Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU
Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU
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DOI：
10.1515/popets-2017-0016
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发表时间：
2016-05
影响因子：
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通讯作者：
Zhe Zhou;Wenrui Diao;Xiangyu Liu;Zhou Li;Kehuan Zhang;Rui Liu
中科院分区：
文献类型：
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作者：
Zhe Zhou;Wenrui Diao;Xiangyu Liu;Zhou Li;Kehuan Zhang;Rui Liu
摘要根据以前的报告，可以从GPU内存中泄漏信息；但是，这种威胁的安全含义大多被忽略了，因为只能通过侧向通道攻击间接提取有限的信息。在本文中，我们提出了一种新型算法，用于直接从许多流行应用的GPU内存残基（例如Google Chrome和Adobe PDF读取器）中恢复原始数据。我们的算法使收集高度敏感的信息，包括信用卡号码和来自GPU内存残留物中的电子邮件内容。评估结果还表明，几乎所有GPU加速应用程序都容易受到此类攻击的影响，并且对手可以发动攻击，而无需在传统的多用户操作系统上均需要任何特殊特权，以及新兴的云计算场景。
Abstract According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.