Prompts used in large language models (LLMs) are emerging as critical indicators of compromise (IOCs) in cybersecurity, highlighting how threat actors exploit these technologies for malicious purposes. The article reviews a recent report from Anthropic detailing various misuse cases of the AI model Claude and emphasizes the need for threat analysts to focus on prompt-based tactics, techniques, and procedures (TTPs) for effective monitoring and detection. The author proposes the NOVA tool for detecting adversarial prompts tailored to specific threat scenarios.

The article discusses the evolving role of Indicators of Compromise (IOCs) and the importance of context in threat detection. It emphasizes the limitations of IOCs in real-time detection due to their quick obsolescence and the need to balance their use with behavioral detections (IOAs) for more effective cybersecurity strategies. The piece also highlights that not all IOCs are created equal and stresses the value of enriched context for maximizing their effectiveness in threat analysis.