The key point: JadePuffer exploits a CVE-2025-3248 vulnerability in Langflow to automatically encrypt AI model weights and training data with EncForge, causing estimated damages of $75,000 to $500,000 per model.
An autonomous AI agent named JadePuffer uses the purpose-built ransomware EncForge to encrypt training data and vector databases of AI systems. Sysdig has analyzed the campaign and documented how the agent independently gains access to target environments.
The AI agent JadePuffer deploys the ransomware EncForge, written in Go, to specifically target AI and machine learning infrastructures. The malware encrypts approximately 180 different file types, including model formats such as PyTorch, TensorFlow, Hugging Face SafeTensors, GGUF and GGML, as well as vector indices from FAISS and datasets in Parquet, Arrow, TFRecord, NumPy and DuckDB formats. According to Sysdig’s analysis to date, no data theft by EncForge has been detected.
For initial access, the agent exploits vulnerability CVE-2025-3248 in Langflow instances. After gaining entry, JadePuffer autonomously scans the network for credentials, API tokens and internal services. In one analyzed attack, the agent discovered an exposed Docker socket with root access. When direct ransomware transfer failed, the agent independently developed and deployed six Python scripts within five minutes to resolve the transfer issues. The final script “deploy.py v2” represented a fully autonomous encryption pipeline that identified the target process ID, copied EncForge across namespace boundaries, performed a test run, and subsequently verified execution by counting .locked files.
EncForge uses hybrid encryption with AES-256 in CTR mode for file contents, while the symmetric key is secured with RSA-2048. The software encrypts only selected file areas to accelerate the process. Encrypted files receive the .locked extension and an extortion message is left behind. Sysdig estimates the financial damage from encrypting model weights and training data at $75,000 to $500,000 per model, resulting from the total loss of months of training work.
Source: www.it-daily.net · Published 22 July 2026
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