Related papers: Security Mitigations for Return-Oriented Programmi…

Low-volume Denial-of-Service ({\mu}DoS) attacks have been demonstrated to fundamentally bypass traditional DoS mitigation schemes based on the flow and volume of network packets. In this paper, we propose a data-driven approach, called…

Nowadays, in operating systems, numerous protection mechanisms prevent or limit the user-mode applicationsto access the kernels internal information. This is regularlycarried out by software-based defenses such as Address Space Layout…

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call…

Deep neural networks (DNNs) are vulnerable to adversarial examples obtained by adding small perturbations to original examples. The added perturbations in existing attacks are mainly determined by the gradient of the loss function with…

We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs). Most state-of-the-art defenses rely on the statistical sanitization of the "inputs" or "latent DNN…

Securing the near-real-time (near-RT) control operations in Open Radio Access Networks (Open RAN) is increasingly critical, yet remains insufficiently addressed, as new runtime threats target the control loop while the system is…

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of…

With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant…

The increasing sophistication of large vision-language models (LVLMs) has been accompanied by advances in safety alignment mechanisms designed to prevent harmful content generation. However, these defenses remain vulnerable to sophisticated…

As LLM agents transition from digital assistants to physical controllers in autonomous systems and robotics, they face an escalating threat from indirect prompt injection. By embedding adversarial instructions into the results of tool…

For network computers to communicate to one another, they need to know one another's IP address and MAC address. Address Resolution Protocol (ARP) is developed to find the Ethernet address that map to a specific IP address. The source…

This paper investigates the performance of various Border Gateway Protocol (BGP) security policies against multiple attack scenarios using different deployment strategies. Through extensive simulations, we evaluate the effectiveness of…

Despite demonstrating superior rate-distortion (RD) performance, learning-based image compression (LIC) algorithms have been found to be vulnerable to malicious perturbations in recent studies. However, the adversarial attacks considered in…

After a plethora of high-profile RowHammer attacks, CPU and DRAM vendors scrambled to deliver what was meant to be the definitive hardware solution against the RowHammer problem: Target Row Refresh (TRR). A common belief among practitioners…

In pervasive systems, mobile devices and other sensors access Gateways, which are Servers that communicate with the devices, provide low latency services, connect them with each other, and connect them to the Internet and backbone networks.…

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority…

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to…

Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail…

Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results. Although defenses against adversarial attacks…

We study masked implementation's security when an adversary randomly probes each of its internal variables, intending to recover non-trivial knowledge about its secrets. We introduce a novel metric called Secret Recovery Probability (SRP)…