# Ransomware 3.0: Self-Composing and LLM-Orchestrated

## TL;DR

This paper introduces Ransomware 3.0, an advanced AI-driven threat that autonomously plans, adapts, and executes ransomware attacks using large language models, highlighting new risks and challenges for cybersecurity defenses.

## Contribution

It presents the first threat model and prototype of LLM-orchestrated ransomware capable of dynamic, autonomous attack campaigns with minimal human input.

## Key findings

- Open source LLMs can generate functional ransomware components.
- The prototype can sustain attacks across diverse environments.
- Behavioral signals and telemetry can help detect AI-enabled ransomware.

## Abstract

Using automated reasoning, code synthesis, and contextual decision-making, we introduce a new threat that exploits large language models (LLMs) to autonomously plan, adapt, and execute the ransomware attack lifecycle. Ransomware 3.0 represents the first threat model and research prototype of LLM-orchestrated ransomware. Unlike conventional malware, the prototype only requires natural language prompts embedded in the binary; malicious code is synthesized dynamically by the LLM at runtime, yielding polymorphic variants that adapt to the execution environment. The system performs reconnaissance, payload generation, and personalized extortion, in a closed-loop attack campaign without human involvement. We evaluate this threat across personal, enterprise, and embedded environments using a phase-centric methodology that measures quantitative fidelity and qualitative coherence in each attack phase. We show that open source LLMs can generate functional ransomware components and sustain closed-loop execution across diverse environments. Finally, we present behavioral signals and multi-level telemetry of Ransomware 3.0 through a case study to motivate future development of better defenses and policy enforcements to address novel AI-enabled ransomware attacks.