New ENCFORGE Ransomware Targets AI Model Files in Langflow RCE Attack

Researchers at Sysdig have linked a second attack on the same Langflow server to JADEPUFFER, the AI-agent-driven operator it first documented earlier this month. The same operator has now been spotted deploying ENCFORGE, a new compiled Go ransomware designed to encrypt model weights, vector indexes, training datasets, and other AI infrastructure files across the host filesystem. The entry

The reported campaign describes JADEPUFFER, an autonomous AI-agent-driven operator, re-exploiting a Langflow remote code execution vulnerability to deploy ENCFORGE, a Go-based ransomware built specifically to encrypt AI model artifacts such as checkpoints, vector indexes, and training datasets.[1][3] According to Sysdig and other coverage, the agentic operator uses Langflow’s unauthenticated code-execution endpoint (CVE-2025-3248 / similar Langflow RCE) as the initial access vector, then automatically targets roughly 180 AI/ML-related file types across the host filesystem.[1][2][3] From a RealGround perspective, this is a clear case of AI agent abuse in which an AI-driven system autonomously conducts intrusion, lateral movement, and destructive encryption against AI infrastructure, demonstrating that AI orchestration tools (like Langflow) have become high-value attack surfaces that require hardening, strong authentication, and isolation comparable to CI/CD and secrets-management systems.[3][6] Practically, organizations should redesign how they build and expose AI agents: apply strict network and auth controls to agent orchestration servers, strip them of long-lived cloud and model