cy from days to minutes

Core Solution

The proof-of-commitment tool implements a behavioral risk engine that maps structural conditions rather than scanning source code. It operates as an MCP server, CLI, and GitHub Action, integrating directly into CI/CD and dependency management workflows.

Architecture Decisions:

• Zero-Install Audit Model: Parses package-lock.json or npm-shrinkwrap.json without executing package installation, eliminating runtime attack surface
• Behavioral Scoring Algorithm: Evaluates maintainer count, release cadence, download velocity, and token exposure windows to generate a 0–100 risk score
• Provenance & Signature Verification: Leverages npm audit signatures to validate package authenticity where available
• Lock-File Pinning Enforcement: Detects unbounded version ranges that allow silent transitive updates

Implementation & Usage:

npx proof-of-commitment express    # scores express + direct deps
npx proof-of-commitment --file package-lock.json  # scores full tree

The scoring model is explicitly behavioral, not code-scanning. It identifies structural conditions that correlate with successful supply chain attacks without predicting specific compromise timelines. Results feed into monitoring pipelines that alert on unexpected version bumps from sole-maintainer packages, enabling proactive mitigation before token compromise materializes.

Pitfall Guide

1. Assuming Staleness Equals Security: Long periods without updates indicate low change frequency, not immunity. The active npm token remains a live attack vector regardless of code stability.
2. Over-Reliance on npm audit and CVE Databases: Traditional scanners only flag known code vulnerabilities. They are structurally blind to credential exposure, maintainer concentration, and behavioral risk signals.
3. Neglecting Token Rotation and Provenance Verification: Failing to rotate npm tokens or validate package signatures leaves high-download packages exposed to single-point credential compromise. Always run npm audit signatures where supported.
4. Ignoring Version Pinning in Lock Files: Unbounded semver ranges allow transitive dependencies to silently update to malicious versions pushed via compromised tokens. Strict pinning is non-negotiable for critical infrastructure.
5. Single-Maintainer Dependency Blind Spot: Relying on packages with sole maintainers for foundational tooling (e.g., rimraf, chokidar, @types/node) creates unmitigated supply chain risk. Map these explicitly and monitor for unexpected version changes.
6. Treating Risk Scores as Absolute Verdicts: Behavioral scores indicate structural conditions correlating with attack vectors, not definitive predictions of compromise. They require contextual monitoring and operational response playbooks.

Deliverables

Supply Chain Structural Risk Blueprint

• Token exposure mapping methodology for sole-maintainer packages
• Behavioral scoring integration patterns for CI/CD pipelines
• Lock-file pinning strategy and transitive dependency isolation framework
• Monitoring architecture for unexpected version bumps and provenance validation

Pre-Deployment Dependency Audit Checklist

• Run npx proof-of-commitment --file package-lock.json and flag all CRITICAL (<70) structural scores
• Verify npm provenance and signatures for high-download sole-maintainer packages
• Enforce strict version pinning in lock files; remove unbounded semver ranges
• Configure alerting for unexpected version publications from packages with >50M weekly downloads
• Document token rotation schedules and publish access controls for internal and third-party dependencies
• Validate that behavioral risk scores are integrated into release gates, not treated as post-deployment diagnostics