SecKnowledge is an AI agent skill designed to equip AI assistants with systematic, expert-level knowledge for web and AI security testing. It targets AI assistants and security professionals by consolidating real-world vulnerability data, research documents, AI-specific risks, and industry-standard frameworks into an easily callable knowledge base. This allows AI to perform comprehensive security assessments, identify attack surfaces, suggest countermeasures, and cover complex AI security threats, effectively acting as an experienced security expert.

How It Works

This skill integrates diverse, high-volume security knowledge sources, including 88,636 WooYun vulnerability cases, over 5,600 Xianzhi research documents, 173 GAARM AI security risks across 5 domains, and OWASP LLM/ASI/WSTG frameworks. The knowledge is meticulously organized into 48 reference files, categorized by AI lifecycle stages (application, deployment, training) and specific risk types for both traditional web vulnerabilities and modern AI security threats. This structured approach enables AI assistants to access and apply systematic, battle-tested security knowledge on demand, facilitating deep dives into attack vectors and mitigation strategies.

Quick Start & Requirements

• Primary install: Clone the repository into the AI assistant's skills directory (e.g., git clone https://github.com/Pa55w0rd/secknowledge-skill.git ~/.claude/skills/secknowledge-skill).
• Prerequisites: Requires an AI assistant environment capable of loading and utilizing custom skills (e.g., Claude Code, Cursor). No specific hardware or software dependencies are listed beyond the AI platform.
• Setup Time: Minimal, primarily involving a git clone operation.

Highlighted Details

• Data Integration: Unifies 88,636 WooYun vulnerability cases, 5,600+ Xianzhi research documents, 173 GAARM AI security risks (5 domains), and OWASP LLM/ASI/WSTG frameworks.
• Comprehensive Scope: Covers traditional web exploits (SQLi, XSS, RCE, logic flaws, file security, modern protocols) and extensive AI security risks across application, model, data, identity, and baseline security domains.
• Structured Knowledge: Organizes information into 48 reference files, each designed for single-read comprehension, with a maximum file size of 589 lines.
• Methodology Framework: Incorporates core methodologies like attack surface identification, hypothesis validation, deep exploitation, defense rollback, and the "Vulnerability = Expected Behavior - Actual Behavior" formula.

Maintenance & Community

The project is maintained by author "Pa55w0rd". The latest version, v2.2.0 (2026-06-17), includes synchronization with the AISS 2026-06 snapshot of the GAARM matrix and the introduction of a Claw-like agent threat matrix. While community sources like AISS are referenced, direct links to community forums (Discord/Slack) or a public roadmap are not provided in the README.

Licensing & Compatibility

The project is released under the MIT License, which is permissive and generally allows for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The GAARM risk matrix is based on a "2026-06 snapshot," indicating potential for newer risks not yet incorporated. The content is explicitly stated to be for security research and defense reference only, requiring legal authorization for any actual security testing. The skill is primarily designed for AI assistant integration, and direct human utilization may require adaptation.