LLMs for Cybersecurity

This field studies how large language models can help identify and explain vulnerabilities, interpret threat intelligence and security logs, and support tasks such as incident response and penetration testing. Common approaches include retrieval-augmented generation, cybersecurity domain adaptation, multi-agent workflows, and integration with static analysis, command-line tools, fuzzers, or execution-based verification. Evaluation spans code datasets, CTI and log benchmarks, and interactive cyber ranges, where results often depend on data quality, tool access, prompts, and scoring methods. Models can assist with specific tasks, but unreliable reasoning, weak generalization to real systems, and the dual-use risks of offensive capability remain central challenges.