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The high-profile Spectre attack and its variants have revealed that speculative execution may leave secret-dependent footprints in the cache, allowing an attacker to learn confidential data. However, existing static side-channel detectors either ignore speculative execution, leading to false negatives, or lack a precise cache model, leading to false positives. In this paper, somewhat surprisingly, we show that it is challenging to develop a speculation-aware static analysis with precise cache models: a combination of existing works does not necessarily catch all cache side channels. Motivated by this observation, we present a new semantic definition of security against cache-based side-channel attacks, called Speculative-Aware noninterference (SANI), which is applicable to a variety of attacks and cache models. We also develop SpecSafe to detect the violations of SANI. Unlike other speculation-aware symbolic executors, SpecSafe employs a novel program transformation so that SANI can be soundly checked by speculation-unaware side-channel detectors. SpecSafe is shown to be both scalable and accurate on a set of moderately sized benchmarks, including commonly used cryptography libraries.
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PREDATOR: A Cache Side-Channel Attack Detector Based on Precise Event Monitoring
Abstract—Recent work has demonstrated the security risk associated with micro-architecture side-channels. The cache timing side-channel is a particularly popular target due to its availability and high leakage bandwidth. Existing proposals for defending cache side-channel attacks either degrade cache performance and/or limit cache sharing, hence, should only be invoked when the system is under attack. A lightweight monitoring mechanism that detects malicious micro-architecture manipulation in realistic environments is essential for the judicious deployment of these defense mechanisms. In this paper, we propose PREDATOR, a cache side-channel attack detector that identifies cache events caused by an attacker. To detect side-channel attacks in noisy environments, we take advantage of the observation that, unlike non-specific noises, an active attacker alters victim’s micro-architectural states on security critical accesses and thus causes the victim extra cache events on those accesses. PREDATOR uses precise performance counters to collect detailed victim’s access information and analyzes location-based deviations. PREDATOR is capable of detecting five different attacks with high accuracy and limited performance overhead in complex noisy execution environments. PREDATOR remains effective even when the attacker slows the attack rate by 256 times. Furthermore, PREDATOR is able to accurately report details about the attack such as the instruction that accesses the attacked data. In the case of GnuPG RSA [20], PREDATOR can pinpoint the square/multiply operations in the Modulo-Reduce algorithm; and in the case of OpenSSL AES [45], it can identify the accesses to the Te-Table.
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- Award ID(s):
- 2106771
- PAR ID:
- 10357261
- Date Published:
- Journal Name:
- 2022 IEEE International Symposium on Secure and Private Execution Environment Design
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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