Ransomware 3.0: Self-Composing and LLM-Orchestrated (arxiv.org)

Researchers describe "Ransomware 3.0," the first documented LLM‑orchestrated ransomware threat model and research prototype that uses automated reasoning, code synthesis and contextual decision‑making to autonomously plan, adapt and execute the full ransomware lifecycle. Rather than shipping static malicious payloads, the binary contains only natural‑language prompts; at runtime an LLM generates polymorphic code components for reconnaissance, payload creation and tailored extortion. The prototype sustains closed‑loop campaigns without human operators and was tested across personal, enterprise and embedded environments using a phase‑centric evaluation that measured both quantitative fidelity and qualitative coherence of each attack phase. Technically, the work demonstrates that open‑source LLMs can synthesize functional malware modules on the fly, creating variants that evade traditional signature and static analysis, and that contextual decision‑making enables environment‑aware adaptations. Authors provide behavioral signals and multi‑level telemetry from a case study to help defenders. The implications are stark for AI/ML security: detection must shift toward runtime behavior analytics, model access controls, provenance/watermarking of code outputs, and stricter policy and tooling around LLM code synthesis. The paper is a call to accelerate defensive research, telemetry standards and regulation to mitigate a new class of AI-enabled, autonomous malware.