Leakage of confidential information represents a serious security risk. Despite a number of novel, theoretical advances, it has been unclear if and how quantitative approaches to measuring leakage of confidential information could be applied to substantial, real-world programs. This is mostly due to the high complexity of computing precise leakage quantities. In this paper, we introduce a technique which makes it possible to decide if a program conforms to a quantitative policy which scales to large state-spaces with the help of bounded model checking.
Our technique is applied to a number of officially reported information leak vulnerabilities in the Linux Kernel. Additionally, we also analysed authentication routines in the Secure Remote Password suite and of a Internet Message Support Protocol implementation. Our technique shows when there is unacceptable leakage; the same technique is also used to verify, for the first time, that the applied software patches indeed plug the information leaks.
This is the first demonstration of quantitative information flow addressing security concerns of real-world industrial programs.
Cache timing attacks allow attackers to infer the properties of a secret execution by observing cache hits and misses. But how much information can actually leak through such attacks? For a given program, a cache model, and an input, our CHALICE framework leverages symbolic execution to compute the amount of information that can possibly leak through cache attacks. At the core of CHALICE is a novel approach to quantify information leak that can highlight critical cache side-channel leaks on arbitrary binary code. In our evaluation on real-world programs from OpenSSL and Linux GDK libraries, CH…
As a new format of mobile application, mini programs, which function within a larger app and are built with HTML, CSS, and JavaScript web technology, have become the way to do almost everything in China. This paper presents our research on the permissions of mini programs. We conducted a systematic study on 9 popular mobile app ecosystems, which host over 7 million mini programs, and tested over 2,580 APIs to understand these emerging systems better. We extracted a common abstracted model for mini programs permission control and revealed six categories of potential security vulnerabilities in …
Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on cryptosystems and media libraries are demonstrated under different scenarios, inferring secret keys and reconstructing private media data such as images. This work first presents eight criteria for designing a full-fledged detector for cache side-channel vulnerabilities. Then, we propose CacheQL, a novel detector that meets all of these criteria. CacheQL precisely quantifies information leaks of binary code, by characterizing the distinguishability of logged side channel tr…
A program is non-interferent if it leaks no secret information to an observable output. However, non-interference is too strict in many practical cases and quantitative information flow (QIF) has been proposed and studied in depth. Originally, QIF is defined as the average of leakage amount of secret information over all executions of a program. However, a vulnerable program that has executions leaking the whole secret but has the small average leakage could be considered as secure. This counter-intuition raises a need for a new definition of information leakage of a particular run, i.e., dyna…
Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for privacy of subjects. Privug is a tool-supported method to explore information leakage properties of data analytics and anonymization programs. In Privug, we reinterpret a program probabilistically, using off-the-shelf tools for Bayesian inference to perform information-theoretic analysis of the information flow. For privacy researchers, Privug provides a fast, lightweight way to experiment with privacy protection measures…
Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data …
Graphics Processing Units (GPUs) are deployed on most present server, desktop, and even mobile platforms. Nowadays, a growing number of applications leverage the high parallelism offered by this architecture to speed-up general purpose computation. This phenomenon is called GPGPU computing (General Purpose GPU computing). The aim of this work is to discover and highlight security issues related to CUDA, the most widespread platform for GPGPU computing. In particular, we provide details and proofs-of-concept about a novel set of vulnerabilities CUDA architectures are subject to, that could be e…
This paper evaluates new security threats due to the processor frontend in modern Intel processors. The root causes of the security threats are the multiple paths in the processor frontend that the micro-operations can take: through the Micro-Instruction Translation Engine (MITE), through the Decode Stream Buffer (DSB), also called the Micro-operation Cache, or through the Loop Stream Detector (LSD). Each path has its own unique timing and power signatures, which lead to the side- and covert-channel attacks presented in this work. Especially, the switching between the different paths leads to …
We develop a new notion of security against timing attacks where the attacker is able to simultaneously observe the execution time of a program and the probability of the values of low variables. We then show how to measure the security of a program with respect to this notion via a computable estimate of the timing leakage and use this estimate for cost optimisation.
