DryRun Security

DryRun Security

DryRun Security is an AI-powered code security analyzer that catches vulnerabilities in pull requests before merge.

Screenshots

DryRun Security screenshot

About DryRun Security

DryRun Security integrates directly into your development workflow as a GitHub App, providing real-time security analysis on every pull request. Unlike traditional security scanners that flood developers with generic alerts, DryRun uses Contextual Security Analysis to examine code changes with awareness of their actual impact and intent. This approach significantly reduces false positives and alert fatigue, allowing teams to focus on genuine security concerns rather than noise. The tool supports a wide range of modern development stacks including Python, Node.js, JavaScript, Go, Rails, Express, and Next.js, with continued expansion planned. It evaluates critical security dimensions including authentication and authorization mechanisms, sensitive codepaths and functions, code authorship patterns, and structural brittleness. By providing security context at the moment developers open their pull requests, DryRun transforms security from a bottleneck into an integrated part of the coding process. Developer velocity improves significantly because security reviews happen inline and instantly rather than as a separate, time-consuming gate. Teams gain a consistent protection layer across all repositories while maintaining rapid merge cycles. The tool's contextual understanding means developers receive actionable feedback tied directly to their specific code changes, making security guidance relevant rather than prescriptive.

Pros

👍 Reduces false positives through contextual analysis rather than generic rules 👍 Integrates seamlessly as GitHub App with instant pull request feedback 👍 Supports multiple languages and frameworks with ongoing expansion 👍 Accelerates development velocity by eliminating security review delays

Cons

👎 Limited to GitHub; integration with other version control systems unclear 👎 Effectiveness depends on pull request quality and code change clarity 👎 Requires organizational adoption and team trust in AI-driven decisions