Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery

Ahn, Jaeguk; Jin, Cheolgyu; Kim, Jiho; Rhu, Minsoo; Fei, Yunsi; Kaeli, David; Kim, John

doi:10.1109/hpca51647.2021.00036

Cited by 4 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As on CPUs, the GPU cache is a particularly interesting resource for side channels. Consequently, prior work also replicated well-known CPU cache side-channel attacks on GPUs [6,10,12,15,16], albeit only in native code so far.…”

Section: Introductionmentioning

confidence: 83%

“…Jiang et al [7][8][9] present a cache-based attack, a shared memory attack, and a bank-conflict attack, all leading to a key recovery attack on AES. Similarly, Ahn et al [6] exploit cache conflicts to recover an AES key from a GPU implementation. While they rely on cycle-accurate timers, our attack works from the browser without a timer.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, general-purpose GPU computing also becomes a more interesting attack target. Recent research confirms these security concerns, as GPUs have become a recurrent target of side-channel attacks, exploiting various shared components [3,[6][7][8][9][10][11][12]. Furthermore, attackers may also leverage the GPU to attack other system components [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generic and Automated Drive-by GPU Cache Attacks from the Browser

Giner,

Czerny,

Gruber

et al. 2024

Proceedings of the 19th ACM Asia Conference on Computer and Communications Security

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generic and Automated Drive-by GPU Cache Attacks from the Browser

Giner,

Czerny,

Gruber

et al. 2024

Proceedings of the 19th ACM Asia Conference on Computer and Communications Security

View full text Add to dashboard Cite

“…Ahn et al revealed the characteristics of local caches in the modern GPU architectures are different from the original cache design. They disclosed a new correlation-based attack that uses negative correlations and cache collisions [35]. Luo et al presented that encryption keys of RSA ported to a GPU domain can be extracted by measuring the execution latencies of several window units of RSA [36].…”

Section: Related Work a Security Attacks On Gpumentioning

confidence: 99%

“…Lin et al presented a software-based modification that changes the compositions of T-tables make AES generates a fixed number of memory requests [41]. Ahn et al proposed a defense mechanism, called Trident, which makes memory transactions bypass the L1 cache randomly [35]. Even though these defense solutions can be effective to protect secret data from the correlation-based attacks, such approaches cause significant performance drops.…”

Section: B Defenses Against Gpu Security Attacksmentioning

confidence: 99%

GhostLeg: Selective Memory Coalescing for Secure GPU Architecture

et al. 2022

View full text Add to dashboard Cite

Architectural considerations for secure executions are getting more critical for GPU since popular security applications and libraries have been ported to a GPU domain to rely on GPU's massively parallel computations. Recent studies disclosed the security attack models that exploit GPU's architectural vulnerabilities to leak the secret keys of AES. The attack models exploit the high correlations between the execution time of a kernel and the number of memory requests generated from memory coalescing. Thus the prior architectural defenses provide secure executions by randomizing or restricting the memory coalescing from load warps. However, those defense approaches result in significant performance overhead since memory coalescing is an essential feature for improving the performance of GPU. In this paper, we propose GhostLeg, an efficient architectural defense approach against correlation-based GPU security attacks. GhostLeg selectively applies secure executions for load warps to minimize performance overhead induced by concealing memory coalescing behavior. Our analysis of AES reveals that only the load warps whose index addresses are influenced by secret keys are vulnerable to security attacks. In order to minimize the performance overhead by secure executions, GhostLeg pinpoints the load warps that require secure executions based on the class of a source register. The secure flag assigned to each register can be set by propagation from non-deterministic user data (GhostLeg-ND) or a specific directive marked by programmers (GhostLeg-Key). Our evaluation shows that GhostLeg guarantees secure executions against the correlation-based attacks and GhostLeg-ND exhibits 54.7% higher performance compared to the stateof-the-art GPU defense solution.INDEX TERMS GPU, secure architecture, security attack, memory coalescing.

show abstract

Microarchitectural Attacks in Heterogeneous Systems: A Survey

2022

View full text Add to dashboard Cite

With the increasing proliferation of hardware accelerators and the predicted continued increase in the heterogeneity of future computing systems, it is necessary to understand the security properties of such systems. In this survey article, we consider the security of heterogeneous systems against microarchitectural attacks, with a focus on covert- and side-channel attacks, as well as fault injection attacks. We review works that have explored the vulnerability of the individual accelerators (such as Graphical Processing Units, GPUs and Field Programmable Gate Arrays, FPGAs) against these attacks, as well as efforts to mitigate them. We also consider the vulnerability of other components within a heterogeneous system such as the interconnect and memory component. We believe that this survey is especially timely, as new accelerators and heterogeneous systems are being designed such that these designs understand the security threats and develop systems that are not only performant but also secure.

show abstract

Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery

Cited by 4 publications

References 27 publications

Generic and Automated Drive-by GPU Cache Attacks from the Browser

Generic and Automated Drive-by GPU Cache Attacks from the Browser

GhostLeg: Selective Memory Coalescing for Secure GPU Architecture

Microarchitectural Attacks in Heterogeneous Systems: A Survey

Contact Info

Product

Resources

About