TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems

• URL: http://arxiv.org/abs/2411.07114v1
• Date: Mon, 11 Nov 2024 16:41:22 GMT
• Title: TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
• Authors: Jacob Huckelberry, Yuke Zhang, Allison Sansone, James Mickens, Peter A. Beerel, Vijay Janapa Reddi,
• Abstract summary: Tiny Machine Learning (TinyML) systems enable machine learning inference on highly resource-constrained devices. TinyML models pose security risks, with weights potentially encoding sensitive data and query interfaces that can be exploited. This paper offers the first thorough survey of TinyML security threats.
• Score: 12.33137384257399
• License:
• Abstract: Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities two to three orders of magnitude smaller than conventional systems, make traditional software and hardware security solutions impractical. The physical accessibility of these devices exacerbates their susceptibility to side-channel attacks and information leakage. Additionally, TinyML models pose security risks, with weights potentially encoding sensitive data and query interfaces that can be exploited. This paper offers the first thorough survey of TinyML security threats. We present a device taxonomy that differentiates between IoT, EdgeML, and TinyML, highlighting vulnerabilities unique to TinyML. We list various attack vectors, assess their threat levels using the Common Vulnerability Scoring System, and evaluate both existing and possible defenses. Our analysis identifies where traditional security measures are adequate and where solutions tailored to TinyML are essential. Our results underscore the pressing need for specialized security solutions in TinyML to ensure robust and secure edge computing applications. We aim to inform the research community and inspire innovative approaches to protecting this rapidly evolving and critical field.

Related papers

• CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration [90.36429361299807]
  multimodal large language models (MLLMs) have demonstrated remarkable success in engaging in conversations involving visual inputs. The integration of visual modality has introduced a unique vulnerability: the MLLM becomes susceptible to malicious visual inputs. We introduce a technique termed CoCA, which amplifies the safety-awareness of the MLLM by calibrating its output distribution.
  arXiv  Detail & Related papers  (2024-09-17T17:14:41Z)
• Enhancing TinyML Security: Study of Adversarial Attack Transferability [0.35998666903987897]
  This research delves into the adversarial vulnerabilities of AI models on resource-constrained embedded hardware. Our findings reveal that adversarial attacks from powerful host machines could be transferred to smaller, less secure devices like ESP32 and Raspberry Pi. This illustrates that adversarial attacks could be extended to tiny devices, underscoring vulnerabilities, and emphasizing the necessity for reinforced security measures in TinyML deployments.
  arXiv  Detail & Related papers  (2024-07-16T10:55:25Z)
• On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case [17.1066653907873]
  This paper reviews challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints. It also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage.
  arXiv  Detail & Related papers  (2024-04-25T01:57:11Z)
• Highlighting the Safety Concerns of Deploying LLMs/VLMs in Robotics [54.57914943017522]
  We highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications.
  arXiv  Detail & Related papers  (2024-02-15T22:01:45Z)
• Survey of Security Issues in Memristor-based Machine Learning Accelerators for RF Analysis [0.0]
  We explore security aspects of a new computing paradigm that combines novel memristors and traditional CMOS. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. Mixed signal approximate computing model has different vulnerabilities than traditional digital implementations.
  arXiv  Detail & Related papers  (2023-12-01T21:44:35Z)
• Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review [51.31851488650698]
  Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. adversarial distortion injected into the power signal will greatly affect the system's normal control and operation. It is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems.
  arXiv  Detail & Related papers  (2023-08-30T03:29:26Z)
• A review of TinyML [0.0]
  The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware. This paper explores how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.
  arXiv  Detail & Related papers  (2022-11-05T06:02:08Z)
• Unsolved Problems in ML Safety [45.82027272958549]
  We present four problems ready for research, namely withstanding hazards, identifying hazards, steering ML systems, and reducing risks to how ML systems are handled. We clarify each problem's motivation and provide concrete research directions.
  arXiv  Detail & Related papers  (2021-09-28T17:59:36Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead [24.60052335548398]
  Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) They are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities.
  arXiv  Detail & Related papers  (2021-01-04T20:06:56Z)
• 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.