Cited by 154Adversarial Robustness in Machine LearningNetwork Security and Intrusion DetectionInformation and Cyber Security

Weaponized AI for cyber attacks

Open access
Yamin, Muhammad Mudassar, Ullah, Mohib, Ullah, Habib, Katt, Basel·2021·Journal of Information Security and Applications

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Crossref · 10.1016/j.jisa.2020.102722

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5 of 5 references verified0 citation errors · 0 not checkable

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  • Increasing AI cyber-offensive capabilities including those that target other AI systems via adversarial attacks and jailbreaking - without a commensurate increase in defensive capabilities could make this form of conflict cheaper, more widespread, and perhaps also harder to detect.

    “Increasing AI cyber-offensive capabilities including those that target other AI systems via adversarial attacks and jailbreaking (Gleave et al., 2020; Yamin et al., 2021; Zou et al., 2023) - without a commensurate increase in defensive capabilities could make this form of conflict cheaper, more widespread, and perhaps also harder to detect (Brundage et al., 2018).”

    From Multi-Agent Risks from Advanced AI (2025) · cites this paper as Yamin et al., 2021

  • Researchers have warned about the potential misuse of LLMs to generate mis/disinformation and harmful content.

    “researchers have warned about the potential misuse of such technologies to generate mis/disinformation and harmful content (Bommasani et al., 2021;Yamin et al., 2021;Guembe et al., 2022;Goldstein et al., 2023).”

    From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Yamin et al., 2021

  • LLMs have the potential to reshape the landscape of cyber-social security dramatically.

    “Therefore, LLMs have the potential to reshape the landscape of cyber-social security dramatically, a concern shared by many researchers (Bommasani et al., 2021;Yamin et al., 2021;Guembe et al., 2022;Goldstein et al., 2023;Kucharavy et al., 2023).”

    From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Yamin et al., 2021

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Authors
Yamin, M. M., Ullah, M., Ullah, H., & Katt, B.
Affiliations
  • Norwegian University of Science and Technology (NO)
  • University of Ha'il (SA)
Corresponding author
Yamin, Muhammad Mudassar
Venue
Journal of Information Security and Applications
Publisher
Elsevier BV
Published
2021-03
Volume
57
Pages
102722
ISSN
2214-2126 (ISSN-L)
OpenAlex
W3120450676
Cited by
154
References
5
Citations by year
2026: 15 · 2025: 33 · 2024: 52 · 2023: 31 · 2022: 15 · 2021: 8
FWCI
9.36
Type
journal article
Language
en
Open access
Locations
  • Journal of Information Security and Applications (publishedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
Keywords
adversarial learning, autonomous vehicle safety, attack mitigation, offensive AI

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