A recent ransomware attack leveraged Agentic AI to infiltrate systems via Langflow, showcasing the potential for Large Language Models (LLMs) to facilitate complex, multi-stage intrusions. This attack demonstrated the ability of LLM agents to combine known exploitation techniques with real-time reasoning, automating the intrusion process. The use of Agentic AI in this context enables attackers to adapt and respond to dynamic environments, making it increasingly difficult for defenders to detect and mitigate such threats. The fact that LLMs can be used to conduct ransomware attacks1 highlights the evolving nature of the threat landscape. As security developments continue to emerge, the ability of attackers to harness these advancements poses significant risks. This development matters to security practitioners because it underscores the need for proactive defense strategies that account for the potential use of AI-powered tools in future attacks.