FEDS: Comprehensive Fault Attack Exploitability Detection for Software Implementations of Block Ciphers

Abstract

Fault injection attacks are one of the most powerful forms of cryptanalytic attacks on ciphers. A single, precisely injected fault during the execution of a cipher like the AES, can completely reveal the key within a few milliseconds. Software implementations of ciphers, therefore, need to be thoroughly evaluated for such attacks. In recent years, automated tools have been developed to perform these evaluations. These tools either work on the cipher algorithm or on their implementations. Tools that work at the algorithm level can provide a comprehensive assessment of fault attack vulnerability for different fault attacks and with different fault models. Their application is, however, restricted because every realization of the cipher has unique vulnerabilities. On the other hand, tools that work on cipher implementations have a much wider application but are often restricted by the range of fault attacks and the number of fault models they can evaluate.In this paper, we propose a framework, called FEDS, that uses a combination of compiler techniques and model checking to merge the advantages of both, algorithmic level tools as well as implementation level tools. Like the algorithmic level tools, FEDS can provide a comprehensive assessment of fault attack exploitability considering a wide range of fault attacks and fault models. Like implementation level tools, FEDS works with implementations, therefore has wide application. We demonstrate the versatility of FEDS by evaluating seven different implementations of AES (including bitsliced implementation) and implementations of CLEFIA and CAMELLIA for Differential Fault Attacks. The framework automatically identifies exploitable instructions in all implementations. Further, we present an application of FEDS in a Fault Attack Aware Compiler, that can automatically identify and protect exploitable regions of the code. We demonstrate that the compiler can generate significantly more efficient code than a naïvely protected equivalent, while maintaining the same level of protection.

SoK: Assisted Fault Simulation

“…AI models learn from a training dataset to develop and hone their search space rather than exhaustively trying all combinations. Tools such as XFC [18], SAFARI [26], FEDS [17], SOLOMON [33] and ALAFA [29] use the concept of non-interference to their advantage, while tools such as DL-FALAT [27], or ExpFault [31], or the ones presented in [30] and [28] employ deep learning and data mining, respectively, to counter the limitations outlined in Sect. 6.…”

“…The target audience for this paper is developers of general-purpose software and not specifically cryptographic implementations. Although many fault simulators [1,5,6,14,[16][17][18]24,[26][27][28][29]31,33] can verify the implementations of cryptographic algorithms, we exclude them in this work because there is already an overview of such tools [4]. We found four fault simulators in the public domain suitable for such a developer, namely FiSim [25], ZOFI [23], ARMORY [13] and ARCHIE [12].…”