Database management systems (DBMSs) are vital components in modern data-driven systems. Their complexity often leads to logic bugs, which are implementation errors within the DBMSs that can lead to incorrect query results, data exposure, unauthorized access, etc., without necessarily causing visible system failures. Existing detection employs two strategies: rule-based bug detection and coverage-guided fuzzing. In general, rule specification itself is challenging; as a result, rule-based detection is limited to specific and simple rules. Coverage-guided fuzzing blindly explores code paths or blocks, many of which are unlikely to contain logic bugs; therefore, this strategy is cost-ineffective. In this paper, we design SQLaser, a SQL-clause-guided fuzzer for detecting logic bugs in DBMSs. Through a comprehensive examination of existing logic bugs across four distinct DBMSs, excluding those causing system crashes, we have identified 35 logic-bug patterns. These patterns manifest as certain SQL clause combinations that commonly result in logic bugs, and behind these clause combinations are a sequence of functions. We therefore model logic-bug patterns as error-prone function chains (i.e., sequences of functions). We further develop a directed fuzzer with a new path-to-path distance-calculation mechanism for effectively testing these chains and discovering additional logic bugs. This mechanism enables SQLaser to swiftly navigate to target sites and uncover potential bugs emerging from these paths. Our evaluation, conducted on SQLite, MySQL, PostgreSQL, and TiDB, demonstrates that SQLaser significantly accelerates bug discovery compared to other fuzzing approaches, reducing detection time by approximately 60%. As a standalone fuzzer, SQLaser identified 22 bugs spanning 18 of the 35 logic-bug patterns, outperforming contemporary fuzzers such as SQLRight, which only uncovered two logic bugs across two patterns within the same testing period (i.e., 60 days) when testing SQLite. Notably, four of the bugs discovered by SQLaser are zero-day, all of which have been reported to and confirmed by vendors.

Compilers are expected to generate optimized code, but they sometimes introduce pessimizations, quality-degrading redundant instructions. These bugs not only incur performance overhead but also, critically, expand the attack surface by introducing unexpected side effects (e.g., redundant memory accesses) without breaking compilation correctness. Existing bug-finding methods are neither designed for nor effective at identifying such security-sensitive pessimizations. This paper presents CLower, a novel, black-box approach for automatically detecting compiler pessimizations via redundant memory accesses. CLower’s core insight is that any extra global memory accesses in a fully optimized binary, compared to the source, indicate a pessimization. To reliably distinguish compiler-introduced redundancy from source-level redundancy, we generate random C programs in which each global variable has a predetermined, controlled number of memory accesses. CLower then executes the instrumented binary and verifies whether superfluous accesses have been introduced during compilation. We applied CLower to GCC and LLVM, reporting 23 unique bugs (21 in GCC, 2 in Clang), with 16 confirmed as new pessimization bugs. Our evaluation shows that CLower accurately detects diverse, impactful pes-simization bugs, the majority of which (75%) also manifest for heap-allocated objects, demonstrating that the underlying compiler flaws are general and not limited to global memory. Furthermore, we identify a systematic conflict between compiler optimizations and pessimization bugs, which causes many such bugs to remain hidden in compiler versions. This study sheds light on the under-explored area of compiler pessimization and provides a practical tool for improving compiler quality.

Causal inconsistency arises when the underlying causal graphs captured by generative models like Normalizing Flows are inconsistent with those specified in causal models like Struct Causal Models. This inconsistency can cause unwanted issues including unfairness. Prior works to achieve causal consistency inevitably compromise the expressiveness of their models by disallowing hidden layers. In this work, we introduce a new approach: Causally Consistent Normalizing Flow (CCNF). To the best of our knowledge, CCNF is the first causally consistent generative model that can approximate any distribution with multiple layers. CCNF relies on two novel constructs: a sequential representation of SCMs and partial causal transformations. These constructs allow CCNF to inherently maintain causal consistency without sacrificing expressiveness. CCNF can handle all forms of causal inference tasks, including interventions and counterfactuals. Through experiments, we show that CCNF outperforms current approaches in causal inference. We also empirically validate the practical utility of CCNF by applying it to real-world datasets and show how CCNF addresses challenges like unfairness effectively.

