DiagChain: New Benchmark for Evaluating LLM Agents in Attack Chain Reconstruction
A novel diagnostic standard known as DiagChain has been launched to assess large language model (LLM) agents in reconstructing evidence-based attack chains. This benchmark, outlined in a paper available on arXiv (2608.03591), fills a void in current evaluation techniques, which mainly concentrate on final results or overall accuracy, offering limited understanding of error origins and their progression through intermediate reasoning phases. DiagChain facilitates a stage-by-stage evaluation of LLM agents, allowing for detailed performance analysis. It features MAIN-69, a collection of 69 scenarios covering various operating systems, evidence noise levels, and chain lengths. Furthermore, the benchmark presents Evidence-Centric Retrieval-Augmented Generation (ECRAG), integrating evidence retrieval with a dynamic structured representation of the reconstructed chain and introducing five metrics to evaluate different reconstruction stages, aiding in systematic failure analysis. This research is significant for AI and technology, especially in cybersecurity and LLM applications.
Key facts
- DiagChain is a diagnostic benchmark for evaluating LLM agents in attack chain reconstruction.
- It enables stage-wise evaluation of LLM agents.
- The benchmark includes MAIN-69, a suite of 69 scenarios.
- Scenarios span multiple operating systems, evidence noise levels, and chain lengths.
- It introduces Evidence-Centric Retrieval-Augmented Generation (ECRAG).
- ECRAG couples evidence retrieval with an evolving structured representation of the reconstructed chain.
- Five complementary metrics are introduced to assess distinct stages of the reconstruction process.
- The paper is available on arXiv with ID 2608.03591.
Entities
Institutions
- arXiv