CTI-REALM: Benchmark to Evaluate Agent Performance on Security Detection Rule Generation Capabilities

• URL: http://arxiv.org/abs/2603.13517v2
• Date: Tue, 17 Mar 2026 15:49:46 GMT
• Title: CTI-REALM: Benchmark to Evaluate Agent Performance on Security Detection Rule Generation Capabilities
• Abstract summary: CTI-REALM is a benchmark designed to evaluate AI agents' ability to interpret cyber threat intelligence (CTI) and develop detection rules.<n>This work demonstrates the potential of AI agents to support labor-intensive aspects of detection engineering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CTI-REALM (Cyber Threat Real World Evaluation and LLM Benchmarking) is a benchmark designed to evaluate AI agents' ability to interpret cyber threat intelligence (CTI) and develop detection rules. The benchmark provides a realistic environment that replicates the security analyst workflow. This enables agents to examine CTI reports, execute queries, understand schema structures, and construct detection rules. Evaluation involves emulated attacks of varying complexity across Linux systems, cloud platforms, and Azure Kubernetes Service (AKS), with ground truth data for accurate assessment. Agent performance is measured through both final detection results and trajectory-based rewards that capture decision-making effectiveness. This work demonstrates the potential of AI agents to support labor-intensive aspects of detection engineering. Our comprehensive evaluation of 16 frontier models shows that Claude Opus 4.6 (High) achieves the highest overall reward (0.637), followed by Claude Opus 4.5 (0.624) and the GPT-5 family. An ablation study confirms that CTI-specific tools significantly improve agent performance, a variance analysis across repeated runs demonstrates result stability. Finally, a memory augmentation study shows that seeded context can close 33\% of the performance gap between smaller and larger models.

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