AI-Driven Malware Detection and Risks

This technical report discusses the implications of AI-driven malware and the challenges posed by large language models (LLMs) in security defense. It outlines how AI can enhance detection capabilities for beaconing signals used by malware to communicate with command-and-control servers. The report details a specific AI-driven approach that analyzes proxy logs to identify unusual traffic patterns, generating alerts for faster defensive actions. It also addresses the dual-use nature of AI technologies, highlighting concerns that advancements may favor attackers, enabling more sophisticated cyber extortion and misinformation campaigns. The report emphasizes the need for a cautious approach to integrating LLMs into business operations, advocating for clear definitions of use cases and rigorous security testing. Additionally, it examines the risks associated with prompt injection and cognitive attacks, which manipulate public perception and trust. The findings suggest that while AI can strengthen security measures, it also introduces new vulnerabilities that require careful management.