Static code analysis tools open source teams reach for most often fall into two groups: general-purpose scanners that support many languages through custom rule engines (Semgrep, CodeQL), and language-specific linters with security-focused rule sets bolted on (Bandit for Python, ESLint security plugins for JavaScript, gosec for Go). None of them is a complete replacement for a commercial SAST platform on its own, but together — and used deliberately — they cover a meaningful share of what teams pay for.
What does Semgrep actually cover?
Semgrep is a pattern-matching static analysis engine that lets you write custom rules in a syntax that looks close to the code itself, and it ships with a large community and Pro rule registry covering common vulnerability classes (injection, hardcoded secrets, insecure deserialization) across a dozen-plus languages. Its core strength is speed and low false-positive rates on well-written rules, since it matches syntactic patterns rather than doing full data-flow analysis by default. Its main gap is cross-file taint tracking — Semgrep's free tier does single-file analysis well but multi-file/interprocedural data-flow analysis (does user input from file A actually reach a dangerous sink in file B) is a Pro feature, which matters a lot for catching real injection vulnerabilities rather than isolated code smells.
What does CodeQL add that pattern matching doesn't?
CodeQL, GitHub's semantic code analysis engine, treats code as a queryable database and can trace data flow across an entire codebase — following a value from an HTTP request parameter through several function calls to see if it reaches a SQL query unsanitized, for example. That's a materially different (and more expensive) kind of analysis than Semgrep's default pattern matching, and it's genuinely good at catching complex, multi-hop vulnerabilities. The tradeoffs: CodeQL has a steeper learning curve for writing custom queries, scans take longer, and free usage is scoped to public repositories on GitHub — private repo usage requires GitHub Advanced Security, which isn't free.
Are language-specific linters worth running alongside a general scanner?
Yes, for coverage a general tool sometimes misses on that language's idioms specifically. Bandit (Python) and gosec (Go) both encode security rules tuned to language-specific footguns — Bandit flags things like subprocess calls with shell=True or use of eval(), patterns a general-purpose scanner's default rule set might not weight as heavily. They're fast, low-maintenance, and easy to add to CI, but their coverage is narrower by design — they're a complement to a general SAST tool, not a replacement.
What do open source SAST tools generally not cover?
- Reachability analysis at scale — knowing whether a flagged vulnerable code path is actually exercised at runtime typically requires either paid tooling or significant custom instrumentation.
- Unified triage across scan types — open source tools each produce their own output format, so combining SAST, SCA, and container scan results into one prioritized queue is usually a DIY integration project.
- Enterprise workflow features — ticket auto-creation, policy gates tied to compliance frameworks, and audit trail requirements typically need commercial tooling or custom scripting on top of the open source scanners.
- Ongoing rule maintenance — community rule sets lag behind newly disclosed vulnerability classes more than actively maintained commercial rule feeds.
How should a team combine open source tools into something coherent?
Run a general scanner (Semgrep) for broad coverage across languages, add a language-specific linter for any language with strong security-focused options (Bandit, gosec), and gate CI on new critical/high findings rather than trying to clear the entire existing backlog at once. This gets meaningful SAST coverage at zero licensing cost, at the price of more integration and maintenance work than a commercial platform would require — a reasonable tradeoff for smaller teams, less so once the maintenance overhead starts costing more engineering time than a subscription would.
Safeguard's SAST/DAST product folds this kind of scanning together with SCA and reachability analysis in one pipeline, for teams that outgrow assembling and maintaining several open source tools separately; see the SAST tools shortlist in the academy for a broader comparison against commercial options.
FAQ
Is Semgrep completely free?
Semgrep's core engine and community rule set are open source and free; its Pro tier (which adds cross-file analysis and a larger proprietary rule set) is a paid product.
Can CodeQL be used on private repositories for free?
Not generally — free CodeQL usage on GitHub is scoped to public repositories; private repositories need GitHub Advanced Security, which is a paid add-on.
Do open source SAST tools produce more false positives than commercial ones?
It varies by tool and rule set rather than being a blanket rule, but commercial tools generally invest more in reachability and taint-tracking refinement specifically to reduce false positives at scale, which is part of what the licensing cost buys.
Should a team pick one open source tool or run several?
Running one general-purpose scanner plus a language-specific linter for your primary language(s) is a common, reasonably low-maintenance combination; running many overlapping tools tends to produce duplicate findings without proportional coverage gains.