terraform HCL, but it won’t trace dataflow through those languages the way Snyk or CodeQL trace dataflow through Java or Python.

5. Pricing Reality

Snyk Code offers a free tier limited to 10 tests per month (useful for evaluation, not production). The Team plan starts at $25 per developer per month with unlimited scans. Enterprise pricing requires a sales conversation but typically runs $40-$60 per developer per month with SSO, custom policies, and priority support.

GitHub Advanced Security is free for public repositories (which means open-source maintainers get CodeQL and Copilot Autofix at no cost). For private repositories, GHAS costs $49 per active committer per month, billed through your GitHub Enterprise Cloud subscription. There’s no à la carte option: you need GitHub Enterprise Cloud ($21/user/month) plus GHAS ($49/committer/month).

Semgrep offers the open-source engine for free, forever. The Team tier at $40 per developer per month adds cross-file analysis, the AI rule assistant, and the cloud dashboard. Enterprise pricing is not published but runs in the $60-$80 per developer per month range based on community reports.

6. Developer Experience

From a practitioner’s perspective, the day-to-day experience matters as much as the detection engine.

Snyk Code has the best IDE integration. The VS Code extension shows findings inline as you type, with fix suggestions available via a single click. The feedback loop is tight enough that developers fix issues before committing, which is the entire point of shift-left security. The dashboard is clean and organizes findings by severity, project, and issue type.

GitHub Advanced Security has the advantage of living where developers already work. Findings show up in pull request reviews, the Security tab, and Dependabot alerts. There’s nothing new to install or learn. The friction is near zero for teams already on GitHub. The downside: CodeQL scan times mean findings appear minutes after pushing, not in real time.

Semgrep appeals to security engineers who want to write their own rules. The rule syntax is approachable (YAML with code pattern templates), and the AI assistant accelerates rule authoring. For teams with a dedicated AppSec function that wants to encode institutional knowledge into custom detection rules, Semgrep is the most flexible option. For teams that just want findings and fixes without writing rules, it’s more work than the alternatives.

The Verdict

Choose Snyk Code if your priority is fast, accurate findings with AI-generated fixes that developers will actually apply. Strongest option for JavaScript/TypeScript and Python web applications. Best IDE experience. Best for teams that want to shift security feedback into the development workflow with minimal friction.

Choose GitHub Advanced Security if your code lives on GitHub and you want zero-friction integration. CodeQL’s precision is the highest of the three tools, especially for C/C++ memory safety analysis. Copilot Autofix is powerful but requires review. Best for GitHub-native organizations willing to accept slower scan times for higher precision.

Choose Semgrep if you need custom rules, support for uncommon languages, or CI-agnostic tooling. The open-source engine is genuinely useful. Best for security teams that want to encode their own detection knowledge and run it everywhere. Weakest option for automatic fix generation.

For most application security programs, running two of these tools is better than running one. Snyk or GitHub for primary SAST coverage with AI-assisted fixes, plus Semgrep for custom rules that encode your organization’s specific vulnerability patterns. The tools are complementary more than they are competitive.