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The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted stages of the supply chain, raising concerns about reverse engineering, piracy, tampering,…
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…
Voltage fault injection (FI) is a well-known attack technique that can be used to force faulty behavior in processors during their operation. Glitching the supply voltage can cause data value corruption, skip security checks, or enable…
This paper investigates the security issue of the data replay attacks on the control systems. The attacker is assumed to interfere with the control system process in a steady-state case. The problem is presented as the standard way to…
LLM-integrated applications and agents are vulnerable to prompt injection attacks, where adversaries embed malicious instructions within seemingly benign input data to manipulate the LLM's intended behavior. Recent defenses based on…
Static side-channel analysis attacks, which rely on a stopped clock to extract sensitive information, pose a growing threat to embedded systems' security. To protect against such attacks, several proposed defenses aim to detect unexpected…
In this letter, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their…
Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perform such an analysis, current attacks on optical flow methods…
Despite the general capabilities of Large Language Models (LLM), these models still request fine-tuning or adaptation with customized data when meeting specific business demands. However, this process inevitably introduces new threats,…
Applications of reversible circuits can be found in the fields of low-power computation, cryptography, communications, digital signal processing, and the emerging field of quantum computation. Furthermore, prototype circuits for low-power…
Large language models (LLMs) possess extensive knowledge and question-answering capabilities, having been widely deployed in privacy-sensitive domains like finance and medical consultation. During LLM inferences, cache-sharing methods are…
Camouflaging gate techniques are typically used in hardware security to prevent reverse engineering. Layout level camouflaging by adding dummy contacts ensures some level of protection against extracting the correct netlist. Threshold…
Constructing good test cases is difficult and time-consuming, especially if the system under test is still under development and its exact behavior is not yet fixed. We propose a new approach to compute test strategies for reactive systems…
Federated learning is a promising approach for training machine learning models while preserving data privacy. However, its distributed nature makes it vulnerable to backdoor attacks, particularly in NLP tasks, where related research…
The functionality of electronic circuits can be seriously impaired by the occurrence of dynamic hardware faults. Particularly, for digital ultra low-power systems, a reduced safety margin can increase the probability of dynamic failures.…
Advances in reverse engineering make it challenging to deploy any on-chip information in a way that is hidden from a determined attacker. A variety of techniques have been proposed for design obfuscation including look-alike cells in which…
Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs were revealed by stealthy adversarial examples where crafted…
We introduce LLA, an effective intellectual property (IP) protection scheme for generative AI models. LLA leverages the synergy between hardware and software to defend against various supply chain threats, including model theft, model…
Backdoor attacks pose a significant threat to deep neural networks, particularly as recent advancements have led to increasingly subtle implantation, making the defense more challenging. Existing defense mechanisms typically rely on an…
In this paper, we study the inference accuracy of the Resistive Random Access Memory (ReRAM) neuromorphic circuit due to stuck-at faults (stuck-on, stuck-off, and stuck at a certain resistive value). A simulation framework using Python is…