Comprehensive AI/LLM Security Skill for Secure Applications

Comprehensive AI/LLM Security Skill for Secure Applications

This AI agent skill is designed to conduct exhaustive security assessments for applications integrating language learning models (LLMs). It aids in evaluating AI systems with focus on prompt injection vulnerabilities, retrieval-augmented generation (RAG) security, agent and tool permissioning, model supply chain integrity, LLM red teaming, AI governance, evaluation design, data leakage prevention, jailbreak testing, and overall secure AI application reviews.

What this skill does

• Inventories AI systems to detail models, prompts, tools, RAG sources, memory, logs, user roles, secrets, data classes, and downstream actions.
• Models threats by assessing trust boundaries from user input to prompt, content retrieval to model, model output to tools, tool output to users, and logs/traces to operators.
• Tests high-risk paths, including direct and indirect prompt injection, data exfiltration, insecure tool invocation, agent over-permissions, policy-altering jailbreaks, and model/provider key leakage.
• Recommends security controls, such as least-privilege tool scopes, allowlisted schemas, retrieval filtering, secret redaction, output validation, human approval for critical actions, and continuous evaluation.

Who it is for

This skill benefits developers and teams working with AI coding agents like Claude Code, Cursor, and Codex. It is particularly useful for those responsible for security assessments and governance of AI-driven applications.

Use cases

• Conducting security assessments for AI applications and agents.
• Implementing governance frameworks and evaluation designs for LLMs.
• Performing red team exercises to test model robustness and resistance to adversarial attacks.
• Ensuring secure integration of retrieval-augmented generation systems.

Technical details

• Utilizes AI agent skills within Claude Code, Cursor, and Codex environments.
• Incorporates AI/LLM security review frameworks to ensure comprehensive threat assessment.
• Supports evaluation and recommendation of control measures to mitigate identified risks.

For authorised security testing, defensive research and educational use only.

Source & Licence

This package is built on open-source work published by 26zl (26zl/cybersec-toolkit) and distributed under MIT. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

• Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
• Format: ZIP archive containing the skill files, documentation and the original licence.
• Support: support@mcpcart.com — we aim to reply within 2 business days.
• Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.