SkillShield

Product Introduction

1. Definition: SkillShield is a specialized security validation platform in the AI/ML development ecosystem. It operates as a technical directory that scans GitHub/GitLab repositories containing SKILL.md files (standardized manifests for AI skills) through automated four-layer security audits.
2. Core Value Proposition: It solves critical AI supply chain risks by providing quantifiable security trust scores (0-100) for third-party AI capabilities, enabling developers to integrate vetted components while helping skill creators validate their code against emerging LLM-specific threats.

Main Features

1. Four-Layer Security Analysis:

   • How it works: Scans SKILL.md files sequentially through:
     • Manifest Checks: Validates metadata integrity and configuration hygiene.
     • Static Code Analysis: Inspects source code for hardcoded secrets, insecure APIs, and prompt injection vulnerabilities using SAST tools.
     • Dependency Scanning: Audits pip/npm packages for known CVEs via SCA tools.
     • LLM Behavioral Tests: Executes adversarial prompts to detect hallucination risks or data leakage.
   • Technology: Combines OWASP ZAP, Semgrep, DependencyTrack, and proprietary Red Council LLM probes.

2. Real-Time Trust Scoring:

   • Generates dynamic 0-100 security scores based on weighted vulnerability severity (critical findings = -20 points). Scores update with each commit via webhook-triggered rescans.

3. Security Badge System:

   • Awards verifiable SVG badges for repositories scoring ≥80. Badges include expiration timestamps and direct links to audit reports, enabling trust signaling in README files.

4. Vulnerability Dashboard:

   • Tracks 6,300+ identified findings across 10,000+ skills, categorizing risks by type (e.g., "LLM data exfiltration," "unsafe deserialization") and severity (critical/high/medium).

Problems Solved

1. Pain Point: Eliminates blind trust in open-source AI components by detecting supply chain attacks, malicious code injections, and emergent LLM exploits before integration.
2. Target Audience:
   • AI Developers integrating third-party skills into RAG pipelines.
   • DevSecOps teams managing AI deployment compliance.
   • Enterprise architects vetting AI vendors.
3. Use Cases:
   • Validating Hugging Face model dependencies pre-deployment.
   • Auditing AI chatbot extensions for financial services.
   • Certifying skills in government-regulated AI systems.

Unique Advantages

1. Differentiation: Unlike generic code scanners (e.g., Snyk), SkillShield specializes in LLM-specific threats via behavioral tests and SKILL.md standardization, covering risks traditional tools miss.
2. Key Innovation: Patent-pending LLM behavioral layer simulating prompt injection, jailbreaking, and output manipulation attacks unique to generative AI workflows.

Frequently Asked Questions (FAQ)

1. How does SkillShield calculate security scores?
   Scores combine weighted results from all four audit layers, with critical vulnerabilities (e.g., RCE risks) causing largest deductions. Scores ≥80 earn "Trusted" status.

2. What types of vulnerabilities does SkillShield detect?
   Identifies 37+ risk categories including dependency CVEs, prompt leaks, training data poisoning, insecure OAuth implementations, and LLM jailbreak susceptibility.

3. Is SkillShield free for open-source projects?
   Yes, public GitHub/GitLab repos with SKILL.md files can be scanned free, with optional paid tiers for private repos and continuous monitoring.

4. How often are skills rescanned for new threats?
   All skills automatically re-audited weekly; real-time scans trigger on repository commits via integrated webhooks.

5. Can SkillShield integrate into CI/CD pipelines?
   Yes, via Red Council's API to enforce security score thresholds (e.g., block builds scoring <70) and generate compliance reports.