International Association for Cryptologic Research

International Association
for Cryptologic Research


Manaar Alam


“Whispering MLaaS”: Exploiting Timing Channels to Compromise User Privacy in Deep Neural Networks
While recent advancements of Deep Learning (DL) in solving complex real-world tasks have spurred their popularity, the usage of privacy-rich data for their training in varied applications has made them an overly-exposed threat surface for privacy violations. Moreover, the rapid adoption of cloud-based Machine-Learning-asa-Service (MLaaS) has broadened the threat surface to various remote side-channel attacks. In this paper, for the first time, we show one such privacy violation by observing a data-dependent timing side-channel (naming this to be Class-Leakage) originating from non-constant time branching operation in a widely popular DL framework, namely PyTorch. We further escalate this timing variability to a practical inference-time attack where an adversary with user level privileges and having hard-label black-box access to an MLaaS can exploit Class-Leakage to compromise the privacy of MLaaS users. DL models have also been shown to be vulnerable to Membership Inference Attack (MIA), where the primary objective of an adversary is to deduce whether any particular data has been used while training the model. Differential Privacy (DP) has been proposed in recent literature as a popular countermeasure against MIA, where inclusivity and exclusivity of a data-point in a dataset cannot be ascertained by definition. In this paper, we also demonstrate that the existence of a data-point within the training dataset of a DL model secured with DP can still be distinguished using the identified timing side-channel. In addition, we propose an efficient countermeasure to the problem by introducing constant-time branching operation that alleviates the Class-Leakage. We validate the approach using five pre-trained DL models trained on two standard benchmarking image classification datasets, CIFAR-10 and CIFAR-100, over two different computing environments having Intel Xeon and Intel i7 processors.
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.
RASSLE: Return Address Stack based Side-channel LEakage 📺
Microarchitectural attacks on computing systems often stem from simple artefacts in the underlying architecture. In this paper, we focus on the Return Address Stack (RAS), a small hardware stack present in modern processors to reduce the branch miss penalty by storing the return addresses of each function call. The RAS is useful to handle specifically the branch predictions for the RET instructions which are not accurately predicted by the typical branch prediction units. In particular, we envisage a spy process who crafts an overflow condition in the RAS by filling it with arbitrary return addresses, and wrestles with a concurrent process to establish a timing side channel between them. We call this attack principle, RASSLE 1 (Return Address Stack based Side-channel Leakage), which an adversary can launch on modern processors by first reverse engineering the RAS using a generic methodology exploiting the established timing channel. Subsequently, we show three concrete attack scenarios: i) How a spy can establish a covert channel with another co-residing process? ii) How RASSLE can be utilized to determine the secret key of the P-384 curves in OpenSSL (v1.1.1 library)? iii) How an Elliptic Curve Digital Signature Algorithm (ECDSA) secret key on P-256 curve of OpenSSL can be revealed using Lattice Attack on partially leaked nonces with the aid of RASSLE? In this attack, we show that the OpenSSL implementation of scalar multiplication on this curve has varying number of add-and-sub function calls, which depends on the secret scalar bits. We demonstrate through several experiments that the number of add-and-sub function calls can be used to template the secret bit, which can be picked up by the spy using the principles of RASSLE. Finally, we demonstrate a full end-to-end attack on OpenSSL ECDSA using curve parameters of curve P-256. In this part of our experiments with RASSLE, we abuse the deadline scheduler policy to attain perfect synchronization between the spy and victim, without any aid of induced synchronization from the victim code. This synchronization and timing leakage through RASSLE is sufficient to retrieve the Most Significant Bits (MSB) of the ephemeral nonces used while signature generation, from which we subsequently retrieve the secret signing key of the sender applying the Hidden Number Problem. 1RASSLE is a non-standard spelling for wrestle.