On the susceptibility of Texas Instruments SimpleLink platform microcontrollers to non-invasive physical attacks

We investigate the susceptibility of the Texas Instruments SimpleLink platform microcontrollers to non-invasive physical attacks. We extracted the ROM bootloader of these microcontrollers and then analysed it using static analysis augmented with information obtained through emulation. We demonstrate a voltage fault injection attack targeting the ROM bootloader that allows to enable debug access on a previously locked microcontroller within seconds. Information provided by Texas Instruments reveals that one of our voltage fault injection attacks abuses functionality that is left over from the integrated circuit manufacturing process. The demonstrated physical attack allows an adversary to extract the firmware (i.e. intellectual property) and to bypass secure boot. Additionally, we mount side-channel attacks and differential fault analysis attacks on the hardware AES co-processor. To demonstrate the practical applicability of these attacks we extract the firmware from a Tesla Model 3 key fob. This paper describes a case study covering Texas Instruments SimpleLink microcontrollers. Similar attack techniques can be, and have been, applied to microcontrollers from other manufacturers. The goal of our work is to document our analysis methodology and to ensure that system designers are aware of these vulnerabilities. They will then be able to take these into account during the product design phase. All identified vulnerabilities were responsibly disclosed

Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime

Evil from Within: Machine Learning Backdoors through Hardware Trojans

Backdoors pose a serious threat to machine learning, as they can compromise the integrity of security-critical systems, such as self-driving cars. While different defenses have been proposed to address this threat, they all rely on the assumption that the hardware on which the learning models are executed during inference is trusted. In this paper, we challenge this assumption and introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning. Outside of the accelerator, neither the learning model nor the software is manipulated, so that current defenses fail. To make this attack practical, we overcome two challenges: First, as memory on a hardware accelerator is severely limited, we introduce the concept of a minimal backdoor that deviates as little as possible from the original model and is activated by replacing a few model parameters only. Second, we develop a configurable hardware trojan that can be provisioned with the backdoor and performs a replacement only when the specific target model is processed. We demonstrate the practical feasibility of our attack by implanting our hardware trojan into the Xilinx Vitis AI DPU, a commercial machine-learning accelerator. We configure the trojan with a minimal backdoor for a traffic-sign recognition system. The backdoor replaces only 30 (0.069%) model parameters, yet it reliably manipulates the recognition once the input contains a backdoor trigger. Our attack expands the hardware circuit of the accelerator by 0.24% and induces no run-time overhead, rendering a detection hardly possible. Given the complex and highly distributed manufacturing process of current hardware, our work points to a new threat in machine learning that is inaccessible to current security mechanisms and calls for hardware to be manufactured only in fully trusted environments

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Understanding Concurrency Vulnerabilities in Linux Kernel

While there is a large body of work on analyzing concurrency related software bugs and developing techniques for detecting and patching them, little attention has been given to concurrency related security vulnerabilities. The two are different in that not all bugs are vulnerabilities: for a bug to be exploitable, there needs be a way for attackers to trigger its execution and cause damage, e.g., by revealing sensitive data or running malicious code. To fill the gap, we conduct the first empirical study of concurrency vulnerabilities reported in the Linux operating system in the past ten years. We focus on analyzing the confirmed vulnerabilities archived in the Common Vulnerabilities and Exposures (CVE) database, which are then categorized into different groups based on bug types, exploit patterns, and patch strategies adopted by developers. We use code snippets to illustrate individual vulnerability types and patch strategies. We also use statistics to illustrate the entire landscape, including the percentage of each vulnerability type. We hope to shed some light on the problem, e.g., concurrency vulnerabilities continue to pose a serious threat to system security, and it is difficult even for kernel developers to analyze and patch them. Therefore, more efforts are needed to develop tools and techniques for analyzing and patching these vulnerabilities.Comment: It was finished in Oct 201

