Regulation of cell death by transfer RNA.

SIGNIFICANCE: Both transfer RNA (tRNA) and cytochrome c are essential molecules for the survival of cells. tRNA decodes mRNA codons into amino-acid-building blocks in protein in all organisms, whereas cytochrome c functions in the electron transport chain that powers ATP synthesis in mitochondrion-containing eukaryotes. Additionally, in vertebrates, cytochrome c that is released from mitochondria is a potent inducer of apoptosis, activating apoptotic proteins (caspases) in the cytoplasm to dismantle cells. A better understanding of both tRNA and cytochrome c is essential for an insight into the regulation of cell life and death. RECENT ADVANCES: A recent study showed that the mitochondrion-released cytochrome c can be removed from the cell-death pathway by tRNA molecules. The direct binding of cytochrome c by tRNA provides a mechanism for tRNA to regulate cell death, beyond its role in gene expression. CRITICAL ISSUES: The nature of the tRNA-cytochrome c binding interaction remains unknown. The questions of how this interaction affects tRNA function, cellular metabolism, and apoptotic sensitivity are unanswered. FUTURE DIRECTIONS: Investigations into the critical issues raised above will improve the understanding of tRNA in the fundamental processes of cell death and metabolism. Such knowledge will inform therapies in cell death-related diseases

A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks

Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel analysis of neural networks allows parameter extraction even for networks with several multiple deep layers with high effectiveness. It is therefore of interest to implement a certain level of protection against these attacks. In this paper, we propose a desynchronization-based countermeasure that makes the timing analysis of activation functions harder. We analyze the timing properties of several activation functions and design the desynchronization in a way that the dependency on the input and the activation type is hidden. We experimentally verify the effectiveness of the countermeasure on a 32-bit ARM Cortex-M4 microcontroller and employ a t-test to show the side-channel information leakage. The overhead ultimately depends on the number of neurons in the fully-connected layer, for example, in the case of 4096 neurons in VGG-19, the overheads are between 2.8% and 11%.Comment: Accepted to the International Symposium on Cyber Security, Cryptology and Machine Learning 2023 (CSCML

Molecular Basis and Consequences of the Cytochrome c-tRNA Interaction.

The intrinsic apoptosis pathway occurs through the release of mitochondrial cytochrome c to the cytosol, where it promotes activation of the caspase family of proteases. The observation that tRNA binds to cytochrome c revealed a previously unexpected mode of apoptotic regulation. However, the molecular characteristics of this interaction, and its impact on each interaction partner, are not well understood. Using a novel fluorescence assay, we show here that cytochrome c binds to tRNA with an affinity comparable with other tRNA-protein binding interactions and with a molecular ratio of ∼3:1. Cytochrome c recognizes the tertiary structural features of tRNA, particularly in the core region. This binding is independent of the charging state of tRNA but is regulated by the redox state of cytochrome c. Compared with reduced cytochrome c, oxidized cytochrome c binds to tRNA with a weaker affinity, which correlates with its stronger pro-apoptotic activity. tRNA binding both facilitates cytochrome c reduction and inhibits the peroxidase activity of cytochrome c, which is involved in its release from mitochondria. Together, these findings provide new insights into the cytochrome c-tRNA interaction and apoptotic regulation

How Practical are Fault Injection Attacks, Really?

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations. There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis. In this paper we tackle the topic of practicality of FIA. We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks. To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark. Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device

Feeding Two Cats with One Bowl: On Designing a Fault and Side-Channel Resistant Software Encoding Scheme (Extended Version)

When it comes to side-channel countermeasures, software encoding schemes are becoming popular and provide a good level of security for general-purpose microcontrollers. However, these schemes are not designed to be fault resistant, and this property is discussed very rarely. Therefore, implementers have to pile up two different countermeasures in order to protect the algorithm against these two popular classes of attacks. In our paper, we discuss the fault resistance properties of encoding schemes in general. We define theoretical bounds that clearly show the possibilities and limitations of encoding-based countermeasures, together with trade-offs between side-channel and fault resistance. Moreover, we simulate several codes with respect to most popular fault models, using a general-purpose microcontroller assembly implementation. Our algorithm shows how to implement fault resistance to an encoding scheme that currently has the best side-channel resistant capabilities. As a result, we are able to design a code by using automated methods, that can provide the optimal trade-off between side-channel and fault resistance

SNIFF: Reverse Engineering of Neural Networks with Fault Attacks

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various fault injection techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. In this paper we explore the possibility to reverse engineer neural networks with the usage of fault attacks. SNIFF stands for sign bit flip fault, which enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of the model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than $10^{-13}$ with the usage of 64-bit floats, which improves the current state of the art by 6 orders of magnitude. Additionally, we discuss the protection techniques against fault injection attacks that can be applied to enhance the fault resistance