This work presents a new tool to verify the correctness of cryptographic implementations with respect to cache attacks. Our methodology discovers vulnerabilities that are hard to find with other techniques, observed as exploitable leakage. The methodology works by identifying secret dependent memory and introducing forced evictions inside potentially vulnerable code to obtain cache traces that are analyzed using Mutual Information. If dependence is observed, the cryptographic implementation is classified as to leak information. We demonstrate the viability of our technique in the design of the…
We study quantum protocols among two distrustful parties. By adopting a rather strict definition of correctness - guaranteeing that honest players obtain their correct outcomes only - we can show that every strictly correct quantum protocol implementing a non-trivial classical primitive necessarily leaks information to a dishonest player. This extends known impossibility results to all non-trivial primitives. We provide a framework for quantifying this leakage and argue that leakage is a good measure for the privacy provided to the players by a given protocol. Our framework also covers the cas…
Information flow analysis is a powerful technique for reasoning about the sensitive information exposed by a program during its execution. While past work has proposed information theoretic metrics (e.g., Shannon entropy, min-entropy, guessing entropy, etc.) to quantify such information leakage, we argue that some of these measures not only result in counter-intuitive measures of leakage, but also are inherently prone to conflicts when comparing two programs P1 and P2 -- say Shannon entropy predicts higher leakage for program P1, while guessing entropy predicts higher leakage for program P2. T…
Algorithmic complexity vulnerabilities are a class of security problems that enables attackers to trigger the worst-case complexity of certain algorithms. Such vulnerabilities can be leveraged to deploy low-volume, asymmetric, CPU-based denial-of-service (DoS) attacks. Previous work speculates that these vulnerabilities are more dangerous in certain web servers, like Node.js, than in traditional ones, like Apache. We believe it is of utmost importance to understand if this is indeed the case or if there are ways to compensate against such problems using various deployment strategies. To this e…
We examine the issue of password length leakage via encrypted traffic i.e., bicycle attacks. We aim to quantify both the prevalence of password length leakage bugs as well as the potential harm to users. In an observational study, we find that {\em most} of the Alexa top 100 rates sites are vulnerable to bicycle attacks meaning that an eavesdropping attacker can infer the exact length of a password based on the length the encrypted packet containing the password. We discuss several ways in which an eavesdropping attacker could link this password length with a particular user account e.g., a ta…
Graph embeddings have been proposed to map graph data to low dimensional space for downstream processing (e.g., node classification or link prediction). With the increasing collection of personal data, graph embeddings can be trained on private and sensitive data. For the first time, we quantify the privacy leakage in graph embeddings through three inference attacks targeting Graph Neural Networks. We propose a membership inference attack to infer whether a graph node corresponding to individual user's data was member of the model's training or not. We consider a blackbox setting where the adv…
Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images, widens the attack surface in the container host, and creates substantial security risks for computing infrastructures at large. This paper presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with four vulnerability scanners, focusing on the use case of neuroscien…
The number of active users of Wi-Fi Direct Device-to-Device file sharing applications on Android has exceeded 1.8 billion. Wi-Fi Direct, also known as Wi-Fi P2P, is commonly used for peer-to-peer, high-speed file transfer between mobile devices, as well as a close proximity connection mode for wireless cameras, network printers, TVs and other IoT and mobile devices. For its end users, such type of direct file transfer does not incur cellular data charges. However, despite the popularity of such applications, we observe that the software vendors tend to prioritize the ease of user flow over the…
The advancements in the digitization world has revolutionized the automotive industry. Today's modern cars are equipped with internet, computers that can provide autonomous driving functionalities as well as infotainment systems that can run mobile operating systems, like Android Auto and Apple CarPlay. Android Automotive is Google's android operating system tailored to run natively on vehicle's infotainment systems, it allows third party apps to be installed and run on vehicle's infotainment systems. Such apps may raise security concerns related to user's safety, security and privacy. This pa…
Differential Privacy (DP) has received increased attention as a rigorous privacy framework. Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuously generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. …
University of Melbourne（墨尔本大学）；Macquarie University（麦考瑞大学）
Machine learning models have been shown to be vulnerable to membership inference attacks, i.e., inferring whether individuals' data have been used for training models. The lack of understanding about factors contributing success of these attacks motivates the need for modelling membership information leakage using information theory and for investigating properties of machine learning models and training algorithms that can reduce membership information leakage. We use conditional mutual information leakage to measure the amount of information leakage from the trained machine learning model ab…
Mobile databases are the statutory backbones of many applications on smartphones, and they store a lot of sensitive information. However, vulnerabilities in the operating system or the app logic can lead to sensitive data leakage by giving the adversaries unauthorized access to the app's database. In this paper, we study such vulnerabilities to define a threat model, and we propose an OS-version independent protection mechanism that app developers can utilize to detect such attacks. To do so, we model the user behavior with the database query workload created by the original apps. Here, we mod…