Análise de malware com suporte de hardware

Orientadores: Paulo Lício de Geus, André Ricardo Abed GrégioDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O mundo atual é impulsionado pelo uso de sistemas computacionais, estando estes pre- sentes em todos aspectos da vida cotidiana. Portanto, o correto funcionamento destes é essencial para se assegurar a manutenção das possibilidades trazidas pelos desenvolvi- mentos tecnológicos. Contudo, garantir o correto funcionamento destes não é uma tarefa fácil, dado que indivíduos mal-intencionados tentam constantemente subvertê-los visando benefíciar a si próprios ou a terceiros. Os tipos mais comuns de subversão são os ataques por códigos maliciosos (malware), capazes de dar a um atacante controle total sobre uma máquina. O combate à ameaça trazida por malware baseia-se na análise dos artefatos coletados de forma a permitir resposta aos incidentes ocorridos e o desenvolvimento de contramedidas futuras. No entanto, atacantes têm se especializado em burlar sistemas de análise e assim manter suas operações ativas. Para este propósito, faz-se uso de uma série de técnicas denominadas de "anti-análise", capazes de impedir a inspeção direta dos códigos maliciosos. Dentre essas técnicas, destaca-se a evasão do processo de análise, na qual são empregadas exemplares capazes de detectar a presença de um sistema de análise para então esconder seu comportamento malicioso. Exemplares evasivos têm sido cada vez mais utilizados em ataques e seu impacto sobre a segurança de sistemas é considerá- vel, dado que análises antes feitas de forma automática passaram a exigir a supervisão de analistas humanos em busca de sinais de evasão, aumentando assim o custo de se manter um sistema protegido. As formas mais comuns de detecção de um ambiente de análise se dão através da detecção de: (i) código injetado, usado pelo analista para inspecionar a aplicação; (ii) máquinas virtuais, usadas em ambientes de análise por questões de escala; (iii) efeitos colaterais de execução, geralmente causados por emuladores, também usados por analistas. Para lidar com malware evasivo, analistas tem se valido de técnicas ditas transparentes, isto é, que não requerem injeção de código nem causam efeitos colaterais de execução. Um modo de se obter transparência em um processo de análise é contar com suporte do hardware. Desta forma, este trabalho versa sobre a aplicação do suporte de hardware para fins de análise de ameaças evasivas. No decorrer deste texto, apresenta-se uma avaliação das tecnologias existentes de suporte de hardware, dentre as quais máqui- nas virtuais de hardware, suporte de BIOS e monitores de performance. A avaliação crítica de tais tecnologias oferece uma base de comparação entre diferentes casos de uso. Além disso, são enumeradas lacunas de desenvolvimento existentes atualmente. Mais que isso, uma destas lacunas é preenchida neste trabalho pela proposição da expansão do uso dos monitores de performance para fins de monitoração de malware. Mais especificamente, é proposto o uso do monitor BTS para fins de construção de um tracer e um debugger. O framework proposto e desenvolvido neste trabalho é capaz, ainda, de lidar com ataques do tipo ROP, um dos mais utilizados atualmente para exploração de vulnerabilidades. A avaliação da solução demonstra que não há a introdução de efeitos colaterais, o que per- mite análises de forma transparente. Beneficiando-se desta característica, demonstramos a análise de aplicações protegidas e a identificação de técnicas de evasãoAbstract: Today¿s world is driven by the usage of computer systems, which are present in all aspects of everyday life. Therefore, the correct working of these systems is essential to ensure the maintenance of the possibilities brought about by technological developments. However, ensuring the correct working of such systems is not an easy task, as many people attempt to subvert systems working for their own benefit. The most common kind of subversion against computer systems are malware attacks, which can make an attacker to gain com- plete machine control. The fight against this kind of threat is based on analysis procedures of the collected malicious artifacts, allowing the incident response and the development of future countermeasures. However, attackers have specialized in circumventing analysis systems and thus keeping their operations active. For this purpose, they employ a series of techniques called anti-analysis, able to prevent the inspection of their malicious codes. Among these techniques, I highlight the analysis procedure evasion, that is, the usage of samples able to detect the presence of an analysis solution and then hide their malicious behavior. Evasive examples have become popular, and their impact on systems security is considerable, since automatic analysis now requires human supervision in order to find evasion signs, which significantly raises the cost of maintaining a protected system. The most common ways for detecting an analysis environment are: i) Injected code detec- tion, since injection is used by analysts to inspect applications on their way; ii) Virtual machine detection, since they are used in analysis environments due to scalability issues; iii) Execution side effects detection, usually caused by emulators, also used by analysts. To handle evasive malware, analysts have relied on the so-called transparent techniques, that is, those which do not require code injection nor cause execution side effects. A way to achieve transparency in an analysis process is to rely on hardware support. In this way, this work covers the application of the hardware support for the evasive threats analysis purpose. In the course of this text, I present an assessment of existing hardware support technologies, including hardware virtual machines, BIOS support, performance monitors and PCI cards. My critical evaluation of such technologies provides basis for comparing different usage cases. In addition, I pinpoint development gaps that currently exists. More than that, I fill one of these gaps by proposing to expand the usage of performance monitors for malware monitoring purposes. More specifically, I propose the usage of the BTS monitor for the purpose of developing a tracer and a debugger. The proposed framework is also able of dealing with ROP attacks, one of the most common used technique for remote vulnerability exploitation. The framework evaluation shows no side-effect is introduced, thus allowing transparent analysis. Making use of this capability, I demonstrate how protected applications can be inspected and how evasion techniques can be identifiedMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

