JADEPUFFER, Sysdig documents an autonomous, LLM-driven ransomware operation entering via Langflow CVE-2025-3248
Sysdig documents JADEPUFFER, an end-to-end LLM-driven extortion run that entered through an unpatched, internet-exposed Langflow
Defender actions
- Patch Langflow to ≥ 1.3.0 and remove the code-validation/execution endpoint from internet exposure; the initial-access CVE has been on CISA KEV since May 2025.
- Rotate MinIO and Nacos default credentials (minioadmin:minioadmin; Nacos default token.secret.key) and stop Nacos authenticating to its backing database as root.
- Egress-filter AI-orchestration and application hosts so a compromised server cannot reach arbitrary external databases or staging infrastructure, and move LLM-provider/cloud credentials into a secrets manager off web-reachable hosts.
- For any Langflow or self-hosted AI-pipeline estate, confirm model checkpoints and training datasets are backed up to storage isolated from the compute host, ENCFORGE encrypts ~180 ML-artifact file types and any training data sitting on the same host, so a co-located backup is inside the blast radius and recovery would otherwise mean re-training from scratch.
Analysis
Sysdig's Threat Research Team documented JADEPUFFER, which it assesses to be the first observed ransomware operation driven end-to-end by a large language model rather than a human operator (Sysdig Threat Research Team, 2026-07-01). Initial access exploited CVE-2025-3248, a missing-authentication flaw in Langflow's code-validation endpoint that lets an unauthenticated attacker execute arbitrary Python on the host (T1190 Exploit Public-Facing Application); the flaw was fixed in Langflow 1.3.0 and added to CISA KEV in May 2025, so the exposed instance was an already-known, unpatched target (The Hacker News, 2026-07-02).
Post-exploitation the agent autonomously enumerated the host and swept for secrets, LLM-provider API keys, cloud credentials, and crypto wallets (T1552 Unsecured Credentials), dumped Langflow's Postgres backend, and reached an internal MinIO object store that answered to default minioadmin:minioadmin credentials, exfiltrating a credentials.json from an internal bucket (Sysdig, 2026-07-01). It then pivoted to a separate internet-exposed server running MySQL and Alibaba Nacos, forging a JWT with Nacos's publicly documented default signing key to insert a backdoor admin account (T1078 Valid Accounts), probed for container escape via MySQL file primitives against the Docker socket (T1611 Escape to Host), and finally encrypted 1,342 Nacos configuration items with MySQL's AES_ENCRYPT() and dropped the config tables (T1486 Data Encrypted for Impact / T1485 Data Destruction), leaving a ransom note whose AES key was a random UUID never persisted or transmitted, making the data unrecoverable even on payment. Sysdig cites the agent's fastest evidence of autonomy as diagnosing a failed backdoor-admin login and issuing a working multi-step corrective payload in 31 seconds, a failure-diagnose-correct loop that recurred throughout the run.
Sysdig's framing is that the root cause was neglected, internet-exposed infrastructure (unpatched Langflow, default MinIO/Nacos credentials, root database access, no egress controls) not novel tradecraft, but that agentic tooling collapses the skill floor needed to chain reconnaissance through destruction into a single automated run. Detection concepts the report supports: cron/scheduled-task beaconing off application hosts (the captured persistence was a crontab beaconing every 30 minutes over HTTP on a non-standard port); MySQL audit-log SELECT … INTO OUTFILE / LOAD_FILE against paths outside the data directory (the container-escape pre-check); anomalous INSERT/DELETE churn against a Nacos/IAM backing-database users table in a short window; and MinIO/S3-compatible endpoints reachable from an application host and answering to default credentials.
Cited evidence
The Sysdig Threat Research Team (TRT) has captured what we assess to be the first documented case of agentic ransomware: a complete extortion operation driven end-to-end by a large language model (LLM).
CVE-2025-3248 is a missing-authentication flaw in its code validation endpoint that allows an unauthenticated attacker to execute arbitrary Python on the host.
The flaw was fixed in Langflow 1.3.0 and added to CISA's Known Exploited Vulnerabilities list in May 2025, but plenty of servers were never updated.
In a new development, the operator behind JADEPUFFER has doubled down on that bet, using ransomware to destroy the one thing an organization can't simply restore: a trained AI model.
Updates1
The JADEPUFFER operator, the agentic-LLM extortion actor Sysdig first documented exploiting Langflow's missing-authentication code-execution flaw (CVE-2025-3248), returned to the same Langflow instance with a materially upgraded payload. Where the original intrusion improvised Python and MySQL AES_ENCRYPT() to extort a downstream database, the new run deploys ENCFORGE (written to disk as lockd), a compiled, UPX-packed Go ransomware purpose-built for the machine-learning stack (Sysdig, 2026-07-20). Sysdig ties it to the same actor (the extortion contact embedded in ENCFORGE matches the one disclosed in the prior report) assessing "the same operator with a materially upgraded toolkit." Infosecurity Magazine corroborates the campaign (Infosecurity Magazine, 2026-07-20).
ENCFORGE targets roughly 180 file extensions spanning the modern ML pipeline (PyTorch/TensorFlow checkpoints, HuggingFace SafeTensors weights, llama.cpp GGUF quantized models, FAISS vector indices, Apache Parquet/TFRecord training datasets, NumPy arrays and LoRA adapters) encrypting with AES-256-CTR under RSA-2048. Sysdig frames the significance bluntly: the operator is "using ransomware to destroy the one thing an organization can't simply restore: a trained AI model," because rebuilding a production fine-tuned model means re-running weeks-to-months of training, and if the training data sits on the same compromised host it is encrypted too.
Sources4
Revision history
- Published 2026-07-04T0009Z-intel
- Update 2026-07-21T0409Z-intel
Sysdig reports (2026-07-20) that the JADEPUFFER operator returned to the same internet-exposed Langflow instance and staged ENCFORGE, a compiled, UPX-packed Go ransomware purpose-built for AI/ML infrastructure, encrypting roughly 180 file types across model checkpoints, weights, quantized models, vector indices and training datasets. The extortion contact matches the July run, confirming the same operator; the operational point for defenders is that encrypted model checkpoints and co-located training data cannot be restored from a vendor patch or a decryptor.
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