ARM Memory Tagging Extension (MTE) is a new hardware feature introduced in ARMv8.5-A architecture, aiming to detect memory corruption vulnerabilities. The low overhead of MTE makes it an attractive solution to mitigate memory corruption attacks in modern software systems and is considered the most promising path forward for improving C/C++ software security. This paper explores the potential security risks posed by speculative execution attacks against MTE. Specifically, this paper identifies new Tiktag gadgets capable of leaking the MTE tags from arbitrary memory addresses through speculative execution. With Tiktag gadgets, attackers can bypass the probabilistic defense of MTE, increasing the attack success rate by close to 100%. We demonstrate that Tiktag gadgets can be used to bypass MTE-based mitigations in real-world systems, Google Chrome and the Linux kernel. Experimental results show that Tiktag gadgets can successfully leak an MTE tag with a success rate higher than 95% in less than 4 seconds. We further propose new defense mechanisms to mitigate the security risks posed by Tiktag gadgets.

Syzbot continuously fuzzes the full Linux kernel to discover latent bugs. Yet, around 75% of recent kernel bugs are caused by recent patches, dubbed regression bugs. Regression fuzzing prioritizes inputs that target recently or frequently patched code. However, this heuristic breaks down in the kernel environment as there are too many patches (and therefore too many targets).

Data-only attacks are emerging as a new threat to the security of modern operating systems. As a typical data-only attack, memory corruption attacks can compromise the integrity of kernel data, which effectively breaks the premises of access control systems. Unfortunately, the prevalence of memory corruption vulnerabilities allows attackers to exploit them and bypass access control mechanisms. Given the arbitrary memory access capability, attackers can overwrite access control policies or illegally access the kernel resources protected by the access control systems.

Kernel concurrency bugs are notoriously difficult to identify, while their consequences severely threaten the reliability and security of the entire system. Especially in the kernel, developers should consider not only locks but also memory barriers to prevent out-of-order execution from breaking the correctness of concurrent execution. Incorrect use of memory barriers may cause non-intuitive concurrency bugs that manifest due to out-of-order execution, which we refer to as OoO bugs. This paper aims to identify OoO bugs in the kernel. We devise a mechanism to emulate out-of-order execution while kernel code is executed, called OEMU. Inspired by how a processor reorders memory accesses, OEMU makes the subtle and non-deterministic behavior of out-of-order execution systematically controllable. Based on OEMU, we propose Ozz , a new testing tool designed to effectively identify kernel OoO bugs. The key feature of Ozz is its ability to deterministically control both out-of-order execution and concurrent execution caused by thread interleavings, enabling comprehensive testing of their combined effects. Our evaluation shows that OEMU is effective in reproducing previously-reported kernel OoO bugs, demonstrating its strong capability of controlling out-of-order execution. Furthermore, with Ozz , we identify 11 new OoO bugs in the latest version of the Linux kernel, subsequently confirmed and patched by kernel developers.

Kernel concurrency failures are notoriously difficult to identify and diagnose their fundamental reason, the root cause. Kernel concurrency bugs frequently involve challenging patterns such as multi-variable races, data races with asynchronous kernel threads, and pervasive benign races. We perform an in-depth study of real-world kernel concurrency bugs and elicit three requirements: comprehensiveness, pattern-agnostic, and conciseness.

A render-update bug arises when a web browser produces an erroneous rendering output due to incorrect rendering updates. Such render-update bugs seriously harm the usability and reliability of web browsers. However, we find that detecting render-update bugs is challenging because the render-update bug is a semantic bug - given a rendering result, it is difficult to determine if it is correct due to the complex rendering specification of DOM and CSS. Thus, unlike memory corruption bugs, the incorrect rendering output does not raise the violation or crash. In practice, render-update bug detection relies on the time-prohibitive manual analysis of domain experts to determine the bug.

