Cross-Framework Analysis

Inkog uses a unified analysis engine that works across all supported frameworks, providing consistent security results regardless of which tools you use.

The Challenge

AI agent codebases span multiple languages and frameworks:

• Python agents built with LangChain, CrewAI, or AutoGen
• TypeScript frontends with LlamaIndex
• No-code workflows in n8n or Flowise
• Custom agents mixing multiple languages

Traditional security tools require separate analyzers for each, missing cross-boundary vulnerabilities.

How Inkog Solves This

Inkog analyzes all supported frameworks using the same detection rules:

Your Code → Inkog Analysis → Security Report

This means:

• Consistent detection - The same vulnerability patterns are caught whether you use LangChain or n8n
• Framework-agnostic rules - Security rules apply across all frameworks
• Cross-language analysis - Vulnerabilities that span Python and JavaScript are detected

What Gets Analyzed

Inkog examines:

• User input handling - How external data enters your agent
• LLM interactions - Prompts, completions, and tool calls
• Data flow - How information moves through your application
• Dangerous operations - Code execution, file access, database queries

Supported Input Types

Example

Whether your code looks like this:

# Python with LangChain
def chat(user_input):
    return llm.invoke(f"Answer: {user_input}")

Or this:

{
  "nodes": [{
    "type": "openai",
    "parameters": {
      "prompt": "={{ $json.body.message }}"
    }
  }]
}

Inkog detects the same prompt injection vulnerability in both.