The use of Large Language Models (LLMs) in bug bounty hunting has emerged as a transformative approach, significantly enhancing vulnerability detection and threat analysis through automation and real-time intelligence. LLMs like GPT-4 provide powerful tools for identifying security flaws, generating test cases, and supporting continuous monitoring. However, these models are not without risks; they are vulnerable to specific attacks such as data poisoning, model inversion, and adversarial inputs. Addressing these vulnerabilities through advanced defensive strategies is crucial to securely integrating LLMs into cybersecurity frameworks while maximizing their benefits.