Indraprastha Institute of Information Technology, Delhi, India（印度理工学院德里分校（Indraprastha Institute of Information Technology, Delhi, India））；Institute for Systems Studies and Analyses, Delhi, India（系统研究与分析研究所（Institute for Systems Studies and Analyses, Delhi, India））；Institute for Systems Studies（系统研究研究所（Institute for Systems Studies））
Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversaries, making them vulnerable to extraction attacks to reverse engineer the proprietary model thereby violating the model privacy and Intellectual Property. Here, the adversary first extracts the model architecture or hyperparameters through side channel leakage, followed by stealing the functionality of the target model by training the reconstructed architecture on a synthetic da…
This paper presents the source code analysis of a file reader server socket program (connection-oriented sockets) developed in Java, to illustrate the identification, impact analysis and solutions to remove five important software security vulnerabilities, which if left unattended could severely impact the server running the software and also the network hosting the server. The five vulnerabilities we study in this paper are: (1) Resource Injection, (2) Path Manipulation, (3) System Information Leak, (4) Denial of Service and (5) Unreleased Resource vulnerabilities. We analyze the reason why e…
Currently, when a security analyst discovers a vulnerability in critical software system, they must navigate a fraught dilemma: immediately disclosing the vulnerability to the public could harm the system's users; whereas disclosing the vulnerability only to the software's vendor lets the vendor disregard or deprioritize the security risk, to the detriment of unwittingly-affected users. A compelling recent line of work aims to resolve this by using Zero Knowledge (ZK) protocols that let analysts prove that they know a vulnerability in a program, without revealing the details of the vulnerabili…
CRESS: Quantifying Vulnerabilities of Attack Scenarios in Hardware Reverse Engineering
TUM School of Computation, Information and Technology（慕尼黑工业大学计算、信息与技术学院）；Fraunhofer Institute for Applied and Integrated Security (AISEC)（弗劳恩霍夫应用与安全研究所）
Recent discovery of security attacks in advanced processors, known as Spectre and Meltdown, has resulted in high public alertness about security of hardware. The root cause of these attacks is information leakage across "covert channels" that reveal secret data without any explicit information flow between the secret and the attacker. Many sources believe that such covert channels are intrinsic to highly advanced processor architectures based on speculation and out-of-order execution, suggesting that such security risks can be avoided by staying away from high-end processors. This paper, howev…
Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data. Prior work has demonstrated that in practice a client's private information, unrelated to the main learning task, can be discovered from the model's gradients, which compromises the promised privacy protection. However, there is still no formal approach for quantifying the leakage of private information via the shared updated model or gradients. In this work, we analyze property inference attacks and define two metrics based on (i) an adaptation of t…
Mobile and IoT applications have greatly enriched our daily life by providing convenient and intelligent services. However, these smart applications have been a prime target of adversaries for stealing sensitive data. It poses a crucial threat to users' identity security, financial security, or even life security. Research communities and industries have proposed many Information Flow Control (IFC) techniques for data leakage detection and prevention, including secure modeling, type system, static analysis, dynamic analysis, \textit{etc}. According to the application's development life cycle, …
Objective. Insecure Direct Object Reference (IDOR) or Broken Object Level Authorization (BOLA) are one of the critical type of access control vulnerabilities for modern applications. As a result, an attacker can bypass authorization checks leading to information leakage, account takeover. Our main research goal was to help an application security architect to optimize security design and testing process by giving an algorithm and tool that allows to automatically analyze system API specifications and generate list of possible vulnerabilities and attack vector ready to be used as security non-f…
National University of Singapore（新加坡国立大学）；Cardiff University（卡迪夫大学）
Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of the model's training set. The existing works empirically show the possibility of these membership inference (tracing) attacks against complex deep learning models. However, the attack results are dependent on the specific training data, can be obtained only after the tedious process of training the model and performing the attack, and are missing any measure of the confidence and unused potential power of the attack. …
Internet of Things (IoT) consists of a large number of devices connected through a network, which exchange a high volume of data, thereby posing new security, privacy, and trust issues. One way to address these issues is ensuring data confidentiality using lightweight encryption algorithms for IoT protocols. However, the design and implementation of such protocols is an error-prone task; flaws in the implementation can lead to devastating security vulnerabilities. Here we propose a new verification approach named Encryption-BMC and Fuzzing (EBF), which combines Bounded Model Checking (BMC) and…
Intel SGX (Software Guard Extension) is a promising TEE (trusted execution environment) technique that can protect programs running in user space from being maliciously accessed by the host operating system. Although it provides hardware access control and memory encryption, the actual effectiveness also depends on the quality of the software. In particular, improper implementation of a code snippet running inside the enclave may still leak private data due to the invalid use of pointers. This paper serves as a first attempt to study the privacy leakage issues of enclave code and proposes a no…
