Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures

• URL: http://arxiv.org/abs/2607.04055v1
• Date: Sat, 04 Jul 2026 23:45:25 GMT
• Title: Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures
• Abstract summary: Deep learning models are increasingly deployed in critical sectors such as healthcare, finance, and security.<n>Side-channel attacks (SCAs) represent a particular challenge since they can extract sensitive information without requiring direct access to the model.<n>By leveraging the physical and micro-architectural properties of the hardware, attackers can compromise systems.
• Score: 0.8602553195689513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As deep learning models are increasingly deployed in critical sectors such as healthcare, finance, and security, ensuring their protection against emerging threats has become crucial. Among these threats, side-channel attacks (SCAs) represent a particular challenge since they can extract sensitive information such as model architectures, parameters, and even user inputs without requiring direct access to the model. By leveraging the physical and micro-architectural properties of the hardware, attackers can compromise systems. This survey begins by classifying leakage sources and attacker objectives, then analyzes representative studies that demonstrate practical side-channel exploits against deep-learning hardware. It also reviews existing defenses aimed at mitigating these vulnerabilities and concludes by outlining key open research challenges and potential future directions.

Related papers

• Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models [69.22690439422531]
  Diffusion models (DMs) have been investigated in various domains due to their ability to generate high-quality data.<n>Similar to traditional deep learning systems, there also exist potential threats to DMs.<n>This survey comprehensively elucidates its framework, threats, and countermeasures.
  arXiv  Detail & Related papers  (2025-09-25T02:51:43Z)
• Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs [0.0]
  Large Language Models (LLMs) have introduced critical security challenges.<n> adversarial actors can manipulate input prompts to cause significant harm and circumvent safety alignments.<n>This survey aims to inform the research community's efforts in building the next generation of secure LLMs.
  arXiv  Detail & Related papers  (2025-09-04T18:59:07Z)
• A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures [0.0]
  Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity.<n>Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering.<n>Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-18T14:21:46Z)
• Deep Learning Model Security: Threats and Defenses [25.074630770554105]
  Deep learning has transformed AI applications but faces critical security challenges.<n>This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality.<n>The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models.
  arXiv  Detail & Related papers  (2024-12-12T06:04:20Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [69.18069679327263]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training.<n>Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs.<n>This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects [0.33554367023486936]
  This paper provides a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations. We discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS.
  arXiv  Detail & Related papers  (2024-09-27T13:27:29Z)
• It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness [4.1310970179750015]
  Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. To address this problem, the area of adversarial robustness investigates mechanisms behind adversarial attacks and defenses against these attacks.
  arXiv  Detail & Related papers  (2023-03-17T04:18:03Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Holistic Adversarial Robustness of Deep Learning Models [91.34155889052786]
  Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
  arXiv  Detail & Related papers  (2022-02-15T05:30:27Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

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.