A Timing Side-Channel Attack on a Mobile GPU

Karimi, Elmira; Jiang, Zhen Hang; Fei, Yunsi; Kaeli, David

doi:10.1109/iccd.2018.00020

Cited by 21 publications

(13 citation statements)

References 10 publications

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“…Side channel attacks on mobile GPUs. Existing attacks on mobile GPUs used GPU timing information to infer ciphertexts in AES encryption [27], and reverse engineered GPU hardware to implement Rowhammer attack on GPU memory [11]. These attacks, however, are not focusing on eavesdropping keyboard inputs and are orthogonal to our work.…”

Section: Moving Circles and Trianglesmentioning

confidence: 99%

Eavesdropping user credentials via GPU side channels on smartphones

Yang

Chen

Huang

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Graphics Processing Unit (GPU) on smartphones is an effective target for hardware attacks. In this paper, we present a new side channel attack on mobile GPUs of Android smartphones, allowing an unprivileged attacker to eavesdrop the user's credentials, such as login usernames and passwords, from their inputs through onscreen keyboard. Our attack targets on Qualcomm Adreno GPUs and investigate the amount of GPU overdraw when rendering the popups of user's key presses of inputs. Such GPU overdraw caused by each key press corresponds to unique variations of selected GPU performance counters, from which these key presses can be accurately inferred. Experiment results from practical use on multiple models of Android smartphones show that our attack can correctly infer more than 80% of user's credential inputs, but incur negligible amounts of computing overhead and network traffic on the victim device. To counter this attack, this paper suggests mitigations of access control on GPU performance counters, or applying obfuscations on the values of GPU performance counters. CCS CONCEPTS• Security and privacy → Systems security; • Human-centered computing → Ubiquitous and mobile computing.

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Section: Moving Circles and Trianglesmentioning

confidence: 99%

Eavesdropping user credentials via GPU side channels on smartphones

Yang

Chen

Huang

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…To port the implementation, we need to decide where to store T-tables in the GPU memory hierarchy and how to assign encryption jobs to GPU threads. In our prior work [13,16] we stored T-tables in the Global Memory unit, but the implementation becomes vulnerable to coalescing attacks [13,16]. Since T-tables are constant data and shared by all threads, they are a good candidate to store in the Shared Memory unit.…”

Section: Aes Encryptionmentioning

confidence: 99%

“…As an example, assuming a stride value 16, for a memory access instruction issued across a warp of 32 threads, we will generate the following 32 memory indices: {0, 16,32,48,64,80,96,112,128,144,160,176,192,208,224,240,256,272,288,304,320,336,352,368,384,400,416,432,448,464, 480, 496}. Using Equation 3, we have the following bank access indices for the warp: {0, 8,16,24,0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,…”

Section: Cache Bank Conflicts-based Side-channel Timing Channelmentioning

confidence: 99%

“…However, guessing multiple key bytes at the same time increases the computational complexity exponentially. The complexity of attacking a 2-key implementation is 2 16 . This approach will become computationally infeasible once the number of different keys used in the implementation reaches 5.…”

Section: Known Key Mappingmentioning

confidence: 99%

“…Moreover, GPU architectures incorporate a number of unique microarchitectural features to support high data throughput, but some of which become a source of side-channel timing leakage. Work by Jiang [13] and Karimi [16] explore the side-channel timing vulnerability of a GPU's on-chip coalescing unit, a GPU-specific microarchitecture to consolidate concurrent spatially local Global Memory access requests into fewer requests. Another study by Naghibijouybari et al [28] explores the timing-based covert-channel on GPUs through contention on caches, computational units, and memory operations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Bank Conflict-based Side-channel Timing Leakage of GPUs

Jiang

Fei

Kaeli

2019

ACM Trans. Archit. Code Optim.

Self Cite

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To prevent information leakage during program execution, modern software cryptographic implementations target constant-time function, where the number of instructions executed remains the same when program inputs change. However, the underlying microarchitecture behaves differently when processing different data inputs, impacting the execution time of the same instructions. These differences in execution time can covertly leak confidential information through a timing channel. Given the recent reports of covert channels present on commercial microprocessors, a number of microarchitectural features on CPUs have been reexamined from a timing leakage perspective. Unfortunately, a similar microarchitectural evaluation of the potential attack surfaces on GPUs has not been adequately performed. Several prior work has considered a timing channel based on the behavior of a GPU's coalescing unit. In this article, we identify a second finer-grained microarchitectural timing channel, related to the banking structure of the GPU's Shared Memory. By considering the timing channel caused by Shared Memory bank conflicts, we have developed a differential timing attack that can compromise table-based cryptographic algorithms. We implement our timing attack on an Nvidia Kepler K40 GPU and successfully recover the complete 128-bit encryption key of an Advanced Encryption Standard (AES) GPU implementation using 900,000 timing samples. We also evaluate the scalability of our attack method by attacking an implementation of the AES encryption algorithm that fully occupies the compute resources of the GPU. We extend our timing analysis onto other Nvidia architectures: Maxwell, Pascal, Volta, and Turing GPUs. We also discuss countermeasures and experiment with a novel multi-key implementation, evaluating its resistance to our side-channel timing attack and its associated performance overhead. CCS Concepts: • Security and privacy → Hardware security implementation; Side-channel analysis and countermeasures; • Computer systems organization → Single instruction, multiple data;

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