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Regex Pattern Security Audit Tool
A static analysis and linting tool that scans regex patterns in codebases for security-relevant bugs: Unicode-aware word boundaries in PII detection, incorrect character classes (literal pipes in ranges), greedy matching that causes partial matches, and other common regex pitfalls that create security blind spots.
Why this matters
You write regex patterns for input validation, PII detection, or firewall rules. You use \b for word boundaries because that is what every tutorial shows. You do not realize that \b is Unicode-aware in Python, meaning your email validator silently fails when text contains non-ASCII characters. You also do not notice that your character class [A-Z|a-z] accidentally includes a literal pipe character. These bugs ship to production and create security gaps that are nearly impossible to discover through normal testing because they only manifest with specific Unicode inputs.
- · Built for DevSecOps teams and security-conscious engineering organizations that use regex for input validation, PII detection, firewall rules, or data classification.
- · Most likely monetization: Freemium CLI tool with paid SaaS dashboard for enterprise codebase scanning and compliance reporting.
The Pain · Narrative
You write regex patterns for input validation, PII detection, or firewall rules. You use \b for word boundaries because that is what every tutorial shows. You do not realize that \b is Unicode-aware in Python, meaning your email validator silently fails when text contains non-ASCII characters. You also do not notice that your character class [A-Z|a-z] accidentally includes a literal pipe character. These bugs ship to production and create security gaps that are nearly impossible to discover through normal testing because they only manifest with specific Unicode inputs.
Score Breakdown
Market Signal
Go-to-Market
DevSecOps engineers at mid-to-large companies who run automated code scanning in CI pipelines and need to catch regex-based security bugs before deployment
~15,000 engineering teams with formal DevSecOps practices who use SAST tools in CI
GitHub Marketplace as a GitHub Action, supplemented by content marketing targeting security engineering blogs
$49/month for teams (up to 25 repos), $299/month for enterprise with custom rules and compliance reporting
500 GitHub Actions installs and 10 paying teams within 60 days of launch
MVP Scope · 1–2 weeks
- Build a regex pattern parser that extracts patterns from Python source files using AST analysis
- Implement detection rules for the top 5 regex security pitfalls: Unicode \b in PII patterns, incorrect character classes, greedy matching risks, catastrophic backtracking, and ReDoS vulnerabilities
- Create a CLI tool that scans a directory and outputs findings with severity, location, and suggested fix
- Build a rule engine architecture that allows adding new detection rules without modifying core logic
- Write test cases for each rule using real-world patterns from open-source PII detection libraries
- Package as a GitHub Action that runs on pull requests and posts inline review comments on flagged patterns
- Add support for JavaScript/TypeScript regex patterns to expand addressable market
- Create a web dashboard showing scan results across repositories with trend tracking
- Implement auto-fix suggestions for common issues like replacing \b with ASCII lookarounds
- Publish to GitHub Marketplace and submit to the VS Code extension marketplace for inline editor warnings
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Existing SAST tools like Semgrep, SonarQube, and CodeQL could add regex security rules to their existing rule sets, instantly reaching a much larger user base
- 2The specific bugs discovered in this discussion are relatively obscure, and convincing developers they need a dedicated regex security linter requires significant education
- 3Regex patterns are often generated dynamically or stored in configuration files, making static analysis incomplete and reducing the tool's perceived value
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Four distinct regex bugs were identified in a single PII detection module: Unicode-aware word boundaries causing detection failures, a literal pipe character in a TLD character class, greedy matching causing partial email leaks, and inconsistent detection based on Unicode category of adjacent characters. Multiple independent libraries were found to share the same flawed patterns, suggesting these bugs are systemic and copy-paste propagated. The pattern of bugs indicates a class of regex security issues that automated tooling could catch.
Action Plan
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Recommended Next Step
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Landing Page Copy Kit
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Headline
Regex Pattern Security Audit Tool
Sub-headline
A static analysis and linting tool that scans regex patterns in codebases for security-relevant bugs: Unicode-aware word boundaries in PII detection, incorrect character classes (literal pipes in ranges), greedy matching that causes partial matches, and other common regex pitfalls that create security blind spots.
Who It's For
For DevSecOps teams and security-conscious engineering organizations that use regex for input validation, PII detection, firewall rules, or data classification
Feature List
✓ Static analysis of regex patterns for Unicode boundary issues, incorrect character classes, and greedy matching risks ✓ Security-focused pattern linting with severity ratings and suggested fixes ✓ Codebase scanner that finds all regex patterns and flags security-relevant ones ✓ CI/CD integration with GitHub Actions and pre-commit hooks ✓ Custom rule engine for organization-specific regex security policies
Where to Validate
Share your landing page in r/GitHub · langchain-ai/langchain — that's exactly where these pain points were discovered.
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