The field of cybersecurity is rapidly evolving, with a growing focus on the development of AI-driven tools and techniques for vulnerability assessment and exploitation. Recent research has highlighted the potential for large language models (LLMs) to be used in cybersecurity applications, including the generation of proof-of-concept exploits and the evaluation of malware classifiers. One of the key challenges in this area is the need for high-quality datasets and benchmarks, which are essential for training and evaluating AI models. Several new datasets and benchmarks have been proposed, including EMBER2024, which provides a comprehensive dataset for holistic evaluation of malware classifiers. Additionally, researchers have developed new frameworks and tools, such as GeneBreaker and PoCGen, which leverage LLMs to generate and validate proof-of-concept exploits. Notable papers in this area include GeneBreaker, which introduces a framework for jailbreaking DNA foundation models, and PoCGen, which presents a novel approach to autonomously generating and validating proof-of-concept exploits for vulnerabilities in npm packages. Overall, the field of cybersecurity is rapidly advancing, with a growing focus on the development of AI-driven tools and techniques for vulnerability assessment and exploitation.