Bad actors can launch complex attacks with less skill. AI makes cybercrime scale faster and cheaper. Real systems already got hit this way. Defenders face new speed of automated threats.
Sysdig full report on JADEPUFFER:
Ransomware has had a human at the keyboard, or at least a human writing its script, since it was first established as a category of threat. 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).
This operator, which we have dubbed JADEPUFFER, gained initial access to an internet-facing Langflow instance through CVE-2025-3248 and ran an adaptive and fully automated campaign, ultimately pivoting to the intended target and running a destructive database-extortion playbook against the victim’s production database server. JADEPUFFER is considered an agentic threat actor (ATA), or an operator whose attack capability is delivered by an AI agent rather than a human-driven toolkit.
The most striking characteristic, however, was the LLM’s behavior. JADEPUFFER’s own payloads were self-narrating. They contained natural language reasoning, target prioritization, and the kind of detailed annotations that human operators don’t often write but LLM-generated code produces reflexively. The operation also adapted in real time, retrying failed steps within refined parameters. In one sequence, it went from a failed login to a working fix in 31 seconds.
The research below examines the Sysdig TRT’s observations of JADEPUFFER, along with its indicators of compromise and recommended defensive actions.