Discovering kernel concurrency bugs through fuzzing is challenging. Identifying kernel concurrency bugs, as opposed to non-concurrency bugs, necessitates an analysis of possible interleavings between two or more threads. However, because the search space of thread interleaving is vast, it is impractical to investigate all conceivable thread interleavings. To explore the vast search space, most previous approaches perform random or simple heuristic searches without having coverage for thread interleaving or with an insufficient form of coverage. As a result, they either conduct wasteful searches with redundant executions or overlook concurrent bugs that their coverage cannot address.To overcome such limitations, we propose SegFuzz, a fuzzing framework for kernel concurrency bugs. When exploring the search space of thread interleavings, SegFuzz decomposes an entire thread interleaving into a set of segments, each of which represents an interleaving of the small number of instructions, and utilizes individual segments as interleaving coverage, called interleaving segment coverage. When searching for thread interleavings, SegFuzz mutates interleavings in explored interleaving segments to construct new thread interleavings that have not yet been explored. With SegFuzz, we discover new 21 concurrency bugs in Linux kernels, and demonstrate the efficiency of SegFuzz by showing that SegFuzz can identify known bugs on average 4.1 times quickly than the state-of-the-art approaches.

Transient execution vulnerabilities have critical security impacts to software systems since those break the fundamental security assumptions guaranteed by the CPU. Detecting these critical vulnerabilities in the RTL development stage is particularly important, as it offers a chance to fix the vulnerability early before reaching the chip manufacturing stage.

A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. Such rendering bugs critically harm the usability of web browsers as well as web applications. The unique aspect of rendering bugs is that they affect the presented visual appearance of web pages, but those web pages have no pre-defined correct appearance. Therefore, it is challenging to automatically detect errors in their appearance. In practice, web browser vendors rely on non-trivial and time-prohibitive manual analysis to detect and handle rendering regressions.

Universal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smart phones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FuzzUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FuzzUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. We have implemented FuzzUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 8 CVEs. Furthermore, compared to the baseline, FuzzUSB has also demonstrated different improvements, including $ 3\times$ higher code coverage, $ 50\times$ improved bug-finding efficiency for Linux USB gadget stacks, $ 2\times$ higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FuzzUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope.

Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program’s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity hardware incur prohibitive overheads due to expensive compiler instrumentation of memory accesses; hence, they are not efficient enough to be used in all development and testing settings.

Security bugs in CPUs have critical security impacts to all the computation related hardware and software components as it is the core of the computation. In spite of the fact that architecture and security communities have explored a vast number of static or dynamic analysis techniques to automatically identify such bugs, the problem remains unsolved and challenging largely due to the complex nature of CPU RTL designs.This paper proposes DIFUZZRTL, an RTL fuzzer to automatically discover unknown bugs in CPU RTLs. DIFUZZRTL develops a register-coverage guided fuzzing technique, which efficiently yet correctly identifies a state transition in the finite state machine of RTL designs. DIFUZZRTL also develops several new techniques in consideration of unique RTL design characteristics, including cycle-sensitive register coverage guiding, asynchronous interrupt events handling, a unified CPU input format with Tilelink protocols, and drop-in-replacement designs to support various CPU RTLs. We implemented DIFUZZRTL, and performed the evaluation with three real-world open source CPU RTLs: OpenRISC Mor1kx Cappuccino, RISC-V Rocket Core, and RISC-V Boom Core. During the evaluation, DIFUZZRTL identified 16 new bugs from these CPU RTLs, all of which were confirmed by the respective development communities and vendors. Six of those are assigned with CVE numbers, and to the best of our knowledge, we reported the first and the only CVE of RISC-V cores, demonstrating its strong practical impacts to the security community.

