The last line of defence before your code hits git history
LocalForge serves as the last line of defense before code enters git history, functioning as a local pre-commit gate specifically designed for development teams utilizing AI coding assistants. This macOS application provides comprehensive security scanning that operates entirely on-device, ensuring no sensitive data ever leaves your machine while catching potential issues across multiple categories. The tool addresses the critical need for maintaining code quality and security in modern development workflows where AI-generated code can introduce unexpected vulnerabilities. By intercepting commits before they reach version control, LocalForge prevents embarrassing security breaches and maintains clean project histories without requiring cloud dependencies or internet connectivity.
Development teams face significant risks when AI coding tools inadvertently introduce hardcoded secrets, security vulnerabilities, or problematic patterns into their codebase. These issues can lead to serious consequences including data breaches, system compromises, and technical debt accumulation that slows down development velocity. Traditional security tools often operate post-commit or require cloud connectivity, creating windows of exposure and privacy concerns. LocalForge solves this by providing immediate, local scanning that catches problems at the source—before they become part of the permanent project record. This proactive approach ensures that security and quality issues are addressed during the development phase rather than requiring later remediation.
The Rust Regex layer represents the first major feature group, providing ultra-fast pattern matching that blocks commits in under 1 millisecond. This initial gate scans for 26 distinct secret patterns across 13 major providers including AWS, GCP, Azure, Stripe, GitHub, and Anthropic tokens. The system compiles these patterns once at boot time and performs lightning-fast matching against staged code changes, effectively catching obvious security violations without requiring complex analysis. This layer serves as the first line of defense by identifying clear violations like exposed API keys and SSH private keys, immediately blocking commits that contain these critical security risks before any deeper analysis occurs.
The CoreML and Apple Neural Engine layer constitutes the second feature group, providing machine learning-based detection with trained models that achieve a 0.754 F1 score. This intermediate layer analyzes code changes using CoreML models trained on 297 samples across 11 programming languages, leveraging Apple's Neural Engine for efficient on-device processing. Operating in approximately 200 milliseconds, this layer identifies more subtle patterns and potential security issues that might escape simple regex matching. The system blocks commits when this layer detects high-confidence matches, providing a secondary security gate that complements the initial regex scanning with more sophisticated pattern recognition capabilities.
Publisher
A
admin
Launch Date2026-06-21
Pricingfree