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 them

What 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 weights

Restoring 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 still exists.

It built its own escape hatch in five minutes

The delivery failure is where this campaign shows its hand.

After confirming execution, the agent swept the host for cloud keys, connection strings and API tokens, replayed them against internal database and cache services, and found the Docker socket at /var/run/docker.sock, which is functionally root. It then tried to pull the ransomware binary from its command and control server, and the fetch did not land.

Rather than retry, it changed strategy, building six Python scripts through the Langflow channel and converging on a working host escape in five minutes and 24 seconds, each correcting a failure in the one before it. The final script finds the host process ID through the Docker API, copies the binary across the namespace boundary, runs the encryption, then counts the files to confirm it worked. In the first campaign, that same behavior was a failed login diagnosed and fixed in 31 seconds.

The problem got harder and the method held. Sam Evans, then CISO at Clearwater Analytics, put it in budget terms. "In security, it's all about dwell time," he told VentureBeat in an exclusive interview. "If there's a bad actor in your environment and they've been there for a while, your dwell time is increased, therefore the blast radius has increased. Probability of it becoming a material incident is exponentially high."

Mike Riemer, Ivanti's field CISO, has watched that pivot become standard. Vendors hardened the front door, he told VentureBeat, so attackers quit knocking. "I can't get through the front door, so let me get somebody his house key, and I can make it through the back door with a house key," he said. Behind it sits whatever teams assume is covered, because "they don't sit out directly on the internet, and they're behind a protection barrier, but they're not."

No one claimed a machine did this unsupervised. TechCrunch reported on July 6 that the first operation still needed a person to pick the target and stand up infrastructure, and Sysdig could not trace the root credentials. A human aimed this one, and everything after ran with nobody at the keyboard.

Heath Renfrow, co-founder and CISO at breach-recovery firm Fenix24, told Infosecurity Magazine that when an agent compresses hours of operator work into minutes, "defenders lose valuable time." Whether the attacker was AI-driven does not change the response.

The door had been open for 14 months

CVE-2025-3248 carries a CVSS score of 9.8. CISA added it to the Known Exploited Vulnerabilities catalog on May 5, 2025, with a federal deadline of May 26, and Langflow fixed it in 1.3.0. When JADEPUFFER came back in July 2026, the server was more than fourteen months past that listing and already documented publicly as a breach victim.

Riemer put a number on how little time that leaves. "If I release a patch and a customer doesn't patch within 72 hours of that release, they're open to exploit, because that's how fast they can now do it," he said, adding most customers need a week to patch by hand. Set 72 hours against fourteen months, and the gap stops reading as one lapse.

Nothing in either campaign was new. The first forged a Nacos admin token with a default signing key public since 2020, walked through CVE-2021-29441, an authentication bypass Alibaba patched in 2021, and found a MinIO store on minioadmin:minioadmin. Sysdig counted more than 600 payloads, every one leaning on a known misconfiguration or a patched bug left exposed. The second added an exposed Docker socket. Every weakness was routine. Assembling them at machine speed was not.

Langflow draws this attention because of what it holds. VentureBeat reported in June that roughly 7,000 instances sit exposed, most in North America, holding provider API keys, cloud credentials and live connections to the vector stores ENCFORGE was built to encrypt. Riemer puts it bluntly. "When you put your security at the edge of your network, you're inviting the entire world in to the edge of your network," he said.

CISA has added five Langflow flaws to its Known Exploited Vulnerabilities catalog, two of them this month. Five Langflow flaws now sit on the KEV catalog, two of them this month. CISA added CVE-2026-55255 on July 7, a cross-tenant bypass letting any authenticated user on a shared instance run another tenant's flows with that tenant's credentials, which the maintainers' advisory rates 9.9 and fixed in 1.9.1.

On July 21, CISA added CVE-2026-0770, and that one is worse. Trend Micro found an unauthenticated path to root code execution through the exec_globals parameter, on the same validate endpoint JADEPUFFER came through, rated 9.8. KEVIntel logged exploitation from June 27, more than 220 attempts across 64 addresses. According to founder Ryan Dewhurst, speaking to BleepingComputer, the payloads reached past reconnaissance for AWS credentials and container metadata. Federal agencies had until July 24.

None of this is an exotic environment. A Cloud Security Alliance survey of 418 professionals, commissioned by Token Security, found 82% of organizations had discovered AI agents nobody knew about, and 65% had handled an agent-related incident in the past year.

McGladrey traces the permission half back four decades. Companies always cloned one employee's access profile onto the next hire, and now do it with agents. An agent "does whatever it needs to do to get its job done," he said, and it "uses far more permissions that it shouldn't have... than a human would do, because of the speed of scale and also intent."

What to do before the board asks

"Every time some new ransomware attack is published in the news, we get the inquiry from the board. What are we doing about this?" Evans said. "And if it involves AI, it sparks their fear even more."

Five things are worth answering this week. None requires buying anything.

1. Get every internet-reachable Langflow instance onto the current supported release. Anything off it is exposed to at least one of the five flaws CISA has now listed, including the July 21 addition. Then review historical requests to /api/v1/validate/code for the exec_globals pattern.

2. Get the Docker socket out of application containers. Langflow has no reason to create them. If the mount is required, front it with a socket proxy allowing only needed calls.

3. Name model artifact paths in the backup plan. Immutable snapshots of checkpoints, vector indexes and training data, restore tested, data off the host holding the weights.

4. Rotate every credential the host could reach, then pull provider keys out of its runtime. Patching does not revoke what already left. The first campaign harvested OpenAI, Anthropic and cloud credentials within seconds. Scope replacements to a secrets manager.

5. Detect mass .locked file creation in directories holding .gguf, .safetensors, .ckpt or .faiss files. Sysdig published a YARA rule and both hashes, neither with antivirus coverage at analysis time. It published no file count, so what is documented is a live encryption pass, not a measured loss.

An attacker coming through an exposed AI framework now arrives carrying something built for what it connects to, and those assets are the ones a restore cannot reproduce. Model artifacts belong in the recovery plan next to the databases. Under three weeks passed between Sysdig's two reports, all this attacker needed to go from improvised Python to a compiled locker.