Intel SGX is a security solution promising strong and practical security guarantees for trusted computing. However, recent reports demonstrated that such security guarantees of SGX are broken due to access pattern based side-channel attacks, including page fault, cache, branch prediction, and speculative execution. In order to stop these side-channel attackers, Oblivious RAM (ORAM) has gained strong attention from the security community as it provides cryptographically proven protection against access pattern based side-channels. While several proposed systems have successfully applied ORAM to thwart side-channels, those are severely limited in performance and its scalability due to notorious performance issues of ORAM. This paper presents TrustOre, addressing these issues that arise when using ORAM with Intel SGX. TrustOre leverages an external device, FPGA, to implement a trusted storage service within a completed isolated environment secure from side-channel attacks. TrustOre tackles several challenges in achieving such a goal: extending trust from SGX to FPGA without imposing architectural changes, providing a verifiably-secure connection between SGX applications and FPGA, and seamlessly supporting various access operations from SGX applications to FPGA.We implemented TrustOre on the commodity Intel Hybrid CPU-FPGA architecture. Then we evaluated with three state-of-the-art ORAM-based SGX applications, ZeroTrace, Obliviate, and Obfuscuro, as well as an end-to-end key-value store application. According to our evaluation, TrustOre-based applications outperforms ORAM-based original applications ranging from 10x to 43x, while also showing far better scalability than ORAM-based ones. We emphasize that since TrustOre can be deployed as a simple plug-in to SGX machine's PCIe slot, it is readily used to thwart side-channel attacks in SGX, arguably one of the most cryptic and critical security holes today.

Online gaming, with a reported 152 billion US dollar market, is immensely popular today. One of the critical issues in multiplayer online games is cheating, in which a player uses an illegal methodology to create an advantage beyond honest game play. For example, wallhacks, the main focus of this work, animate enemy objects on a cheating player's screen, despite being actually hidden behind walls (or other occluding objects). Since such cheats discourage honest players and cause game companies to lose revenue, gaming companies deploy mitigation solutions alongside game applications on the player's machine. However, their solutions are fundamentally flawed since they are deployed on a machine where the attacker has absolute control.

Deep learning systems on the cloud are increasingly targeted by attacks that attempt to steal sensitive data. Intel SGX has been proven effective to protect the confidentiality and integrity of such data during computation. However, state-of-the-art SGX systems still suffer from substantial performance overhead induced by the limited physical memory of SGX. This limitation significantly undermines the usability of deep learning systems due to their memory-intensive characteristics.

Fuzz testing has been proved its effectiveness in discovering software vulnerabilities. Empowered its randomness nature along with a coverage-guiding feature, fuzzing has been identified a vast number of vulnerabilities in real-world programs. This paper begins with an observation that the design of the current state-of-the-art fuzzers is not well suited for a particular (but yet important) set of software programs. Specifically, current fuzzers have limitations in fuzzing programs serving multiple purposes, where each purpose is controlled by extra options.

Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T 3, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T 3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T 3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.

TrustZone is a hardware security technique in ARM mobile devices. Using TrustZone, software components running within the secure world can be completely isolated from the normal world, which ensures hardware-enforced security access control over the underlying computing resources. In order to support multiple trusted applications, TrustZone runs its own operating system, called the secure OS, within the secure world. Unfortunately, attackers have been exploiting privilege escalation vulnerabilities in a secure OS, as reported in most of major secure OSes from product vendors including Samsung, Huawei, and Qualcomm. More critically, as all trusted applications are running on the same secure OS instance, compromising the secure OS leads to compromising all trusted applications, rendering the secure OS as a single point of failure endangering the entire TrustZone's security. This paper presents PrOS, our mechanism to privatize secure OSes through direct virtualization of TrustZone. PrOS allows each trusted application to run with its own secure OS such that the secure OS is no longer a single point of security failure. One particular challenge for PrOS lies in how efficiently to implement software-only virtualization for TrustZone for a practical deployment in real systems despite the condition that the current ARM architectures do not support hardware-assisted virtualization for TrustZone. As opposed to the common belief that software-only virtualization is inefficient and sluggish, we have found several common design features inherent in the secure OS to leverage for optimally tailoring the TrustZone virtualization scheme. We implemented PrOS on a 64-bit ARM development board. According to our evaluation, PrOS incurs 0.02 and 1.18 percent performance overheads on average in the normal and secure worlds, respectively, demonstrating its effectiveness in the field.

