i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler

📰 ArXiv cs.AI

Learn how to use i-EXAM, a tool that helps system administrators create security profiles and identify network hardening strategies using planning compilation and Large Language Models

advanced Published 8 Jul 2026
Action Steps
  1. Install i-EXAM and configure it for your network
  2. Use i-EXAM to generate attack connectivity graphs and identify potential vulnerabilities
  3. Perform what-if analyses to evaluate different security metrics and hardening strategies
  4. Apply i-EXAM's explanations in natural language to understand and justify recommended hardening strategies
  5. Test and compare the effectiveness of different hardening strategies using i-EXAM's planning compilation
Who Needs to Know This

Security teams and system administrators can benefit from i-EXAM to analyze and harden their networks against potential attacks

Key Insight

💡 i-EXAM leverages planning compilation and Large Language Models to provide soundness and completeness guarantees for identifying attack paths and generating hardening strategies

Share This
🚀 Introducing i-EXAM: a planning-powered tool for creating security profiles and identifying network hardening strategies #cybersecurity #networksecurity

Key Takeaways

Learn how to use i-EXAM, a tool that helps system administrators create security profiles and identify network hardening strategies using planning compilation and Large Language Models

Full Article

Title: i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler

Abstract:
arXiv:2607.05888v1 Announce Type: cross Abstract: i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strategies. It leverages planning compilation that provides soundness and completeness guarantees to identify attack paths, evaluate security metrics, generate diverse hardening strategies, and explain these strategies in natural language using Large Language Models.
Read full paper → ← Back to Reads

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