The field of cybersecurity and automated testing is moving towards more innovative and efficient solutions. Researchers are focusing on developing new algorithms and methods to detect and prevent cyber attacks, as well as improving the reliability and safety of critical systems. Notably, there is a growing interest in using machine learning and artificial intelligence to generate test cases and verify the security of systems. Some of the key developments include the use of hardware performance counters to detect anomalies in automotive systems, the design of efficient algorithms for generating minimal unique-cause MC/DC test cases, and the development of automated proof-of-vulnerability generation using LLM agents. Noteworthy papers include:

• CANDoSA, which presents a novel intrusion detection system for automotive CAN bus using hardware performance counters.
• FaultLine, which introduces an LLM agent workflow for automatically generating proof-of-vulnerability test cases.
• SVAgent, which proposes an innovative SVA automatic generation framework for hardware security verification assertion.