Related papers: Attacking the Nintendo 3DS Boot ROMs

Modern x86 processors support an AVX instruction set to boost performance. However, this extension may cause security issues. We discovered that there are vulnerable properties in implementing masked load/store instructions. Based on this,…

Reading or writing outside the bounds of a buffer is a serious security vulnerability that has been exploited in numerous occasions. These attacks can be prevented by ensuring that every buffer is only accessed within its specified bounds.…

Deep neural networks (NNs) for computer vision are vulnerable to adversarial attacks, i.e., miniscule malicious changes to inputs may induce unintuitive outputs. One key approach to verify and mitigate such robustness issues is to falsify…

Rowhammer attacks have emerged as a significant threat to modern DRAM-based memory systems, leveraging frequent memory accesses to induce bit flips in adjacent memory cells. This work-in-progress paper presents an adaptive, many-sided…

Image anomaly detection (IAD) is essential in applications such as industrial inspection, medical imaging, and security. Despite the progress achieved with deep learning models like Deep Semi-Supervised Anomaly Detection (DeepSAD), these…

Over the years, the increasingly complex and interconnected vehicles raised the need for effective and efficient Intrusion Detection Systems against on-board networks. In light of the stringent domain requirements and the heterogeneity of…

Fault injection attacks (FIA) pose significant security threats to embedded systems as they exploit weaknesses across multiple layers, including system software, instruction set architecture (ISA), microarchitecture, and physical hardware.…

While security vulnerabilities in traditional Deep Neural Networks (DNNs) have been extensively studied, the susceptibility of Spiking Neural Networks (SNNs) to adversarial attacks remains mostly underexplored. Until now, the mechanisms to…

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined…

Backdoor attacks pose a persistent security risk to deep neural networks (DNNs) due to their stealth and durability. While recent research has explored leveraging model unlearning mechanisms to enhance backdoor concealment, existing attack…

We present a new type of clogging DoS attacks, with the highest amplification factors achieved by off-path attackers, using only puppets, i.e., sandboxed malware on victim machines. Specifically, we present off-path variants of the Opt-ack,…

The Replay Protected Memory Block (RPMB) in modern storage systems provides a secure area where data integrity is ensured by authentication. This block is used in digital devices to store pivotal information that must be safeguarded against…

Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper, we focus on the so-called \textit{backdoor attack},…

In the official whitepaper of Secure Encrypted Virtualization with Secure Nested Paging (SEV-SNP), AMD explicitly emphasizes the capability to prevent Trusted Computing Base (TCB) rollback attacks. Cryptographically, this is realized by…

Signature-based and protocol-based intrusion detection systems (IDS) are employed as means to reveal content-based network attacks. Such systems have proven to be effective in identifying known intrusion attempts and exploits but they fail…

Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks.…

With the broad application of deep neural networks (DNNs), backdoor attacks have gradually attracted attention. Backdoor attacks are insidious, and poisoned models perform well on benign samples and are only triggered when given specific…

Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. These attacks can occur in almost every stage of the deep learning pipeline. Although the attacked model behaves normally on benign…

The widespread adoption of Internet of Things has led to many security issues. Recently, there have been malware attacks on IoT devices, the most prominent one being that of Mirai. IoT devices such as IP cameras, DVRs and routers were…

This work explores backdoor attacks for automatic speech recognition systems where we inject inaudible triggers. By doing so, we make the backdoor attack challenging to detect for legitimate users, and thus, potentially more dangerous. We…