The SECURE benchmark can be extended to evaluate Large Language Models (LLMs) in other security domains by incorporating domain-specific datasets and tasks that reflect the unique challenges and requirements of those areas. For instance, in the realm of network security, the benchmark could include datasets focused on intrusion detection, threat intelligence, and incident response. Tasks could be designed to assess LLMs' abilities to extract relevant information from network logs, understand attack patterns, and reason about potential vulnerabilities in network configurations.
Additionally, the benchmark could be adapted for application security by creating datasets that focus on common vulnerabilities and exposures (CVEs) related to software development, such as SQL injection, cross-site scripting, and buffer overflows. Tasks could involve evaluating LLMs' capabilities in code review, vulnerability assessment, and remediation strategies.
To ensure comprehensive coverage, collaboration with domain experts in various security fields is essential. This collaboration would help in curating high-quality datasets and defining relevant tasks that accurately reflect the complexities of each domain. Furthermore, integrating real-world scenarios and case studies into the benchmark would enhance its applicability and relevance, allowing LLMs to be evaluated in contexts that closely mimic actual security challenges faced by organizations.