CAIP

Context-aware iterative prompting for detecting router misconfigurations with LLMs

CAIP

Overview

CAIP (Context-Aware Iterative Prompting) is a framework that leverages large language models (LLMs) to automatically detect router misconfigurations. It addresses the limitations of traditional model checkers and existing LLM-based approaches by efficiently extracting network-specific context and optimizing prompts for accurate misconfiguration detection.

The Problem

Network operators face significant challenges in detecting router configuration errors:

  • Manual Development: Traditional model checkers and consistency checkers require substantial manual development and maintenance
  • Limited Context: Existing LLM-based partition prompting methods don’t provide enough network-specific context from actual configurations
  • Scalability: Configuration files are complex and require intelligent context extraction
  • Accuracy: Current automated approaches often miss real-world misconfigurations

What CAIP Does

CAIP automates the detection of router misconfigurations through three key innovations:

  1. Efficient Context Extraction: Automatically extracts relevant network-specific context from configuration files
  2. Parameter Distinction: Distinguishes between pre-defined and user-defined parameters to avoid irrelevant context
  3. Iterative Prompting: Manages prompt complexity through guided, iterative model interactions

Key Features

  • Automated Analysis: No manual rule development required
  • Context-Aware: Extracts and leverages network-specific configuration context
  • LLM-Powered: Uses large language models for intelligent configuration analysis
  • Iterative Refinement: Guides LLM through complex configuration analysis step-by-step
  • Real-World Validation: Tested on actual router configurations

Use Cases

  • Router configuration validation
  • Network security auditing
  • Configuration change review
  • Automated policy compliance checking
  • Network troubleshooting and debugging

Results

  • 30%+ improvement in detection accuracy over partition-based LLM approaches, model checkers, and consistency checkers
  • 20+ previously undetected misconfigurations identified in real-world configurations
  • Successfully handles complex, real-world router configurations

Resources

Citation

@article{jiang2024caip,
  title={CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs},
  author={Jiang, Xi and Gember-Jacobson, Aaron and Feamster, Nick},
  journal={arXiv preprint arXiv:2411.14283},
  year={2024}
}