Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters
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| Format: | Preprint |
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2025
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| _version_ | 1866916648427978752 |
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| author | Rahaman, Habibur Chatterjee, Atri Bhunia, Swarup |
| author_facet | Rahaman, Habibur Chatterjee, Atri Bhunia, Swarup |
| contents | Rapid adoption of AI technologies raises several major security concerns, including the risks of adversarial perturbations, which threaten the confidentiality and integrity of AI applications. Protecting AI hardware from misuse and diverse security threats is a challenging task. To address this challenge, we propose SAMURAI, a novel framework for safeguarding against malicious usage of AI hardware and its resilience to attacks. SAMURAI introduces an AI Performance Counter (APC) for tracking dynamic behavior of an AI model coupled with an on-chip Machine Learning (ML) analysis engine, known as TANTO (Trained Anomaly Inspection Through Trace Observation). APC records the runtime profile of the low-level hardware events of different AI operations. Subsequently, the summary information recorded by the APC is processed by TANTO to efficiently identify potential security breaches and ensure secure, responsible use of AI. SAMURAI enables real-time detection of security threats and misuse without relying on traditional software-based solutions that require model integration. Experimental results demonstrate that SAMURAI achieves up to 97% accuracy in detecting adversarial attacks with moderate overhead on various AI models, significantly outperforming conventional software-based approaches. It enhances security and regulatory compliance, providing a comprehensive solution for safeguarding AI against emergent threats. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_07568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters Rahaman, Habibur Chatterjee, Atri Bhunia, Swarup Cryptography and Security Artificial Intelligence Machine Learning Rapid adoption of AI technologies raises several major security concerns, including the risks of adversarial perturbations, which threaten the confidentiality and integrity of AI applications. Protecting AI hardware from misuse and diverse security threats is a challenging task. To address this challenge, we propose SAMURAI, a novel framework for safeguarding against malicious usage of AI hardware and its resilience to attacks. SAMURAI introduces an AI Performance Counter (APC) for tracking dynamic behavior of an AI model coupled with an on-chip Machine Learning (ML) analysis engine, known as TANTO (Trained Anomaly Inspection Through Trace Observation). APC records the runtime profile of the low-level hardware events of different AI operations. Subsequently, the summary information recorded by the APC is processed by TANTO to efficiently identify potential security breaches and ensure secure, responsible use of AI. SAMURAI enables real-time detection of security threats and misuse without relying on traditional software-based solutions that require model integration. Experimental results demonstrate that SAMURAI achieves up to 97% accuracy in detecting adversarial attacks with moderate overhead on various AI models, significantly outperforming conventional software-based approaches. It enhances security and regulatory compliance, providing a comprehensive solution for safeguarding AI against emergent threats. |
| title | Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.07568 |