Chester Rebeiro

Fault attacks recover secret keys by exploiting faults injected during the execution of a block cipher. However, not all faults are exploitable and every exploitable fault is associated with an offline complexity to determine the key. The ideal fault attack would recover maximum key bits with minimum offline effort. Finding the ideal fault attack for a block cipher is a laborious manual task, which can take several months to years before such an attack is discovered. In this paper, we present a framework that would analyze block ciphers for their vulnerabilities to faults and automatically predict whether a differential fault attack would be successful. The framework, which we call XFC, uses colors to analyze the fault propagation and exploitability in the cipher. XFC would be able to (a) predict the key bits that can be derived by the fault attack and (b) estimate the offline complexity. It can thus be used to identify the ideal fault attack for a block cipher. As a proof of concept, we have applied XFC to the block ciphers AES, CLEFIA and SMS4 and were able to automatically derive fault attacks that correspond to the best known till date in the single fault model.

Fault injection attacks are a potent class of physical attacks where an injected fault during the execution of a cryptosystem is exploited to retrieve the secret key. The success of the fault attack depends on the software implementation and the underlying hardware. While there are several tools to detect vulnerable locations, none of them consider all the influencing aspects that, include the cipher algorithm, its implementation, and the underlying hardware. In this paper, we propose a three-stage secure compiler framework that automatically identifies exploitable instructions, quantifies exploitability, and adds countermeasures. It can enable tradeoffs based on performance requirements and the user's security requirements. We demonstrate the versatility of the framework by evaluating nine different block cipher implementations.

Fault attacks are a potent class of physical attacks that exploit a fault injected during device operation to steal secret keys from a cryptographic device. The success of a fault attack depends intricately on (a) the cryptographic properties of the cipher, (b) the program structure, and (c) the underlying hardware architecture. While there are several tools that automate the process of fault attack evaluation, none of them consider all three influencing aspects. This paper proposes a framework called FaultMeter that builds on the state-of-art by not just identifying fault vulnerable locations in a block cipher software, but also providing a quantification for each vulnerable location. The quantification provides a probability that an injected fault can be successfully exploited. It takes into consid-eration the cryptographic properties of the cipher, structure of the implementation, and the underlying Instruction Set Architecture’s (ISA) susceptibility to faults. We demonstrate an application of FaultMeter to automatically insert optimal amounts of countermeasures in a program to meet the user’s security requirements while minimizing overheads. We demonstrate the versatility of the FaultMeter framework by evaluating five cipher implementations on multiple hardware platforms, namely, ARM (32 and 64 bit), RISC-V (32 and 64 bit), TI MSP-430 (16-bit) and Intel x86 (64-bit).