HackerPapers

arXiv:2106.15764cs.AI

The Threat of Offensive AI to Organizations

Yisroel Mirsky, Ambra Demontis, Jaidip Kotak, Ram Shankar, Deng Gelei, Liu Yang, Xiangyu Zhang, Wenke Lee, Yuval Elovici, Battista Biggio

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI (such as machine learning) to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this survey, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary's methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 33 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a user study spanning industry and academia, we rank the AI threats and provide insights on the adversaries.

arXiv comments
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.CRCryptography and Security cs.CYComputers and Society cs.LGMachine Learning

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Apr 2023 to Apr 2023.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. GitHub Copilot emits GPL code 1 comment
    • It seems to me, from a copyright perspective, all commercial use of generative AI depends on whether the output is transformative fair use (vs derived work). While the courts will have its say, ultimately whether new rules are carved out or not is going to be again (as all…

      lhlView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.