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The increased parallelism in modern processors has sparked interest in offloading security policy enforcement to processes or hardware operating in parallel with the main application. This approach can reduce application latency, enhance security, and improve compatibility. However, existing software solutions often incur high overheads and are susceptible to memory corruption attacks, while hardware solutions tend to be inflexible and require substantial modifications to the processor. In this paper, we present SIDECAR, a novel approach that offloads security checks to run concurrently with applications by leveraging the debugging infrastructure available in commodity processors. Specifically, we utilize softwaredriven logging (SDL) extensions in Intel and Arm processors to create secure, append-only channels between applications and security monitors. We build and evaluate a prototype of SIDECAR for the x86-64 and Aarch64 architectures. To demonstrate its utility, we adapt well-known security defenses within SIDECAR, providing control-flow integrity (CFI), shadow call stacks (SCS), and memory error checking (ASAN). Our evaluation shows that these extensions perform better on the Intel architecture. In terms of defenses, SIDECAR reduces the latency of CFI in the tested real-world applications by an average of 30%, offers enhanced security with similar overhead for SCS, and is versatile enough to support complex defenses like ASAN. Furthermore, our security monitor for CFI+SCS is 30 times more efficient compared to previous work. 

Rust is a young systems programming language, but it has gained tremendous popularity thanks to its assurance of memory safety. However, the performance of Rust has been less systematically understood, although many people are claiming that Rust is comparable to C/C++ regarding efficiency. In this paper, we aim to understand the performance of Rust, using C as the baseline. First, we collect a set of micro benchmarks where each program is implemented with both Rust and C. To ensure fairness, we manually validate that the Rust version and the C version implement the identical functionality using the same algorithm. Our measurement based on the micro benchmarks shows that Rust is in general slower than C, but the extent of the slowdown varies across different programs. On average, Rust brings a 1.77x “performance overhead” compared to C. Second, we dissect the root causes of the overhead and unveil that it is primarily incurred by run-time checks inserted by the compiler and restrictions enforced by the language design. With the run-time checks disabled and the restrictions loosened, Rust presents a performance indistinguishable from C. 

Embedded devices are ubiquitous. However, preliminary evidence shows that attack mitigations protecting our desktops/servers/phones are missing in embedded devices, posing a significant threat to embedded security. To this end, this paper presents an in-depth study on the adoption of common attack mitigations on embedded devices. Precisely, it measures the presence of standard mitigations against memory corruptions in over 10k Linux-based firmware of deployed embedded devices. The study reveals that embedded devices largely omit both user-space and kernel-level attack mitigations. The adoption rates on embedded devices are multiple times lower than their desktop counterparts. An equally important observation is that the situation is not improving over time. Without changing the current practices, the attack mitigations will remain missing, which may become a bigger threat in the upcoming IoT era. Throughout follow-up analyses, we further inferred a set of factors possibly contributing to the absence of attack mitigations. The exemplary ones include massive reuse of non-protected software, lateness in upgrading outdated kernels, and restrictions imposed by automated building tools. We envision these will turn into insights towards improving the adoption of attack mitigations on embedded devices in the future.