Open-Source SAST Tools

Why open-source SAST matters

Open-source SAST tools have reached a point where most teams can build an effective application security program without spending anything on tool licenses.

Semgrep alone covers 20+ languages and runs fast enough for CI/CD integration. SonarQube Community Edition adds quality gates and code quality checks. Bandit and Brakeman go deep in their respective language ecosystems. Together, these tools cover the same vulnerability categories that commercial SAST tools charge six-figure license fees for.

That does not make commercial tools irrelevant. The gap between free and paid tools is real — deeper data flow analysis, broader framework support, better enterprise management. But for teams that cannot justify a $100K-$300K SAST budget, open-source tools are a genuine alternative rather than a stopgap.

The OWASP Benchmark Project, which tests SAST tools against a standardized Java test suite, has consistently shown that well-configured open-source tools can match or exceed poorly configured commercial tools. Configuration and rule tuning matter more than the sticker price.

Adoption reflects this. A 2024 Snyk survey found that 87% of organizations use open-source security tools in some capacity, and SAST is one of the most common categories. Even organizations that eventually buy commercial tools typically start with open-source.

Tool-by-tool breakdown

Semgrep

Semgrep is the most versatile open-source SAST tool available. It supports 20+ languages through a unified pattern-matching engine that treats code structurally rather than textually.

What it does well: Custom rules are written in YAML and match code patterns directly, making them accessible to developers who are not security specialists. The community rule registry (Semgrep Registry) contains 3,000+ rules covering OWASP Top 10 patterns for all supported languages. Scan speed is fast — a 500K-line repository takes seconds, not minutes.

Where it falls short: Semgrep Community performs local analysis rather than deep inter-procedural data flow tracking. It catches patterns within a function or a single file well, but complex taint flows across multiple files and function calls require Semgrep Pro (paid). The community rule quality varies — some community-contributed rules have high false positive rates.

Best for: Multi-language teams, teams that want to write custom security rules, CI/CD integration where speed matters.

SonarQube Community Edition

SonarQube Community Edition is both a code quality platform and a SAST tool. It combines security vulnerability detection with code smell analysis, duplication detection, and test coverage tracking.

What it does well: Quality gates let you set pass/fail criteria for builds based on vulnerability count, severity, and code coverage. The “new code” feature separates newly introduced issues from existing technical debt. Language support covers 20+ languages including Java, C#, JavaScript, TypeScript, Python, Go, and PHP. The web dashboard provides good visibility for managers and leads.

Where it falls short: Security rules in the community edition are shallower than dedicated SAST tools. Deep taint analysis and cross-file data flow tracking require SonarQube Developer Edition (paid). The community edition does not support branch analysis or pull request decoration — you need the Developer edition for that. Setup requires running a server with a database.

Best for: Teams that want code quality and security in a single tool, organizations that value quality gates and build enforcement.

GitHub CodeQL

GitHub CodeQL treats code as data. You write queries in a declarative language (QL) to search for patterns, including security vulnerabilities. It is free for public repositories on GitHub.

What it does well: The query language is powerful. You can write queries that trace data flow across functions, modules, and files with a level of precision that pattern-matching tools cannot match. GitHub runs CodeQL automatically on public repositories through code scanning. The pre-built security queries cover a broad range of vulnerability types.

Where it falls short: The QL language has a steep learning curve compared to Semgrep YAML. Writing custom queries requires understanding the QL data model for each language. Scan times are slower than Semgrep — building the CodeQL database for a large repository can take 10-30 minutes. Free usage is limited to public repositories; private repository scanning requires GitHub Advanced Security (paid per committer).

Best for: Teams on GitHub with public repositories, organizations that need deep semantic analysis and can invest in learning the query language.

Bandit

Bandit is a Python-specific security linter. It scans Python source files for common security issues like hardcoded passwords, use of insecure functions, and SQL injection patterns.