Setuid system calls enable critical functions such as user authentications and modular privileged components. Such operations must only be executed after careful validation. However, current systems do not perform rigorous checks, allowing exploitation of privileges through memory corruption vulnerabilities in privileged programs. As a solution, understanding which setuid system calls can be invoked in what context of a process allows precise enforcement of least privileges. We propose a novel comprehensive method to systematically extract and enforce least privilege of setuid system calls to prevent misuse. Our approach learns the required process contexts of setuid system calls along multiple dimensions: process hierarchy, call stack, and parameter in a process-aware way. Every setuid system call is then restricted to the per-process context by our kernel-level context enforcer. Previous approaches without process-awareness are too coarse-grained to control setuid system calls, resulting in over-privilege. Our method reduces available privileges even for identical code depending on whether it is run by a parent or a child process. We present our prototype called PoLPer which systematically discovers only required setuid system calls and effectively prevents real-world exploits targeting vulnerabilities of the setuid family of system calls in popular desktop and server software at near zero overhead.

A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.

Type confusion, often combined with use-after-free, is the main attack vector to compromise modern C++ software like browsers or virtual machines. Typecasting is a core principle that enables modularity in C++. For performance, most typecasts are only checked statically, i.e., the check only tests if a cast is allowed for the given type hierarchy, ignoring the actual runtime type of the object. Using an object of an incompatible base type instead of a derived type results in type confusion. Attackers abuse such type confusion issues to attack popular software products including Adobe Flash, PHP, Google Chrome, or Firefox. We propose to make all type checks explicit, replacing static checks with full runtime type checks. To minimize the performance impact of our mechanism HexType, we develop both low-overhead data structures and compiler optimizations. To maximize detection coverage, we handle specific object allocation patterns, e.g., placement new or reinterpret_cast which are not handled by other mechanisms. Our prototype results show that, compared to prior work, HexType has at least 1.1 -- 6.1 times higher coverage on Firefox benchmarks. For SPEC CPU2006 benchmarks with overhead, we show a 2 -- 33.4 times reduction in overhead. In addition, HexType discovered 4 new type confusion bugs in Qt and Apache Xerces-C++.

Memory corruption vulnerabilities are the root cause of many modern attacks. Existing defense mechanisms are inadequate; in general, the software-based approaches are not efficient and the hardware-based approaches are not flexible. In this paper, we present hardware-assisted data-flow isolation, or, HDFI, a new fine-grained data isolation mechanism that is broadly applicable and very efficient. HDFI enforces isolation at the machine word granularity by virtually extending each memory unit with an additional tag that is defined by dataflow. This capability allows HDFI to enforce a variety of security models such as the Biba Integrity Model and the Bell -- LaPadula Model. We implemented HDFI by extending the RISC-V instruction set architecture (ISA) and instantiating it on the Xilinx Zynq ZC706 evaluation board. We ran several benchmarks including the SPEC CINT 2000 benchmark suite. Evaluation results show that the performance overhead caused by our modification to the hardware is low (<; 2%). We also developed or ported several security mechanisms to leverage HDFI, including stack protection, standard library enhancement, virtual function table protection, code pointer protection, kernel data protection, and information leak prevention. Our results show that HDFI is easy to use, imposes low performance overhead, and allows us to create more elegant and more secure solutions.

Recent advance in web tracking technologies has raised many privacy concerns. To combat users' fear of privacy invasion, online vendors have taken measures such as being more transparent with users about their data use and providing options for users to manage their online activities. Such efforts gain users' trust in online vendors and improve their willingness to share their digital footprints. However, there are still a significant amount of users who actively limit involuntarily sharing of data because vendor provided management tools only restrict the use of collected data and users worry vendors do not have enough measures in place to protect their privacy sensitive information.

