Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI

• URL: http://arxiv.org/abs/2506.12519v1
• Date: Sat, 14 Jun 2025 14:21:01 GMT
• Title: Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
• Authors: Saskia Laura Schröer, Luca Pajola, Alberto Castagnaro, Giovanni Apruzzese, Mauro Conti,
• Abstract summary: AI as a target of attacks (Adversarial AI') and AI as a means to launch attacks on any target (Offensive AI')<n>This article explores two emerging AI-related threats and the interplay between them.
• Score: 18.178555463870214
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As Artificial Intelligence (AI) continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across various sectors. In security, AI's role in strengthening organizational resilience has been studied for over two decades. While much attention has focused on AI's constructive applications, the increasing maturity and integration of AI have also exposed its darker potentials. This article explores two emerging AI-related threats and the interplay between them: AI as a target of attacks (`Adversarial AI') and AI as a means to launch attacks on any target (`Offensive AI') -- potentially even on another AI. By cutting through the confusion and explaining these threats in plain terms, we introduce the complex and often misunderstood interplay between Adversarial AI and Offensive AI, offering a clear and accessible introduction to the challenges posed by these threats.

Related papers

• Designing AI-Enabled Countermeasures to Cognitive Warfare [0.0]
  Foreign information operations on social media platforms pose significant risks to democratic societies.<n>With the rise of Artificial Intelligence (AI), this threat is likely to intensify, potentially overwhelming human defenders.<n>This paper proposes possible AI-enabled countermeasures against cognitive warfare.
  arXiv  Detail & Related papers  (2025-04-14T11:36:03Z)
• Superintelligence Strategy: Expert Version [64.7113737051525]
  Destabilizing AI developments could raise the odds of great-power conflict.<n>Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers.<n>We introduce the concept of Mutual Assured AI Malfunction.
  arXiv  Detail & Related papers  (2025-03-07T17:53:24Z)
• A Survey on Offensive AI Within Cybersecurity [1.8206461789819075]
  This survey paper on offensive AI will comprehensively cover various aspects related to attacks against and using AI systems. It will delve into the impact of offensive AI practices on different domains, including consumer, enterprise, and public digital infrastructure. The paper will explore adversarial machine learning, attacks against AI models, infrastructure, and interfaces, along with offensive techniques like information gathering, social engineering, and weaponized AI.
  arXiv  Detail & Related papers  (2024-09-26T17:36:22Z)
• Views on AI aren't binary -- they're plural [0.10241134756773229]
  We argue that a simple binary is not an accurate model of AI discourse. We provide concrete suggestions for how individuals can help avoid the emergence of us-vs-them conflict in the broad community of people working on AI development and governance.
  arXiv  Detail & Related papers  (2023-12-21T17:50:06Z)
• A Red Teaming Framework for Securing AI in Maritime Autonomous Systems [0.0]
  We propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI.
  arXiv  Detail & Related papers  (2023-12-08T14:59:07Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• The Threat of Offensive AI to Organizations [52.011307264694665]
  This survey explores the threat of offensive AI on organizations. First, we 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.
  arXiv  Detail & Related papers  (2021-06-30T01:03:28Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.