The field of software security is rapidly evolving, with a growing focus on developing innovative solutions to protect against emerging threats. Recent developments have highlighted the importance of efficient and evasion-resistant dynamic binary analysis, as well as the need for secure software development practices. Researchers are exploring new approaches to detect and prevent vulnerabilities in software dependencies, and to ensure the integrity of software supply chains. Notably, hardware-based approaches are being investigated for their potential to improve performance and security. Additionally, novel methods for disrupting malware hidden in neural network parameters are being proposed, leveraging permutation symmetry to prevent malicious activities. Overall, the field is moving towards a more comprehensive and proactive approach to software security, with a emphasis on soundness, precision, and efficiency. Noteworthy papers include: LibIHT, which proposes a hardware-assisted tracing framework for efficient and evasion-resistant dynamic binary analysis. HAMLOCK, which introduces a stealthy attack that distributes the attack logic across the hardware-software boundary, highlighting the need for new cross-layer defenses. NeuPerm, which disrupts malware hidden in neural network parameters by leveraging permutation symmetry, showing promise for preventing malicious activities in deep learning models.