CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?
Analyzing Open Source Intelligence (OSINT) from large volumes of data is critical for drafting and publishing comprehensive CTI reports. This process usually follows a three-stage workflow -- triage, deep search and TI drafting. While Large Language Models (LLMs) offer a promising route toward automation, existing benchmarks still have limitations. These benchmarks often consist of tasks that do not reflect real-world analyst workflows. For example, human analysts rarely receive tasks in the form of multiple-choice questions. Also, existing benchmarks often rely on model-centric metrics that emphasize lexical overlap rather than actionable, detailed insights essential for security analyst...
| Authors | Xiangsen Chen, Xuan Feng, Shuo Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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