anthropics/claude-code-security-review
Claude Code Security Review
This project is an AI-powered static analysis tool and automated vulnerability scanner designed to detect security flaws such as injection and authentication bypasses. It uses large language models to perform semantic reasoning across multiple programming languages, identifying vulnerabilities within code changes.
The tool operates as a GitHub Action that integrates into continuous integration pipelines to analyze pull request diffs. It focuses on modified lines of code to target new risks and reports findings by posting automated comments directly to the pull request.
Analysis is directed by customizable security policies and external rule injection, allowing for project-specific instructions. These custom rules and filters are used to reduce noise and discard low-impact findings to prioritize high-confidence security risks.
Features
- LLM-Based Analysis - Uses large language models to evaluate code intent and semantic context to identify security flaws.
- Vulnerability Scanners - Provides an automated scanner that identifies security risks and insecure coding practices within source code using semantic reasoning.
- AI-Powered Code Analysis Tools - Utilizes large language models to analyze source code for complex security vulnerabilities like injection attacks.
- Diff-Based Change Isolation - Analyzes only modified code blocks within a diff to target new vulnerabilities for AI evaluation.
- Vulnerability Reporting Integrations - Notifies developers of detected vulnerabilities by posting automated comments directly onto pull requests.
- Vulnerability Review Scanners - Scans code changes in pull requests for security issues and displays results during the review process.
- GitHub Actions Workflows - Operates as a managed workflow that triggers on pull requests and communicates via automated API comments.
- Security Linters - Implements a customizable AI-driven security linter that applies project-specific instructions and filters to reduce noise during analysis.
- Security Vulnerability Scanning - Scans source code differences in pull requests to detect common security flaws and vulnerabilities.
- AI-Powered Code Auditing - Uses large language models to scan code for complex security vulnerabilities such as authentication bypasses.
- Pull Request Vulnerability Scanning - Analyzes specific code changes in a diff to identify new security risks without scanning the entire codebase.
- CI Pipeline Integration - Integrates automated vulnerability scanning directly into the continuous integration build process.
- External Rule Management - Loads behavioral constraints and decision logic from external text files to steer the AI analysis process.
- Security Scanning Rules - Directs the analysis process by adding project-specific security instructions and filtering rules through external files.
- AI Scan Policies - Applies project-specific rules and filters to AI security scans to reduce false positives.
- Security Scan Policy Enforcers - Applies project-specific rules and filters to AI scans to reduce false positives and prioritize critical findings.
- False Positive Filtering - Applies custom constraints to discard low-impact findings and focus on high-confidence security risks.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
