ﻻ يوجد ملخص باللغة العربية
Cache timing attacks allow third-party observers to retrieve sensitive information from program executions. But, is it possible to automatically check the vulnerability of a program against cache timing attacks and then, automatically shield program executions against these attacks? For a given program, a cache configuration and an attack model, our CACHEFIX framework either verifies the cache side-channel freedom of the program or synthesizes a series of patches to ensure cache side-channel freedom during program execution. At the core of our framework is a novel symbolic verification technique based on automated abstraction refinement of cache semantics. The power of such a framework is to allow symbolic reasoning over counterexample traces and to combine it with runtime monitoring for eliminating cache side channels during program execution. Our evaluation with routines from OpenSSL, libfixedtimefixedpoint, GDK and FourQlib libraries reveals that our CACHEFIX approach (dis)proves cache sidechannel freedom within an average of 75 seconds. Besides, in all except one case, CACHEFIX synthesizes all patches within 20 minutes to ensure cache side-channel freedom of the respective routines during execution.
We propose a data-driven method for synthesizing a static analyzer to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such a static analyzer, which can be labor intensive, error p
We demonstrate the feasibility of database reconstruction under a cache side-channel attack on SQLite. Specifically, we present a Flush+Reload attack on SQLite that obtains approximate (or noisy) volumes of range queries made to a private database. W
Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally, even practitioners who do not plan to work in security may benefit f
Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversarys capability to conduct black-box attacks against the model. This paper presents the first in-depth sec
Side channels represent a broad class of security vulnerabilities that have been demonstrated to exist in many applications. Because completely eliminating side channels often leads to prohibitively high overhead, there is a need for a principled tra