A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, and their spread can be halted by patching those vulnerabilities. Here we show that artificial intelligence (AI) agents enable a fundamentally new threat: a worm that generates tailored attack strategies to each target it encounters. The worm parasitically uses compromised machines to run open-weight large language models (LLMs) to sustain its reasoning, or extend its reach for further attacks. Deployed on a network of machines spanning Linux, Windows, and IoT (Internet of Things) devices, the worm propagated by exploiting common, real-world corporate network vulnerabilities. Since the worm is powered by stolen compute, the attacker's marginal cost per new infection is zero. This creates a destabilizing economic asymmetry between attackers and defenders. Moreover, because the worm requires no commercial AI platform, centralized safety controls, such as service refusals or rate limiting, are structurally irrelevant. Our results demonstrate that self-sustaining AI-driven cyber-threats are no longer theoretical. We must prepare for autonomous generative adversaries: malware systems that propagate without human operators and are defined not by fixed exploit code, but by the capacity to reason about targets, adapt to observations, and synthesize attack logic in real time.

翻译：计算机蠕虫是一种通过自我复制在网络中传播的恶意软件。传统蠕虫（如WannaCry）利用预设漏洞传播，一旦修补这些漏洞即可阻断其扩散。本研究提出，人工智能（AI）Agent催生了一种全新威胁：能够针对其遭遇的每个目标生成定制化攻击策略的蠕虫。该蠕虫寄生性地利用被攻陷设备运行开源权重的大型语言模型（LLM），以维持其推理能力或扩展攻击范围。当其部署于涵盖Linux、Windows及物联网设备的网络时，可通过利用常见的真实企业网络漏洞进行传播。由于蠕虫依赖窃取的算力运行，攻击者每次新感染所需的边际成本为零，这导致攻防双方形成不稳定的经济不对称性。此外，由于该蠕虫无需依赖商业AI平台，服务拒绝或速率限制等集中式安全控制机制在结构上已失效。本研究证明，自我维持的AI驱动型网络威胁已不再是理论假设。我们必须为自主生成式对抗性实体做好准备：一类无需人类操作者即可传播的恶意软件系统，其核心不再是固定漏洞利用代码，而是实时分析目标、适应环境变化并生成攻击逻辑的能力。