FastSpec: Scalable Generation and Detection of Spectre Gadgets Using Neural Embeddings M. Caner Tol Berk Gulmezoglu Koray Yurtseven Berk Sunar Worcester Polytechnic Institute Iowa State University Worcester Polytechnic Institute Worcester Polytechnic Institute
[email protected] [email protected] [email protected] [email protected] Abstract—Several techniques have been proposed to detect [4] are implemented against the SGX environment and vulnerable Spectre gadgets in widely deployed commercial even remotely over the network [5]. These attacks show software. Unfortunately, detection techniques proposed so the applicability of Spectre attacks in the wild. far rely on hand-written rules which fall short in covering Unfortunately, chip vendors try to patch the leakages subtle variations of known Spectre gadgets as well as demand one-by-one with microcode updates rather than fixing a huge amount of time to analyze each conditional branch the flaws by changing their hardware designs. Therefore, in software. Moreover, detection tool evaluations are based developers rely on automated malware analysis tools to only on a handful of these gadgets, as it requires arduous eliminate mistakenly placed Spectre gadgets in their pro- effort to craft new gadgets manually. grams. The proposed detection tools mostly implement In this work, we employ both fuzzing and deep learning taint analysis [6] and symbolic execution [7], [8] to iden- techniques to automate the generation and detection of tify potential gadgets in benign applications. However, Spectre gadgets. We first create a diverse set of Spectre-V1 the methods proposed so far are associated with two gadgets by introducing perturbations to the known gadgets. shortcomings: (1) the low number of Spectre gadgets Using mutational fuzzing, we produce a data set with more prevents the comprehensive evaluation of the tools, (2) than 1 million Spectre-V1 gadgets which is the largest time consumption exponentially increases when the binary Spectre gadget data set built to date.