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Underwater datacenters (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that attackers can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database's latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at a maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures., Comment: Accepted to IEEE S&P 2024

Providing security guarantees for embedded devices with limited interface capabilities is an increasingly crucial task. Although these devices don't have traditional interfaces, they still generate unintentional electromagnetic signals that correlate with the instructions being executed. By collecting these traces using our methodology and leveraging a random forest algorithm to develop a machine learning model, we built an EM side channel based instruction level disassembler. The disassembler was tested on an Arduino UNO board, yielding an accuracy of 88.69% instruction recognition for traces from twelve instructions captured at a single location in the device; this is an improvement compared to the 75.6% (for twenty instructions) reported in previous similar work., Comment: Accepted to the poster section of the 7th IEEE European Symposium on Security and Privacy 2022

The Android operating system is currently the most popular mobile operating system in the world. Android is based on Linux and therefore inherits its features including its Inter-Process Communication (IPC) mechanisms. These mechanisms are used by processes to communicate with one another and are extensively used in Android. While Android-specific IPC mechanisms have been studied extensively, Unix domain sockets have not been examined comprehensively, despite playing a crucial role in the IPC of highly privileged system daemons. In this paper, we propose SAUSAGE, an efficient novel static analysis framework to study the security properties of these sockets. SAUSAGE considers access control policies implemented in the Android security model, as well as authentication checks implemented by the daemon binaries. It is a fully static analysis framework, specifically designed to analyze Unix domain socket usage in Android system daemons, at scale. We use this framework to analyze 200 Android images across eight popular smartphone vendors spanning Android versions 7-9. As a result, we uncover multiple access control misconfigurations and insecure authentication checks. Our notable findings include a permission bypass in highly privileged Qualcomm system daemons and an unprotected socket that allows an untrusted app to set the scheduling priority of other processes running on the system, despite the implementation of mandatory SELinux policies. Ultimately, the results of our analysis are worrisome; all vendors except the Android Open Source Project (AOSP) have access control issues, allowing an untrusted app to communicate to highly privileged daemons through Unix domain sockets introduced by hardware manufacturer or vendor customization., Comment: Accepted to EuroS&P 2022

Designing deep networks robust to adversarial examples remains an open problem. Likewise, recent zeroth order hard-label attacks on image classification models have shown comparable performance to their first-order, gradient-level alternatives. It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors. In this paper, we argue that query efficiency in the zeroth-order setting is connected to an adversary's traversal through the data manifold. To explain this behavior, we propose an information-theoretic argument based on a noisy manifold distance oracle, which leaks manifold information through the adversary's gradient estimate. Through numerical experiments of manifold-gradient mutual information, we show this behavior acts as a function of the effective problem dimensionality and number of training points. On real-world datasets and multiple zeroth-order attacks using dimension-reduction, we observe the same universal behavior to produce samples closer to the data manifold. This results in up to two-fold decrease in the manifold distance measure, regardless of the model robustness. Our results suggest that taking the manifold-gradient mutual information into account can thus inform better robust model design in the future, and avoid leakage of the sensitive data manifold., Comment: Preprint

A one-time program (OTP) works as follows: Alice provides Bob with the implementation of some function. Bob can have the function evaluated exclusively on a single input of his choosing. Once executed, the program will fail to evaluate on any other input. State-of-the-art one-time programs have remained theoretical, requiring custom hardware that is cost-ineffective/unavailable, or confined to adhoc/unrealistic assumptions. To bridge this gap, we explore how the Trusted Execution Environment (TEE) of modern CPUs can realize the OTP functionality. Specifically, we build two flavours of such a system: in the first, the TEE directly enforces the one-timeness of the program; in the second, the program is represented with a garbled circuit and the TEE ensures Bob's input can only be wired into the circuit once, equivalent to a smaller cryptographic primitive called one-time memory. These have different performance profiles: the first is best when Alice's input is small and Bob's is large, and the second for the converse.

A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function $f$ of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that computes $f$ and returns the result to both parties. There have been tremendous strides in efficiency over the past ten years, yet 2P-SFE protocols remain impractical for most real-time, online computations, particularly on modestly provisioned devices. Intel's Software Guard Extensions (SGX) provides hardware-protected execution environments, called enclaves, that may be viewed as trusted computation oracles. While SGX provides native CPU speed for secure computation, previous side-channel and micro-architecture attacks have demonstrated how security guarantees of enclaves can be compromised. In this paper, we explore a balanced approach to 2P-SFE on SGX-enabled processors by constructing a protocol for evaluating $f$ relative to a partitioning of $f$. This approach alleviates the burden of trust on the enclave by allowing the protocol designer to choose which components should be evaluated within the enclave, and which via standard cryptographic techniques. We describe SGX-enabled SFE protocols (modeling the enclave as an oracle), and formalize the strongest-possible notion of 2P-SFE for our setting. We prove our protocol meets this notion when properly realized. We implement the protocol and apply it to two practical problems: privacy-preserving queries to a database, and a version of Dijkstra's algorithm for privacy-preserving navigation. Our evaluation shows that our SGX-enabled SFE scheme enjoys a 38x increase in performance over garbled-circuit-based SFE. Finally, we justify modeling of the enclave as an oracle by implementing protections against known side-channels., Comment: Full version, with proofs, of conference paper at AsiaCCS 2019; updated to include copyright information

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks.

Establishing unique identities for both humans and end systems has been an active research problem in the security community, giving rise to innovative machine learning-based authentication techniques. Although such techniques offer an automated method to establish identity, they have not been vetted against sophisticated attacks that target their core machine learning technique. This paper demonstrates that mimicking the unique signatures generated by host fingerprinting and biometric authentication systems is possible. We expose the ineffectiveness of underlying machine learning classification models by constructing a blind attack based around the query synthesis framework and utilizing Explainable-AI (XAI) techniques. We launch an attack in under 130 queries on a state-of-the-art face authentication system, and under 100 queries on a host authentication system. We examine how these attacks can be defended against and explore their limitations. XAI provides an effective means for adversaries to infer decision boundaries and provides a new way forward in constructing attacks against systems using machine learning models for authentication.

The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices., Comment: 18 pages, CCS 2017