Weaponized AI for cyber attacks
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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
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
Bag of Tricks for Image Classification with Convolutional Neural Networks
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00065 · Cited by 1,680 · see citing works
Adversarial attacks on an oblivious recommender
Christakopoulou, K., & Banerjee, A. (2019) · Proceedings of the 13th ACM Conference on Recommender Systems · doi:10.1145/3298689.3347031 · Cited by 105 · see citing works
Chatila, R., Firth-Butterflied, K., Havens, J. C., & Karachalios, K. (2017) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2017.2670225 · Cited by 72 · see citing works
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Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges
Nguyen, V.-L., Lin, P.-C., Cheng, B.-C., Hwang, R.-H., & Lin, Y.-D. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3108618 · Cited by 515 · see citing works
Digital deception: generative artificial intelligence in social engineering and phishing
Schmitt, M., & Flechais, I. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10973-2 · Cited by 198 · see citing works
Blockchain for Securing AI Applications and Open Innovations
Shinde, R., Patil, S., Kotecha, K., & Ruikar, K. (2021) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc7030189 · Cited by 75 · see citing works
Anatomy of an AI-powered malicious social botnet
Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71 · see citing works
Multi-Agent Risks from Advanced AI
Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.14143 · Cited by 10 · see citing works
- 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
- green
- hdl.handle.net
- Licence
- 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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