A general prerequisite for a code reuse attack is that the attacker needs to locate code gadgets that perform the desired operations and then direct the control flow of a vulnerable application to those gadgets. Address Space Layout Randomization (ASLR) attempts to stop code reuse attacks by making the first part of the prerequisite unsatisfiable. However, research in recent years has shown that this protection is often defeated by commonly existing information leaks, which provides attackers clues about the whereabouts of certain code gadgets. In this paper, we present ASLR-Guard, a novel mechanism that completely prevents the leaks of code pointers, and render other information leaks (e.g., the ones of data pointers) useless in deriving code address. The main idea behind ASLR-Guard is to render leak of data pointer useless in deriving code address by separating code and data, provide a secure storage for code pointers, and encode the code pointers when they are treated as data. ASLR-Guard can either prevent code pointer leaks or render their leaks harmless. That is, ASLR-Guard makes it impossible to overwrite code pointers with values that point to or will hijack the control flow to a desired address when the code pointers are dereferenced. We have implemented a prototype of ASLR-Guard, including a compilation toolchain and a C/C++ runtime. Our evaluation results show that (1) ASLR-Guard supports normal operations correctly; (2) it completely stops code address leaks and can resist against recent sophisticated attacks; (3) it imposes almost no runtime overhead (< 1%) for C/C++ programs in the SPEC benchmark. Therefore, ASLR-Guard is very practical and can be applied to secure many applications.

Today, systems software is too complex to be bug-free. To find bugs in systems software, developers often rely on code checkers, like Linux's Sparse. However, the capability of existing tools used in commodity, large-scale systems is limited to finding only shallow bugs that tend to be introduced by simple programmer mistakes, and so do not require a deep understanding of code to find them. Unfortunately, the majority of bugs as well as those that are difficult to find are semantic ones, which violate high-level rules or invariants (e.g., missing a permission check). Thus, it is difficult for code checkers lacking the understanding of a programmer's true intention to reason about semantic correctness.

Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.

There have been many research efforts to secure Android applications and the high-level system mechanisms. The low-level operating system designs have been overlooked partially due to the belief that security issues at this level are similar to those on Linux, which are well-studied. However, we identify that certain Android modifications are at odds with security and result in serious vulnerabilities that need to be addressed immediately. In this paper, we analyze the Zygote process creation model, an Android operating system design for speeding up application launches. Zygote weakens Address Space Layout Randomization (ASLR) because all application processes are created with largely identical memory layouts. We design both remote and local attacks capable of bypassing the weakened ASLR and executing return-oriented programming on Android. We demonstrate the attacks using real applications, such as the Chrome Browser and VLC Media Player. Further, we design and implement Morula, a secure replacement for Zygote. Morula introduces a small amount of code to the Android operating system and can be easily adopted by device vendors. Our evaluation shows that, compared to Zygote, Morula incurs a 13 MB memory increase for each running application but allows each Android process to have an individually randomized memory layout and even a slightly shorter average launch time.

As the use of mobile devices increases, a location-based service (LBS) becomes increasingly popular because it provides more convenient context-aware services. However, LBS introduces problematic issues for location privacy due to the nature of the service. Location privacy protection methods based on k-anonymity and l-diversity have been proposed to provide anonymized use of LBS. However, the k-anonymity and l-diversity methods still can endanger the user's privacy because location semantic information could easily be breached while using LBS. This paper presents a novel location privacy protection technique, which protects the location semantics from an adversary. In our scheme, location semantics are first learned from location data. Then, the trusted-anonymization server performs the anonymization using the location semantic information by cloaking with semantically heterogeneous locations. Thus, the location semantic information is kept secure as the cloaking is done with semantically heterogeneous locations and the true location information is not delivered to the LBS applications. This paper proposes algorithms for learning location semantics and achieving semantically secure cloaking.

Reverse engineering is the process of discovering a high-level structure and its semantics from a lower-level structure. In order to prevent malicious use of reverse engineering against binaries, various techniques have been developed called binary obfuscation. Obfuscated binary is a transformed binary which retains original binary's executing behavior while its outer representation obstructs the reverse engineering. In this paper we propose three novel approaches to improve the binary obfuscation. First we propose a generalized binary obfuscation algorithm that covers any specific or whole part of a binary code by using confusing code and redirecting control-flow using exceptions. Second, we employ a data-mining method to make our obfuscated binary look like a normal binary. And third, we address the issue that the previous techniques could not be applied to Windows binaries by designing a new exception hooking mechanism in Windows. Experimental results show that our obfuscated binary can hide 60--90% of the original instructions from reverse engineering tools, while its execution slows down a little, and moreover the obfuscated binary's stealth can be guaranteed.