Related papers: Secure Rewind and Discard on ARM Morello

Up to 10% of memory-safety vulnerabilities in languages like C and C++ stem from uninitialized variables. This work addresses the prevalence and lack of adequate software mitigations for uninitialized memory issues, proposing architectural…

The widespread deployment of embedded systems in critical infrastructures, interconnected edge devices like autonomous drones, and smart industrial systems requires robust security measures. Compromised systems increase the risks of…

Memory corruption attacks have been prevalent in software for a long time. Some mitigation strategies against these attacks do exist, but they are not as far-reaching or as efficient as the CHERI architecture. CHERI uses capabilities to…

MMU-less Linux variant lacks security because it does not have protection or isolation mechanisms. It also does not use MPUs as they do not fit with its software model because of the design drawbacks of MPUs (\ie coarse-grained protection…

The use of unsafe programming languages still remains one of the major root causes of software vulnerabilities. Although well-known defenses that detect and mitigate memory-safety related issues exist, they don't address the challenge of…

While memory corruption bugs stemming from the use of unsafe programming languages are an old and well-researched problem, the resulting vulnerabilities still dominate real-world exploitation today. Various mitigations have been proposed to…

CHERI (Capability Hardware Enhanced RISC Instructions) is a novel hardware designed to address memory safety issues. By replacing traditional pointers with hardware capabilities, it enhances security in modern software systems. A Virtual…

Several open-source memory allocators have been ported to CHERI, a hardware capability platform. In this paper we examine the security and performance of these allocators when run under CheriBSD on Arm's experimental Morello platform. We…

In-memory computing architectures provide a much needed solution to energy-efficiency barriers posed by Von-Neumann computing due to the movement of data between the processor and the memory. Functions implemented in such in-memory…

Lack of memory-safety and exposure to side channels are two prominent, persistent challenges for the secure implementation of software. Memory-safe programming languages promise to significantly reduce the prevalence of memory-safety bugs,…

A digital security-by-design computer architecture, like CHERI, lets you program without fear of buffer overflows or other memory safety errors, but CHERI also rewrites some of the assumptions about how C works and how fundamental types…

Fine-tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine-tuning attacks in which adversarial data removes safeguards and induces unsafe behaviors. We propose SPARD, a defense…

Resistive random-access memory (RRAM) is gaining popularity due to its ability to offer computing within the memory and its non-volatile nature. The unique properties of RRAM, such as binary switching, multi-state switching, and device…

The security goals of cloud providers and users include memory confidentiality and integrity, which requires implementing Replay-Attack protection (RAP). RAP can be achieved using integrity trees or mutually authenticated channels.…

In this work we present the Secure Machine, SeM for short, a CPU architecture extension for secure computing. SeM uses a small amount of in-chip additional hardware that monitors key communication channels inside the CPU chip, and only acts…

Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were the early role models preventing primitive code injection and return-oriented programming (ROP) attacks. Notably, these techniques…

There is growing interest in securing the hardware foundations software stacks build upon. However, before making any investment decision, software and hardware supply chain stakeholders require evidence from realistic, multiple long-term…

Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject undetectable perturbations into the data during inference,…

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine…

Large Language Models (LLMs) remain vulnerable to jailbreak attacks, which attempt to elicit harmful responses from LLMs. The evolving nature and diversity of these attacks pose many challenges for defense systems, including (1) adaptation…