New ransomware targets AI model weights and can't even collect the ransom

The same attacker broke into the same internet-facing Langflow server twice, and the second time brought ransomware built to destroy trained AI models. Sysdig's Threat Research Team documented the first campaign on July 1 and the second on July 20. The entry point never changed, but the payload changed completely.Both ran through CVE-2025-3248, a missing-authentication flaw in Langflow's code-validation endpoint that lets anyone reaching the server execute Python on it. In the first, the agent improvised, encrypting 1,342 Alibaba Nacos configuration items with MySQL's own encryption function and dropping the tables. In the second, it staged ENCFORGE, a compiled Go binary sweeping roughly 180 file extensions.ENCFORGE was built for AI assets, not adapted to themWhat gives the design away is the extension list. Sysdig found PyTorch and TensorFlow checkpoints, Hugging Face SafeTensors weights, the GGUF format behind most local LLM deployment, FAISS vector indexes and training data in Parquet and NumPy.Generic ransomware picks up model files by accident because it encrypts everything. ENCFORGE names them.Its flag for appending formats uses LoRA adapters and legacy GGML weights as the example, and an attacker who writes that knows whose machines these are.Michael Clark, who leads Sysdig's threat research team, framed the objective as destroying "the one thing an organization can't simply restore." ENCFORGE carries no network code. Sysdig found no outbound dial in the binary, no leak site and no payment portal, and the identical Proton Mail contact in both ransom notes ties the campaigns to the crew it tracks as JADEPUFFER. The agent harvested credentials on the way in, but the locker cannot exfiltrate anything, so its only pressure is making files unusable. It encrypts regions of a file rather than the whole file, under AES-256-CTR with a per-run key wrapped in an embedded RSA-2048 key, the speed optimization established ransomware families use to ruin large files fast.Your backup plan probably does not cover model weightsRestoring a database from Friday's snapshot costs a weekend of transactions. Restoring a fine-tuned model costs everything learned since Friday, none of it stored as rows to replay.Rebuilding one is not a restore job. Sysdig puts direct recovery for a production-ready fine-tuned model between $75,000 and $500,000, reflecting cloud GPU rates across the training runs a usable result requires plus the engineering hours behind them. That is per model, and teams keep several variants on shared storage. If the training data sits on the same host as the weights, recovery is blocked until the dataset is rebuilt. Paying is no way out either. In the first campaign, the encryption key was generated at random, printed to the console once and never saved, which made that payload a wiper wearing a ransom note.That figure makes the argument fundable. Kayne McGladrey, an IEEE Senior Member who has spent his career in identity security, told VentureBeat that security teams lose these fights by filing the exposure under the wrong heading. Companies "should be focused on business risks rather than some, you know, cybersecurity risk, because if it doesn't affect the business, like a loss or financial loss, in this case, predominantly, then nobody's going to pay any action to it, and they will not budget it appropriately, nor will they adequately put in controls to prevent it," he said. A destroyed model carries a known replacement cost, which is the version of this story a CFO acts on.Official guidance has not caught up. In May 2025 the NSA's Artificial Intelligence Security Center, CISA and the FBI published "AI Data Security," the most authoritative document on the subject, co-sealed with the U.K., Australia and New Zealand. The three risks it names are the data supply chain, maliciously modified data and data drift. All three ask whether the data can be trusted. ENCFORGE asks whether it