International Association for Cryptologic Research

International Association
for Cryptologic Research


Pallab Dasgupta


Learn from Your Faults: Leakage Assessment in Fault Attacks Using Deep Learning
Generic vulnerability assessment of cipher implementations against Fault Attacks (FA) is a largely unexplored research area. Security assessment against FA is critical for FA countermeasures. On several occasions, countermeasures fail to fulfil their sole purpose of preventing FA due to flawed design or implementation. This paper proposes a generic, simulation-based, statistical yes/no experiment for evaluating fault-assisted information leakage based on the principle of non-interference . It builds on an initial idea called ALAFA that utilizes t -test and its higher-order variants for detecting leakage at different moments of ciphertext distributions. In this paper, we improve this idea with a Deep Learning (DL)-based leakage detection test. The DL-based detection test is not specific to only moment-based leakages. It thus can expose leakages in several cases where t -test-based technique demands a prohibitively large number of ciphertexts. Further, we present two generalizations of the leakage assessment experiment—one for evaluating against the statistical ineffective fault model and another for assessing fault-induced leakages originating from “non-cryptographic” peripheral components of a security module. Finally, we explore techniques for efficiently covering the fault space of a block cipher by exploiting logic-level and cipher-level fault equivalences. The efficacy of our proposals has been evaluated on a rich test suite of hardened implementations, including an open-source Statistical Ineffective Fault Attack countermeasure and a hardware security module called Secured-Hardware-Extension.
ExpFault: An Automated Framework for Exploitable Fault Characterization in Block Ciphers 📺
Malicious exploitation of faults for extracting secrets is one of the most practical and potent threats to modern cryptographic primitives. Interestingly, not every possible fault for a cryptosystem is maliciously exploitable, and evaluation of the exploitability of a fault is nontrivial. In order to devise precise defense mechanisms against such rogue faults, a comprehensive knowledge is required about the exploitable part of the fault space of a cryptosystem. Unfortunately, the fault space is diversified and of formidable size even while a single cryptoprimitive is considered and traditional manual fault analysis techniques may often fall short to practically cover such a fault space within reasonable time. An automation for analyzing individual fault instances for their exploitability is thus inevitable. Such an automation is supposed to work as the core engine for analyzing the fault spaces of cryptographic primitives. In this paper, we propose an automation for evaluating the exploitability status of fault instances from block ciphers, mainly in the context of Differential Fault Analysis (DFA) attacks. The proposed framework is generic and scalable, which are perhaps the two most important features for covering diversified fault spaces of formidable size originating from different ciphers. As a proof-of-concept, we reconstruct some known attack examples on AES and PRESENT using the framework and finally analyze a recently proposed cipher GIFT [BPP+17] for the first time. It is found that the secret key of GIFT can be uniquely determined with 1 nibble fault instance injected at the beginning of the 25th round with a reasonable computational complexity of 214.