AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies
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Abstract (arXiv)
Artificial Intelligence's dual-use nature is revolutionizing the cybersecurity landscape, introducing new threats across four main categories: deepfakes and synthetic media, adversarial AI attacks, automated malware, and AI-powered social engineering. This paper aims to analyze emerging risks, attack mechanisms, and defense shortcomings related to AI in cybersecurity. We introduce a comparative taxonomy connecting AI capabilities with threat modalities and defenses, review over 70 academic and industry references, and identify impactful opportunities for research, such as hybrid detection pipelines and benchmarking frameworks. The paper is structured thematically by threat type, with each section addressing technical context, real-world incidents, legal frameworks, and countermeasures. Our findings emphasize the urgency for explainable, interdisciplinary, and regulatory-compliant AI defense systems to maintain trust and security in digital ecosystems.
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References · 8
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
- 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 124
- 2025Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024cited by 45
- 2024Identifying Bias in Deep Neural Networks Using Image Transformscited by 6
- 2025Identifying bias in CNN image classification using image scrambling and transformscited by 4
- 2025Explainable identification of similarities between entities for discovery in large textcited by 1
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