Advanced Techniques with RATS — Rough Auditing Tool for SecurityRATS (Rough Auditing Tool for Security) is a lightweight, open-source static analysis tool originally designed to scan source code for common vulnerabilities quickly. While its default checks are useful for catching low-hanging fruit, security engineers and developers can get significantly more value by using advanced techniques: customizing rules, integrating RATS into CI/CD pipelines, combining it with other tools, and tuning analysis for large codebases. This article covers practical strategies and examples to raise RATS from a basic scanner to a powerful part of your secure-development toolset.
What RATS does well (and its limitations)
RATS excels at fast pattern-based detection of common insecure coding patterns across multiple languages (C, C++, Java, Perl, Python, PHP, etc.). It uses a rule set of regular expressions and simple heuristics to flag potential vulnerabilities like command injection, buffer overflow sources, unsafe use of system calls, and insecure file-handling.
- Strengths:
- Fast, low overhead scans suitable for inclusion in pre-commit hooks and CI.
- Multi-language support with consistent output.
- Easily extensible rule set via configuration files.
- Limitations:
- High false-positive rate due to simple pattern matching.
- Limited contextual understanding compared to full AST-based or taint-analysis tools.
- Not sufficient as a sole security solution; best used in combination with deeper analysis tools and manual review.
Setting goals before applying advanced techniques
Before changing your RATS setup, define clear objectives so your configuration supports them:
- Reduce noisy alerts to focus on actionable issues.
- Catch specific classes of vulnerabilities (e.g., command injection, SQL injection).
- Ensure scans run fast enough to integrate into developer workflows.
- Provide clear guidance and remediation steps for developers.
Customizing and extending RATS rules
One of the most powerful ways to improve RATS accuracy is tailoring its rule database for your codebase.
- Locate the ruleset (usually in a rules/ or etc/ directory).
- Create project-specific rule files to:
- Add new regex patterns for proprietary APIs or frameworks.
- Modify existing patterns to reduce false positives (e.g., require surrounding context tokens).
- Disable rules that are irrelevant to your environment or languages in use.
Example approach:
- Identify recurring false positives by reviewing a week’s worth of findings.
- For each pattern, craft a stricter regex or add negative-lookahead to ignore safe usages.
- Tag rules with severity and confidence metadata (if your local process supports it).
Tip: Keep a version-controlled rules repository and include tests (sample code) that a rule should detect or ignore.
Reducing false positives with context and whitelists
RATS’s simple matching can be improved with project-level context:
- Maintain a whitelist of known-safe files, libraries, or code regions.
- Use inline suppression comments in source code for intentional, reviewed exceptions. Establish a short, standardized format for suppressions (e.g., /* RATS:IGNORE – reason, ticket# */) and require link to a tracking ticket.
- Post-process RATS output with a script to correlate findings against a maintained whitelist database or code ownership metadata (so issues are routed only to relevant teams).
Combining RATS with other tools for layered analysis
RATS is most effective when paired with complementary tools:
- Use RATS for fast, broad scans and a taint-analysis or AST-based scanner (e.g., Semgrep, CodeQL, Flawfinder, or commercial SAST) for deep contextual findings.
- Run RATS as a first-pass filter in CI to catch obvious issues quickly, and schedule deeper scans nightly.
- Aggregate outputs into a single dashboard (SIEM, SAST management tool, or issue tracker) so triage is centralized.
Comparison: RATS vs. deeper tools
| Tool type | Strength | Typical role |
|---|---|---|
| RATS (pattern-based) | Fast, multi-language; good for initial filtering | Pre-commit/quick CI checks |
| AST/taint-based tools (Semgrep, CodeQL) | Context-aware, fewer false positives | In-depth analysis, nightly/PR checks |
| Dynamic analysis (DAST, fuzzers) | Finds runtime issues not visible statically | QA/staging testing |
Integrating RATS into CI/CD pipelines
Practical CI integration tips:
- Run RATS on changed files only in pull requests to save time, and run full-repo scans on a schedule.
- Fail the build on new critical-severity findings; for lower severities, create issues automatically but don’t block merges.
- Normalize RATS output to a machine-readable format (JSON or CSV) with a wrapper script, then feed it into your pipeline’s reporting tools or issue tracker.
- Example pipeline steps:
- Checkout code and install RATS.
- Run RATS limited to changed files: rats –diff-branch=main (or run a custom script).
- Filter against whitelist and suppression rules.
- Post results to PR as a comment and create issues for high-severity findings.
Tuning performance for large repositories
Large mono-repos or legacy codebases require careful tuning to keep scans fast and actionable.
- Scope scans: focus on active modules or recently modified files.
- Parallelize scanning across cores or split by language directories.
- Incremental scanning: maintain a cache of previous scan timestamps and only rescan files that changed since the last run.
- Archive or explicitly mark third-party vendor code as excluded.
Creating developer-friendly reports and remediation guidance
Reduce friction by delivering clear, actionable findings:
- Map each RATS rule to a short, plain-language description and remediation steps.
- Include example vulnerable code and a corrected version for high-value rules.
- Provide links to internal secure-coding guidelines and relevant OWASP/CWE entries.
- Use automated issue templates populated with RATS output to help triage and assign work.
Using RATS for threat modeling and secure code reviews
RATS output can inform higher-level security activities:
- Feed recurring patterns into threat models to highlight risky components or APIs.
- Use RATS findings as pointers during manual code reviews—treat them as leads, not definitive proof.
- Track metrics over time: number of findings per sprint, rule-specific trends, and mean-time-to-fix.
Example: tightening a rule to reduce false positives
Suppose RATS flags uses of exec() broadly. If your codebase always validates inputs using a specific sanitize_cmd() helper, modify the exec rule to require detection of exec without a preceding sanitize_cmd call within a small token window, or add a suppression pattern for sanitize_cmd usage. Maintain test snippets showing the safe and unsafe cases so rule changes are validated automatically.
Governance, workflow, and developer buy-in
For sustained value, pair tooling with process:
- Define ownership: who configures rules, triages findings, and approves suppressions.
- Require a documented review and ticket for each suppression.
- Run periodic training showing common RATS findings and fixes.
- Celebrate reductions in false positives and faster remediation times.
When to stop relying on RATS
RATS should not be your only SAST mechanism. If your project handles sensitive data, financial transactions, or safety-critical functionality, introduce more sophisticated static and dynamic tools and ensure manual security reviews. Use RATS as a first-line, low-cost detector, not the final arbiter.
Conclusion
By customizing rules, reducing false positives with context, integrating RATS into CI/CD, combining it with deeper analysis tools, and aligning processes and governance, you can elevate RATS from a basic scanner to a practical, high-value component of your security program. Properly tuned, it accelerates developer feedback, surfaces common issues early, and feeds higher-fidelity tools and manual reviews with focused leads.
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