The Malicious Use of Artificial Intelligence

A report titled "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation" was submitted to arXiv on 20 Feb 2018 and last revised 1 Dec 2024, surveying potential security threats from malicious uses of AI across the digital, physical, and political domains. The report's authors, including Miles Brundage, make four high-level recommendations for AI researchers and other stakeholders and suggest several promising areas for further research to expand defenses or make attacks less effective. The report also discusses, without conclusively resolving, the long-term equilibrium of attackers and defenders.

Computer Science Artificial Intelligence Submitted on 20 Feb 2018 v1 https://arxiv.org/abs/1802.07228v1 , last revised 1 Dec 2024 this version, v2 Title:The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation View PDF /pdf/1802.07228 Abstract:This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders. Submission history From: Miles Brundage view email /show-email/ea10e57d/1802.07228 Tue, 20 Feb 2018 18:07:50 UTC 1,400 KB \ v1\ /abs/1802.07228v1 v2 Sun, 1 Dec 2024 17:59:04 UTC 1,400 KB Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both 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. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .