As new large language models, or LLMs, are rapidly developed and deployed, existing methods for evaluating their safety and discovering potential vulnerabilities quickly become outdated. To identify safety issues before they impact critical applications, Johns Hopkins researchers have developed a renewable and sustainable framework for evaluating LLMs that simplifies different types of attacks into high-quality, easily updatable safety tests—all while requiring minimal human effort to run.As new large language models, or LLMs, are rapidly developed and deployed, existing methods for evaluating their safety and discovering potential vulnerabilities quickly become outdated. To identify safety issues before they impact critical applications, Johns Hopkins researchers have developed a renewable and sustainable framework for evaluating LLMs that simplifies different types of attacks into high-quality, easily updatable safety tests—all while requiring minimal human effort to run.Machine learning & AI[#item_full_content]