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CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs

Xi Jiang, Aaron Gember-Jacobson, Nick Feamster

TL;DR

A Context-Aware Iterative Prompting (CAIP) framework is introduced that automates network-specific context extraction and optimizes LLM prompts for more precise router misconfiguration detection, uncovering over 20 previously undetected misconfigurations in real-world configurations.

Abstract

Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A models have emerged as a promising alternative, allowing users to query partitions of configurations through prompts and receive answers based on learned patterns, thanks to transformer models pre-trained on vast datasets that provide generic configuration context for interpreting router configurations. Yet, current methods of partition-based prompting often do not provide enough network-specific context from the actual configurations to enable accurate inference. We introduce a Context-Aware Iterative Prompting (CAIP) framework that automates network-specific context extraction and optimizes LLM prompts for more precise router misconfiguration detection. CAIP addresses three challenges: (1) efficiently mining relevant context from complex configuration files, (2) accurately distinguishing between pre-defined and user-defined parameter values to prevent irrelevant context from being introduced, and (3) managing prompt context overload with iterative, guided interactions with the model. Our evaluations on synthetic and real-world configurations show that CAIP improves misconfiguration detection accuracy by more than 30% compared to partition-based LLM approaches, model checkers, and consistency checkers, uncovering over 20 previously undetected misconfigurations in real-world configurations.

CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs

TL;DR

A Context-Aware Iterative Prompting (CAIP) framework is introduced that automates network-specific context extraction and optimizes LLM prompts for more precise router misconfiguration detection, uncovering over 20 previously undetected misconfigurations in real-world configurations.

Abstract

Model checkers and consistency checkers detect critical errors in router configurations, but these tools require significant manual effort to develop and maintain. LLM-based Q&A models have emerged as a promising alternative, allowing users to query partitions of configurations through prompts and receive answers based on learned patterns, thanks to transformer models pre-trained on vast datasets that provide generic configuration context for interpreting router configurations. Yet, current methods of partition-based prompting often do not provide enough network-specific context from the actual configurations to enable accurate inference. We introduce a Context-Aware Iterative Prompting (CAIP) framework that automates network-specific context extraction and optimizes LLM prompts for more precise router misconfiguration detection. CAIP addresses three challenges: (1) efficiently mining relevant context from complex configuration files, (2) accurately distinguishing between pre-defined and user-defined parameter values to prevent irrelevant context from being introduced, and (3) managing prompt context overload with iterative, guided interactions with the model. Our evaluations on synthetic and real-world configurations show that CAIP improves misconfiguration detection accuracy by more than 30% compared to partition-based LLM approaches, model checkers, and consistency checkers, uncovering over 20 previously undetected misconfigurations in real-world configurations.

Paper Structure

This paper contains 25 sections, 12 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Method
  4. Context Mining
  5. Challenge 1 - Efficient and accurate context mining
  6. Neighboring Configuration Mining:
  7. Similar Configuration Mining:
  8. Referenceable Configuration Mining:
  9. Challenge 2 - Referenceable Parameter Value Ambiguity
  10. Iterative Prompting
  11. Challenge 3 - Context Overload in Prompting
  12. Why Iterative, Requested Based Prompting Works
  13. Evaluation
  14. Evaluation Setup
  15. Context Extraction Hardware Setup
  16. ...and 10 more sections

Figures (5)

  • Figure 1: CAIP system overview.
  • Figure 2: Example snippet of tree-formatted Junpiter router configuration file.
  • Figure 3: Example context mined on selected config line.
  • Figure 4: Example: initial prompting and LLM context request response for detecting syntax misconfiguration.
  • Figure 5: Example: Adding requested context and retrieving misconfiguration detection result.