What it does well: Fast, simple, and focused. Install with pip install bandit, run bandit -r your_project/, and get results immediately. Zero configuration needed for a useful first scan. The plugin architecture lets you add custom checks. False positive rates are low because the rules are Python-specific.

Where it falls short: No data flow analysis. Bandit catches patterns — calling eval(), using pickle.loads() with untrusted input, hardcoded secrets — but it does not trace how user input reaches dangerous functions. Python-only, so it does not help if you also write JavaScript or Go.

Best for: Python teams that want a lightweight first-pass security scanner, pre-commit hooks, and quick CI checks.

Brakeman

Brakeman is a Ruby on Rails-specific SAST tool. It understands the Rails framework deeply — routes, controllers, models, views, and the conventions that connect them.

What it does well: Rails awareness means very low false positive rates. Brakeman knows that User.find(params[:id]) in a standard Rails controller does not need SQL injection protection because ActiveRecord parameterizes the query. Generic SAST tools flag this incorrectly. It also understands Rails-specific patterns like mass assignment, unsafe redirect_to, and unsafe render inline.

Where it falls short: Rails only. If your application uses Ruby outside of Rails (Sinatra, Hanami, pure Ruby), Brakeman’s coverage drops significantly. No support for other languages.

Best for: Ruby on Rails teams. If you are building Rails applications, Brakeman is not optional — it catches Rails-specific issues that no general-purpose tool finds.

Other notable tools

gosec: Go-specific security scanner. Checks for hardcoded credentials, SQL injection, file path traversal, and Go-specific issues like misuse of crypto/rand. Lightweight and fast. No data flow analysis.

Bearer: Open-source SAST that focuses on data flow analysis for JavaScript, TypeScript, Ruby, Java, Go, and Python. Newer entrant with growing rule coverage. Tracks sensitive data flows specifically, which is useful for privacy compliance.

PHPStan (with security rules): PHP static analysis tool that catches type errors and code quality issues. Third-party security rule sets add vulnerability detection. PHP-specific.

Flawfinder: C/C++ security scanner. Scans for calls to risky functions (strcpy, sprintf, gets) and ranks findings by risk level. Pattern matching only — no data flow — but useful as a quick check for C/C++ codebases.

Language coverage comparison

Semgrep has the broadest language coverage among open-source options. SonarQube CE is close behind. CodeQL matches them but limits free usage to public repositories.

For languages not covered well by any single tool, combining Semgrep (broad coverage, fast) with a language-specific tool (Bandit for Python, gosec for Go, Brakeman for Rails) gives the best results.

CI/CD integration comparison

Semgrep and SonarQube have the most mature CI/CD integrations. Both support SARIF output, which GitHub and GitLab can ingest for code scanning alerts.

CodeQL’s CI/CD story is GitHub-native. If you are on GitHub, the integration is seamless. If you are on GitLab or Bitbucket, CodeQL is harder to set up.

Bandit, Brakeman, and gosec all produce SARIF output that can be uploaded to GitHub code scanning. The integration works but requires more manual configuration than tools with native CI plugins.

Detection quality: what open-source misses vs commercial

The honest comparison: open-source tools catch pattern-based vulnerabilities well. They miss complex data flow vulnerabilities that cross file and module boundaries.

What open-source catches reliably

• Hardcoded secrets (API keys, passwords, tokens)
• Use of insecure functions (eval, exec, unsafe deserialization)
• SQL injection in direct query construction
• XSS in direct HTML output (not through template engines)
• Insecure cryptography (weak algorithms, short key lengths)
• Framework-specific issues (when using a framework-aware tool)

What open-source often misses

• Cross-file taint analysis: user input enters in controller.java, passes through service.java, reaches a dangerous function in dao.java. Tracking this across three files requires inter-procedural analysis that most free tools do not perform.
• Complex sanitization validation: input passes through a custom sanitizer, but the sanitizer has a bypass. Commercial tools model sanitizer completeness; most open-source tools treat any sanitizer as fully effective.
• Second-order vulnerabilities: user input is stored in a database, retrieved later, and used in a dangerous context. This requires understanding data persistence, which static analysis handles poorly regardless of price point.

