The field of large language models (LLMs) is rapidly advancing, with a growing focus on ensuring their safe use. Recent research has highlighted the vulnerabilities of LLMs to jailbreak attacks, which can be used to elicit harmful responses. The development of new attack methods, such as semantically relevant nested scenarios and controlled-release prompting, has demonstrated the ability to bypass existing defenses and exploit these vulnerabilities. Furthermore, the limitations of lightweight prompt guards and the potential for adversarial attacks to degrade or bypass production-grade malware detection systems have been exposed. These findings emphasize the need for more robust defenses and a shift in focus from blocking malicious inputs to preventing malicious outputs. Noteworthy papers in this area include: Jailbreaking LLMs via Semantically Relevant Nested Scenarios with Targeted Toxic Knowledge, which proposes an adaptive and automated framework to examine LLMs' alignment. Bypassing Prompt Guards in Production with Controlled-Release Prompting, which introduces a new attack that circumvents prompt guards and highlights their limitations. Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks, which studies the vulnerability of a production malware detection system to adversarial attacks and develops an approach to mitigate their severity.