CyberMaskQA is a new privacy-aware QA benchmark for cybersecurity that annotates private entities in realistic organizational scenarios with causal dependencies to jointly evaluate reasoning accuracy and masking performance.
Cyberbench: A multi-task benchmark for evaluating large language models in cybersecurity
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HIDBench unifies DARPA-E3, DARPA-E5, and NodLink datasets with a data pipeline to benchmark LLMs for host-based intrusion detection, showing high precision on simple logs but sharp drops in MCC and rises in false positives on complex noisy data.
citing papers explorer
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CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
CyberMaskQA is a new privacy-aware QA benchmark for cybersecurity that annotates private entities in realistic organizational scenarios with causal dependencies to jointly evaluate reasoning accuracy and masking performance.
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HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection
HIDBench unifies DARPA-E3, DARPA-E5, and NodLink datasets with a data pipeline to benchmark LLMs for host-based intrusion detection, showing high precision on simple logs but sharp drops in MCC and rises in false positives on complex noisy data.