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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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Online Detection of Spectre Attacks Using Microarchitectural Traces from Performance Counters
To improve processor performance, computer architects have adopted such acceleration techniques as speculative execution and caching. However, researchers have recently discovered that this approach implies inherent security flaws, as exploited by Meltdown and Spectre. Attacks targeting these vulnerabilities can leak protected data through side channels such as data cache timing by exploiting mis-speculated executions. The flaws can be catastrophic because they are fundamental and widespread and they affect many modern processors. Mitigating the effect of Meltdown is relatively straightforward in that it entails a software-based fix which has already been deployed by major OS vendors. However, to this day, there is no effective mitigation to Spectre. Fixing the problem may require a redesign of the architecture for conditional execution in future processors. In addition, a Spectre attack is hard to detect using traditional software-based antivirus techniques because it does not leave traces in traditional log files. In this paper, we proposed to monitor microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters to detect Spectre during attack runtime. Our detector was able to achieve 0% false negatives with less than 1% false positives using various machine learning classifiers with a reasonable performance overhead.
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- Award ID(s):
- 1763793
- PAR ID:
- 10098124
- Date Published:
- Journal Name:
- Proceedings of the 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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