2026-06-03站内改写2 min readUpdated: 2026-06-03

AI Agents Enable Adaptive Computer Worms

A new preprint on arXiv demonstrates a computer worm that uses AI agents on compromised machines to generate tailored attack strategies for each target, spreading across Linux, Windows, and IoT devices at zero marginal cost to the attacker, bypassing traditional safety controls.

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[2606.03811] AI Agents Enable Adaptive Computer Worms

[Submitted on 2 Jun 2026]

Title:AI Agents Enable Adaptive Computer Worms

View a PDF of the paper titled AI Agents Enable Adaptive Computer Worms, by Jonas Guan and 5 other authors

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Abstract: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.

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as: arXiv:2606.03811 [cs.CR]

(or arXiv:2606.03811v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.03811

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hengrui Jia [view email] [v1] Tue, 2 Jun 2026 15:54:39 UTC (918 KB)

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