{"slug": "ai-agents-enable-adaptive-computer-worms", "title": "AI Agents Enable Adaptive Computer Worms", "summary": "Researchers have demonstrated a new class of AI-powered computer worm that uses large language models to generate unique attack strategies for each target it infects. The worm, which spreads across Linux, Windows, and IoT devices by exploiting common corporate network vulnerabilities, operates at zero marginal cost to attackers by parasitically using compromised machines' computing power. The findings, published on arXiv, show that self-sustaining, AI-driven malware capable of reasoning and adapting in real time is no longer theoretical, posing a destabilizing economic threat to cybersecurity defenses.", "body_md": "# Computer Science > Cryptography and Security\n\n[Submitted on 2 Jun 2026]\n\n# Title:AI Agents Enable Adaptive Computer Worms\n\n[View PDF](/pdf/2606.03811)\n\n[HTML (experimental)](https://arxiv.org/html/2606.03811v1)\n\nAbstract: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.\n\n### Current browse context:\n\ncs.CR\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/ai-agents-enable-adaptive-computer-worms", "canonical_source": "https://arxiv.org/abs/2606.03811", "published_at": "2026-06-03 05:52:33+00:00", "updated_at": "2026-06-03 06:17:23.456022+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "ai-safety", "ai-policy", "ai-research"], "entities": ["WannaCry", "Linux", "Windows", "IoT"], "alternates": {"html": "https://wpnews.pro/news/ai-agents-enable-adaptive-computer-worms", "markdown": "https://wpnews.pro/news/ai-agents-enable-adaptive-computer-worms.md", "text": "https://wpnews.pro/news/ai-agents-enable-adaptive-computer-worms.txt", "jsonld": "https://wpnews.pro/news/ai-agents-enable-adaptive-computer-worms.jsonld"}}