ConTExT: A Generic Approach for Mitigating Spectre

Schwarz, Michael; Lipp, Moritz; Canella, Claudio; Schilling, Robert; Kargl, Florian; Gruss, Daniel

doi:10.14722/ndss.2020.24271

Cited by 52 publications

(33 citation statements)

References 39 publications

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“…With additional hardware changes, those techniques fundamentally mitigate microarchitectural attacks by isolating execution results of transient instructions until they commit [53]- [55] or by prohibiting transient executions within predefined secure domain [56].…”

Section: A Hardware-based Mitigationmentioning

confidence: 99%

Multibyte Microarchitectural Data Sampling and its Application to Session Key Extraction Attacks

Shin

2021

IEEE Access

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Microarchitectural data sampling (MDS) attacks leak secret data from the internal buffers of a processor to the attacker during transient execution. Because of the narrow window of transient execution, previous MDS attacks relied on repetitive sampling to obtain arbitrarily sized data from the buffer. However, as an MDS attacker cannot control the address for data leakage, such an approach significantly degrades the signal-to-noise ratio in the sampled data. In this paper, we propose a novel multibyte microarchitectural data sampling technique for performing MDS attacks. The proposed technique allows several continuous bytes to be captured in one execution without repetition of sampling. The implementation of the technique is quite challenging, because a transient execution window is not sufficiently large to allow multibyte sampling to be completed. We address this problem by leveraging a return stack buffer-based speculation technique, which originally was used for variants of Spectre-type attacks. We repurpose it to enlarge the transient execution window in our attack. Our implementations can capture data of up to 16 bytes in length in one execution from a line-fill buffer. To validate the effectiveness of the multibyte sampling technique, we demonstrate session key extraction attacks against secure network protocols. In particular, our objective is to extract AES-128 and AES-256 keys from TLS and SSH applications. To recover session keys in a postprocessing phase efficiently, we also propose a novel clustering-based search method that assembles the bytes of interest from the noisy sampled data. The experimental results show that our technique can successfully extract AES-128/256 session keys from victim applications with a probability of at least 98% and a reasonable search complexity. INDEX TERMS Microarchitectural data sampling, Transient execution attack, Session key extraction attackRecently, new vulnerabilities have been found on microarchitectural buffers inside some Intel processors [3], [4]. The

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Section: A Hardware-based Mitigationmentioning

confidence: 99%

Multibyte Microarchitectural Data Sampling and its Application to Session Key Extraction Attacks

Shin

2021

IEEE Access

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“…For instance, page table isolation (e.g., KPTI of Linux) PTE inversion, and L1d flush [5], compilerbased mitigation [12,23,30,40] provides generic solutions for exception-based and misprediction-based speculative execution attacks. SPECCFI [24], ConTExT [36] mitigates a specific type of speculative attack. Furthermore, many works focus on detecting the code gadget of speculative execution attacks [13,18,20,28,33,47].…”

Section: Related Workmentioning

confidence: 99%

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Wang

Chen

Cheng³

et al. 2021

Detection of Intrusions and Malware, and Vulnerability Assessment

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This paper presents Specularizer, a framework for uncovering speculative execution attacks using performance tracing features available in commodity processors. It is motivated by the practical difficulty of eradicating such vulnerabilities in the design of CPU hardware and operating systems and the principle of defense-in-depth. The key idea of Specularizer is the use of Hardware Performance Counters and Processor Trace to perform lightweight monitoring of production applications and the use of machine learning techniques for identifying the occurrence of the attacks during offline forensics analysis. Different from prior works that use performance counters to detect side-channel attacks, Specularizer monitors triggers of the critical paths of the speculative execution attacks, thus making the detection mechanisms robust to different choices of side channels used in the attacks. To evaluate Specularizer, we model all known types of exception-based and misprediction-based speculative execution attacks and automatically generate thousands of attack variants. Experimental results show that Specularizer yields superior detection accuracy and the online tracing of Specularizer incur reasonable overhead.

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“…OISA [71] ensures that the accesses to sensitive data must use special instructions from a customized instruction subset, and these instructions cannot be executed out of order. ConTExT [49] marks the protected memory pages and registers to prevent their data from being obtained in the out-of-order execution state. Comparing with these defenses, SPECBOX can protect all the secret data from Spectre attacks instead of the special data assigned by software.…”

Section: Related Workmentioning

confidence: 99%

SpecBox: A Label-Based Transparent Speculation Scheme Against Transient Execution Attacks

Tang¹,

Wu²,

Wang³

et al. 2021

Preprint

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Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient execution attacks, such as Spectre. Many defenses are proposed to address this problem, but they all face various challenges: (1) Tracking data flow in the instruction pipeline could comprehensively address this problem, but it could cause pipeline stalls and incur high performance overhead; (2) Making side effect of speculative execution imperceptible to attackers, but it often needs additional storage components and complicated data movement operations. In this paper, we propose a label-based transparent speculation scheme called SPECBOX. It dynamically partitions the cache system to isolate speculative data and nonspeculative data, which can prevent transient execution from being observed by subsequent execution. Moreover, it uses thread ownership semaphores to prevent speculative data from being accessed across cores. In addition, SPECBOX also enhances the auxiliary components in the cache system against transient execution attacks, such as hardware prefetcher. Our security analysis shows that SPECBOX is secure and the performance evaluation shows that the performance overhead on SPEC CPU 2006 and PARSEC-3.0 benchmarks is small.

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ConTExT: A Generic Approach for Mitigating Spectre

Cited by 52 publications

References 39 publications

Multibyte Microarchitectural Data Sampling and its Application to Session Key Extraction Attacks

Multibyte Microarchitectural Data Sampling and its Application to Session Key Extraction Attacks

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

SpecBox: A Label-Based Transparent Speculation Scheme Against Transient Execution Attacks

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