The OWASP Benchmark perspective

The OWASP Benchmark scores tools on true positive rate (TPR) and false positive rate (FPR). Commercial tools like Checkmarx and Fortify score higher on TPR for complex data flow tests. Open-source tools score comparably on pattern-matching tests and often have lower FPR because they are more conservative.

A well-configured Semgrep deployment with custom rules can reach 60-70% of the vulnerability detection of a commercial tool, with lower noise. The remaining 30-40% is where deep inter-procedural analysis matters.

Building a multi-tool open-source SAST stack

No single open-source tool covers everything. A practical stack combines a general-purpose scanner with language-specific tools and secrets detection.

Recommended stack for most teams

1. Semgrep as the primary SAST scanner. Runs on every PR. Covers all languages in your stack. Custom rules for your internal frameworks and patterns.

2. Language-specific tool for your primary language. Bandit for Python, Brakeman for Rails, gosec for Go. These catch language-specific patterns that Semgrep’s generic rules may miss.

3. SonarQube CE for quality gates and code quality. Combines security scanning with code smell detection, duplication analysis, and coverage tracking. Enforces build policies.

4. Secrets detection with GitGuardian or TruffleHog. This is not SAST, but it catches one of the most common and high-impact vulnerability types: committed credentials.

How they work together

Semgrep runs first in the PR pipeline (fast, seconds). The language-specific tool runs in parallel. SonarQube runs as a post-merge analysis with quality gates. Secrets scanning runs as a pre-commit hook and again in CI.

Findings from all tools get uploaded to GitHub code scanning (via SARIF) or a centralized dashboard. Deduplication matters — a SQL injection flagged by both Semgrep and Bandit should be one finding, not two.

What this stack costs

Tool licenses: $0. Server costs: SonarQube CE needs a small VM or container (2 CPU, 4GB RAM). CI compute: additional pipeline minutes, typically a few minutes per PR. Total cost is infrastructure and developer time for setup and tuning.

When to upgrade to commercial

Open-source SAST is enough for many teams. Here are the specific triggers that justify upgrading to commercial tools.

Your codebase has complex cross-file data flows. If your application architecture involves data flowing through multiple services, middleware layers, and shared libraries, open-source tools miss vulnerabilities that inter-procedural analysis catches. Checkmarx, Fortify, and Veracode SAST all perform deep cross-file analysis.

You need compliance dashboards. Regulated industries (finance, healthcare, government) require evidence of security testing. Commercial tools provide audit trails, compliance reports, and role-based access that auditors expect. Open-source tools can produce similar data, but you will build the reporting yourself.

You have 50+ developers and need centralized management. At scale, managing SonarQube quality profiles, Semgrep rule packs, and Bandit configurations across hundreds of repositories becomes an operational burden. Platforms like Checkmarx One and Snyk centralize policy, configuration, and findings management.

Your false positive rate is stubbornly high. If you have tuned rules, written custom queries, and still see 30%+ false positive rates, a commercial tool with deeper analysis may produce cleaner results out of the box.

You need dedicated support. When a tool flags something unexpected and you need to understand why, open-source means reading source code and searching GitHub issues. Commercial tools come with support engineers who can help you interpret findings and write custom rules.

For teams that do not hit these triggers, the open-source stack described above is a sound long-term strategy, not just a temporary measure. See our SAST tools category page for full reviews of both open-source and commercial options, and our Semgrep vs CodeQL comparison and SonarQube vs Semgrep comparison for detailed head-to-head analysis.

FAQ

Frequently Asked Questions

Written by

Suphi Cankurt

Suphi Cankurt is an application security enthusiast based in Helsinki, Finland. He reviews and compares 154 AppSec tools across 10 categories on AppSec Santa. Learn more.