Since Spectre's initial disclosure in 2018, the difficulty of mitigating speculative execution attacks completely in hardware has led to the proliferation of several new variants and attack surfaces in the past six years. Most of the progeny build on top of the original Spectre attack's key insight, namely that CPUs can execute the wrong control flow transiently and disclose secrets through side-channel traces when attempting to alleviate control hazards, such as conditional or indirect branches and return statements. In this paper we go beyond (speculatively) affecting control flow, and present a new data speculation primitive that stems from microarchitectural optimizations designed to alleviate data hazards. More specifically, we show that Apple CPUs are equipped with a Load Address Predictor (LAP). The LAP monitors past addresses from the same load instruction to speculatively load a predicted address, which may incorrectly point to secrets at rest (i.e., never architecturally read by the CPU). Once the secret is retrieved, the LAP allows for a large speculation window that suffices for an adversary to compute on the secret, such as leaking it over a covert channel. We demonstrate the LAP's presence on recent Apple CPUs, such as the M2, A15, and newer models. We then evaluate the LAP's implications on security by showing its capabilities to read out-of-bounds, speculatively invoke rogue functions, break ASLR, and compromise the Safari web browser. Here, we leverage the LAP to disclose sensitive cross-site data (such as inbox content from Gmail) to a remote web-based adversary.

Over the past few years, the high-end CPU market is undergoing a transformational change. Moving away from using x86 as the sole architecture for high performance devices, we have witnessed the introduction of heavy-weight Arm CPUs computing devices. Among these, perhaps the most influential was the introduction of Apple's M-series architecture, aimed at completely replacing Intel CPUs in the Apple ecosystem. However, while significant effort has been invested analyzing x86 CPUs, the Apple ecosystem remains largely unexplored.

A binary’s behavior is greatly influenced by how the compiler builds its source code. Although most compiler configuration details are abstracted away during compilation, recovering them is useful for reverse engineering and program comprehension tasks on unknown binaries, such as code similarity detection. We observe that previous work has thoroughly explored this on x86-64 binaries. However, there has been limited investigation of ARM binaries, which are increasingly prevalent.In this paper, we extend previous work with a shallow-learning model that efficiently and accurately recovers compiler configuration properties for ARM binaries. We apply opcode and register-derived features, that have previously been effective on x86-64 binaries, to ARM binaries. Furthermore, we compare this work with Pizzolotto et al., a recent architecture-agnostic model that uses deep learning, whose dataset and code are available.We observe that the lightweight features are reproducible on ARM binaries. We achieve over 99% accuracy, on par with state-of-the-art deep learning approaches, while achieving a 583-times speedup during training and 3,826-times speedup during inference. Finally, we also discuss findings of overfitting that was previously undetected in prior work.

The discovery of the Spectre attack in 2018 has sent shockwaves through the computer industry, affecting processor vendors, OS providers, programming language developers, and more. Because web browsers execute untrusted code while potentially accessing sensitive information, they were considered prime targets for attacks and underwent significant changes to protect users from speculative execution attacks. In particular, the Google Chrome browser adopted the strict site isolation policy that prevents leakage by ensuring that content from different domains is not shared in the same address space. The perceived level of risk that Spectre poses to web browsers stands in stark contrast with the paucity of published demonstrations of the attack. Before mid-March 2021, there was no public proof-of-concept demonstrating leakage of information that is otherwise inaccessible to an attacker. Moreover, Google’s leaky.page, the only current proof-of-concept that can read such information, is severely restricted to only a subset of the address space and does not perform cross-website accesses. In this paper, we demonstrate that the absence of published attacks does not indicate that the risk is mitigated. We present Spook.js, a JavaScript-based Spectre attack that can read from the entire address space of the attacking webpage. We further investigate the implementation of strict site isolation in Chrome, and demonstrate limitations that allow Spook.js to read sensitive information from other webpages. We further show that Spectre adversely affects the security model of extensions in Chrome, demonstrating leaks of usernames and passwords from the LastPass password manager. Finally, we show that the problem also affects other Chromium-based browsers, such as Microsoft Edge and Brave.