ai-ide-source-audit

Name: ai-ide-source-audit
Availability: InStock
Author: Mindgard

Audit open-source AI IDE source code across six prioritized security targets.

61|8|Updated Feb 16, 2026

Search Tags:#security #audit #mcp #static-analysis #source-code #ai-ide #command-filtering

Ready to run

npx skills add https://github.com/Mindgard/ai-ide-skills --skill ai-ide-source-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-ide-source-audit
Source: https://github.com/Mindgard/ai-ide-skills/tree/main/skills/ai-ide-source-audit
Command: npx skills add https://github.com/Mindgard/ai-ide-skills --skill ai-ide-source-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Open-source AI IDE source code is large; this skill focuses security auditing on the most impactful targets to identify vulnerabilities quickly, reducing time to find critical issues.

Core Features & Use Cases

Prioritized six audit targets to guide security reviews: Config auto-loading, Command execution pipeline, MCP integration, File-write permission model, Prompt/rules loading, and Output rendering.
Provides practical workflows for open-source AI IDEs and recommended tooling (Semgrep, CodeQL) to detect common vulnerability patterns.
Use cases include auditing projects like Cline, Continue, Roo Code, and other AI coding agents to improve trust and safety.

Quick Start

Audit the target AI IDE's source code focusing on prioritized targets like command filtering, MCP integration, and file-write permissions to uncover high-risk weaknesses.

Frequently Asked Questions about ai-ide-source-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I audit AI IDE source code for security vulnerabilities?▼

Auditing AI IDE source code involves tracing data flows from workspace content to execution sinks using static analysis patterns. It targets command execution pipelines, MCP integration, and file-write permissions to identify high-risk weaknesses in open-source AI coding agents.

What are the most critical security targets in open-source AI coding agents?▼

The most critical security targets in open-source AI coding agents are config auto-loading, command execution pipelines, MCP integration, file-write permission models, prompt/rules loading, and output rendering. These six areas guide focused analysis across core components to uncover severe vulnerabilities.

How do I trace data flows to execution sinks in an AI IDE?▼

You can trace data flows to execution sinks by applying static analysis patterns and references to map how workspace content reaches command execution pipelines and output rendering. This reveals how untrusted input might trigger unauthorized actions within the AI IDE.

Can I use static analysis to find command filtering issues in MCP integration?▼

Static analysis is highly effective for finding command filtering issues in MCP integration. By applying specific patterns to trace data flows from workspace content to execution sinks, you can detect where untrusted input bypasses command filtering in the Model Context Protocol setup.

Does this security audit approach work for projects like Cline and Continue?▼

Yes, this security audit approach works for projects like Cline, Continue, and Roo Code. It provides practical workflows for open-source AI IDEs, applying static analysis to identify common vulnerability patterns across their command execution and file-write permission models.

What is the best way to review file-write permission models in AI IDEs?▼

The best way to review file-write permission models in AI IDEs is to apply static analysis patterns to trace data flows from workspace content to file-write sinks. This focused analysis identifies potential unauthorized file modifications within the AI coding agent's architecture.