Rust is a rapidly growing system programming language that ensures a speed comparable to traditional C/C++ system programming languages, along with the additional benefit of guaranteed memory safety. However, Rust’s strict security rules make implementing and executing some features challenging. To address this, Rust has introduced unsafe Rust, which is less constrained by these strict rules. Nevertheless, these unsafe Rust, where strict Rust security rules are not fully applied, can cause temporal and spatial memory bugs that account for 22% of the Rust bugs reported between 2016 and 2023.In this paper, we propose an efficient address sanitizer design customized for Rust, called ERASan, to detect memory bugs in Rust programs more efficiently than prior work. Based on our thorough analysis of safe and unsafe Rust programming language standards as well as memory bugs found in real-world Rust programs over the past years, we design and implement ERASan to only instrument memory accesses in both safe and unsafe code areas where Rust cannot guarantee safety. We evaluate ERASan with several real-world applications. ERASan removes an average of 90.03% of ASan’s memory access checks. Due to this, ERASan significantly reduces ASan’s performance overhead by an average of 239.05% without harming its bug-finding ability.

Cross-platform binary analysis requires a common representation of binaries across platforms, on which a specific analysis can be performed. Recent work proposed to learn low-dimensional, numeric vector representations (i.e., embeddings) of disassembled binary code, and perform binary analysis in the embedding space. Unfortunately, however, existing techniques fall short in that they are either (i) specific to a single platform producing embeddings not aligned across platforms, or (ii) not designed to capture the rich contextual information available in a disassembled binary.

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent studies execute part of a DNN in a trusted execution environment (e.g., TrustZone) to isolate DNN execution from the other processes; however, as the trusted execution environments for mobile/embedded devices provide limited memory protection, DNN data remain unencrypted in DRAM and become vulnerable to physical attacks. The devices can prevent the physical attacks by keeping DNN data encrypted in DRAM; when DNN data get referenced during DNN execution, they get loaded to the SRAM and get decrypted by a CPU core. Unfortunately, using the SRAM with demand paging greatly increases DNN execution time due to the inefficient use of the SRAM and the high CPU consumption of data encryption/decryption.

The C and C++ programming languages are notoriously insecure yet remain indispensable. Developers therefore resort to a multi-pronged approach to find security issues before adversaries. These include manual, static, and dynamic program analysis. Dynamic bug finding tools-henceforth "sanitizers"-can find bugs that elude other types of analysis because they observe the actual execution of a program, and can therefore directly observe incorrect program behavior as it happens. A vast number of sanitizers have been prototyped by academics and refined by practitioners. We provide a systematic overview of sanitizers with an emphasis on their role in finding security issues. Specifically, we taxonomize the available tools and the security vulnerabilities they cover, describe their performance and compatibility properties, and highlight various trade-offs.