CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
arXiv:2605.24765v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to cybersecurity question answering (QA) for critical tasks such as incident response and vulnerability analysis. However, real-world operational contexts, including system logs and network configurations, inherently contain sensitive identifiers, e.g., IP addresses, host names, and user accounts. Processing this data with cloud-based models is often unsafe or infeasible in regulated environments. Furthermore, progress in privacy-preserving QA is hindered by the lack of annotated, context-ri
The proliferation of LLMs in critical enterprise functions, coupled with increasing data privacy regulations, makes the development of privacy-aware benchmarks for sensitive domains like cybersecurity essential.
This benchmark directly addresses the tension between leveraging powerful AI models for cybersecurity and maintaining data privacy, which is a significant barrier to LLM adoption in regulated industries.
The availability of CyberMaskQA will enable more accurate evaluation and development of privacy-preserving LLMs, potentially accelerating their secure deployment in sensitive operational environments.
- · AI/ML researchers in privacy and security
- · Cybersecurity solution providers
- · Enterprises with strong data privacy requirements
- · Cloud-based LLM providers without robust privacy solutions
- · Organizations with inadequate data anonymization practices
Improved security posture through LLM-driven incident response and vulnerability analysis becomes more accessible for regulated entities.
Increased demand for on-premise or federated learning LLM solutions that process sensitive data locally.
Enhanced regulatory scrutiny on LLM training data and deployment practices, leading to new compliance standards for AI in critical infrastructure.
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Read at arXiv cs.LG