The rapid adoption of Large Language Models (LLMs) has outpaced the security maturity of the tools used to manage them. Today, we are tracking a significant escalation in attacks targeting AI workflow orchestration platforms, specifically Flowise and Langflow. The disclosure of CVE-2026-0770, a critical Remote Code Execution (RCE) vulnerability in Flowise, serves as a stark warning. This 'one-click' vulnerability allows an attacker to execute arbitrary code on a self-hosted Flowise server simply by tricking a user into importing a malicious 'chatflow' file. This technique mirrors the classic document-based phishing of the early 2000s but is adapted for the era of generative AI. According to SecurityWeek and OSINT signals from Reddit's r/cybersecurity, exploit code for this flaw is already circulating, and opportunistic scanning has begun. The danger here is structural: Flowise and Langflow are often granted high-level permissions to access internal databases, API keys, and sensitive corporate data to 'ground' the AI's responses. A compromise of the orchestration layer is, therefore, a compromise of the entire AI-enabled business process. Furthermore, Microsoft Security has identified a parallel campaign involving 33 malicious npm packages that use dependency confusion to profile developer environments. These packages specifically target developers working on AI and machine learning projects, attempting to harvest environment variables and cloud credentials. This dual-threat—vulnerabilities in the AI tools themselves and poisoning of the developer ecosystem—suggests that threat actors view the AI boom as a soft target. Organizations must treat AI infrastructure with the same level of scrutiny as their core financial or identity systems. This includes sandboxing AI orchestration layers, implementing strict egress filtering, and performing deep audits of any third-party 'chatflows' or 'agents' before they are integrated into production environments. The 'AI Workflow Trap' is not just a bug; it is a fundamental challenge to the rapid industrialization of LLMs.