On Generating Gadget Chains for Return-Oriented Programming

With the increased popularity of embedded devices, low-level programming languages like C and C++ are currently experiencing a strong renewed interest. However, these languages are, meaning that programming errors may lead to undefined behaviour, which, in turn, may be exploited to compromise a system's integrity. Many programs written in these languages contain such programming errors, most infamous of which are buffer overflows. In order to fight this, there exists a large range of mitigation techniques designed to hinder exploitation, some of which are integral parts of most major operating systems' security concept. Even the most sophisticated mitigations, however, can often be bypassed by modern exploits, which are based on the principle of code reuse: they assemble, or chain, together existing code fragments (known as gadgets) in a way to achieve malicious behaviour. This technique is currently the cornerstone of modern exploits. In this dissertation, we present ROPocop, an approach to mitigate code-reuse attacks. ROPocop is a configurable, heuristic-based detector that monitors program execution and raises an alarm if it detects suspicious behaviour. It monitors the frequency of indirect branches and the length of basic blocks, two characteristics in which code-reuse attacks differ greatly from normal program behaviour. However, like all mitigations, ROPocop has its weaknesses and we show that it and other similar approaches can be bypassed in an automatic way by an aware attacker. To this end, we present PSHAPE, a practical, cross-platform framework to support the construction of code-reuse exploits. It offers two distinguishing features, namely it creates concise semantic summaries for gadgets, which allow exploit developers to assess the utility of a gadget much quicker than by going through the individual assembly instructions. And secondly, PSHAPE automatically composes gadgets to construct a chain of gadgets that can invoke any arbitrary function with user-supplied parameters. Invoking a function is indeed the most common goal of concurrent exploits, as calling a function such as mprotect greatly simplifies later steps of exploitation. For a mitigation to be viable, it must detect actual attacks reliably while at the same time avoiding false positives and ensuring that protected applications remain usable, i.e., do not crash or become very slow. In the tested sample set of applications, ROPocop detects and stops all twelve real attacks with no false positives. When executed with ROPocop, real-world programs exhibit only some slight input lag at startup but otherwise remain responsive. Yet, we further show how PSHAPE can be used to fully automatically create exploits that bypass various mitigations, for example, ROPocop itself. We also show gadgets PSHAPE found easily, that have great relevance in real exploits, and which previously required intense manual searches to find. Lastly, using PSHAPE, we also discovered a new and very useful gadget type that greatly simplifies gadget chaining

PEO-Store: Practical and Economical Oblivious Store with Peer-to-Peer Delegation

The growing popularity of cloud storage has brought attention to critical need for preventing information leakage from cloud access patterns. To this end, recent efforts have extended Oblivious RAM (ORAM) to the cloud environment in the form of Oblivious Store. However, its impracticality due to the use of probability encryption with fake accesses to obfuscate the access pattern, as well as the security requirements of conventional obliviousness designs, which hinder cloud interests in improving storage utilization by removing redundant data among cross-users, limit its effectiveness. Thus, we propose a practical Oblivious Store, PEO-Store, which integrates the obliviousness property into the cloud while removing redundancy without compromising security. Unlike conventional schemes, PEO-Store randomly selects a delegate for each client to communicate with the cloud, breaking the mapping link between a valid access pattern sequence and a specific client. Each client encrypts their data and shares it with selected delegates, who act as intermediaries with the cloud provider. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof to protect access pattern privacy and accurately identify and remove redundancy in the cloud. The theoretical proof demonstrates that the probability of identifying the valid access pattern with a specific user is negligible in our design. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space