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ZeroKernel: Secure Context-Isolated Execution on Commodity GPUs
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DOI：10.1109/TDSC.2019.2946250.png)
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摘要
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In the last decade, the dedicated graphics processing unit (GPU) has emerged as an architecture for high-performance computing workloads. Recently, researchers have also focused on the isolation property of a dedicated GPU and suggested GPU-based secure computing environments with several promising applications. However, despite the security analysis conducted by the prior studies, it has been unclear whether a dedicated GPU can be leveraged as a secure processor in the presence of a kernel-privileged attacker. In this paper, we first demonstrate the security of dedicated GPUs through comprehensive studies on context information for GPU execution. The paper shows that a kernel-privileged attacker can manipulate the GPU contexts to redirect memory accesses or execute arbitrary GPU codes on the running GPU kernel. Based on the security analysis, this paper proposes a new on-chip execution model for the dedicated GPU and a novel defense mechanism supporting the security of the on-chip execution. With comprehensive evaluation, the paper assures that the proposed solutions effectively isolate sensitive data in on-chip storages and defend against known attack vectors from a privileged attacker, supporting that the commodity GPUs can be leveraged as a secure processor.
Keyword:
Graphics processing units
Kernel
Context
Security
System-on-chip
Registers
Computer architecture
Graphics processors
reverse engineering
security
on-chip execution
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