With the rapid development of large language models (LLMs), their applications have expanded into diverse fields, such as code assistance. However, the substantial size of LLMs makes their training highly resource- and time-consuming, which leads to lengthy retraining and delayed updating. Consequently, LLMs trained based on old data may generate outdated results. This becomes extremely critical in the scenario of avoiding vulnerabilities. New vulnerabilities are discovered every day. Without updating their knowledge, LLMs may inadvertently generate code that includes these newly discovered vulnerabilities. Current strategies, such as prompt engineering and fine-tuning, do not effectively address this issue. Prompt engineering fails to equip LLMs with comprehensive, up-to-date knowledge, while fine-tuning remains prohibitively resource-intensive and time-consuming. To address this issue, we study the problem of LLM recommending outdated APIs and propose a new solution, named APILOT, which maintains a real-time, quickly updatable dataset of outdated APIs. Additionally, APILOT utilizes pre-constructed cache prediction and augmented generation methods that leverage this dataset to navigate LLMs in generating secure, version-aware code. We conducted a comprehensive empirical evaluation of APILOT across seventeen state-of-the-art large language models (LLMs), including both open-source and commercial systems. The results demonstrate that APILOT reduces outdated API recommendations by an average of 75%, with some models achieving up to 100% mitigation in specific large language models. Notably, these improvements are achieved with minimal performance overhead. Interestingly, while enhancing security, APILOT also improves the usability of LLM -generated code by an average of 37%, with gains reaching up to 85.6% in certain large language models. Importantly, these improvements are achieved without compromising code functionality, as measured by ICE-SCORE evaluations across diverse prompts and LLMs. This demonstrates APILOT's dual benefit─it not only reduces the risk of outdated API usage but also enhances the practical utility and deployability of generated code. Together, these results highlight APILOT 's potential to improve both security and developer experience in real-world AI-assisted programming environments.

Under the monolithic architecture of the Linux kernel, all its components operate within the same address space. Notably, device drivers constitute over half of the kernel codebase yet are particularly prone to bugs. Therefore, exploring vulnerabilities in drivers is critical for ensuring kernel security. Extensive research has been done to fuzz kernel drivers through system calls and hardware interrupts. Through a comprehensive study of the Linux Kernel Device Model, we identified that the execution of device drivers is also influenced by runtime parameters, including device attributes and kernel module parameters. Our analysis reveals that large portions of the uncovered code are masked by these parameters, which are exposed to the userspace through a specialized virtual file system known as sysfs. Furthermore, adjacent devices interconnected within the same device tree also impact drivers' behavior.

As the de-facto standard for container orchestration, Kubernetes is extensively adopted by numerous companies and cloud vendors, making its security critical. In this paper, we define a new attack surface called implicit permission: The execution of explicitly granted permissions in Kubernetes dynamically leads to implicit operations on other resources, enabling new permissions beyond the explicitly granted ones. Such implicit permissions create security vulnerabilities that attackers can exploit to compromise an entire cluster.

OS-level virtualization (a.k.a. container) has become a fundamental technology in cloud computing due to the efficiency provided by the shared-kernel design. However, this design results in containers sharing thousands of kernel variables and data structures (termedabstract resources), which are prevalent but under-protected. Without exploiting other kernel vulnerabilities, a non-privileged container can easily exhaust abstract resources to cause DoS attacks against other containers. Even worse, our experiments demonstrate that abstract resource attacks are a broad class of attacks that affect Linux, FreeBSD, Fuchsia, and all shared-kernel container environments on the top four cloud vendors. To defend against the abstract resource attack, we automatically analyze vulnerable abstract resources in the Linux kernel and detect 501 container-exhaustible resources. To confine these abstract resources dynamically, we propose two new techniques: the flexible in-kernel attachment for flexible resource consumption attachment and the tree-based resource accounting for efficient usage retrieval. Based on these two techniques, we design and implement aflexibleabstract resource confinement framework, named Flask, to achieve flexible and efficient abstract resource confinement. Our evaluation shows Flask can efficiently limit abstract resource usage with less than 0.6% performance overhead.

Control-flow integrity (CFI) is a strong and efficient defense mechanism against memory-corruption attacks.The practical versions of CFI, which have been integrated into compilers, employ static analysis to collect all possibly valid target functions of indirect calls.They are however less effective because the static analysis is imprecise.While more precise CFI techniques have been proposed, such as dynamic CFI, they are not yet practical due to issues on performance, compatibility, and deployability.We believe that to be practical, CFI based on static analysis is still the promising direction.However, these years have not seen much progress on the effectiveness of such practical CFI.This paper aims to boost the effectiveness of practical CFI by dramatically optimizing the target-function sets (aka equivalence class or EC) of indirect calls.We first identify two fundamental limitations that lead to the imprecision of static indirect-call analysis: incomplete field sensitivity due to variable field indexes and the unawareness of the origins of point-to targets.We then propose two novel analysis techniques, complete field sensitivity and origin awareness, which handle variable field indexes and distinguish target origins.The techniques dramatically reduce the size of target functions.To enforce the origin awareness, we further employ Intel Memory Protection Keys to safely store the origin information.We implement our techniques as a system called ECCut.The evaluation results show that compared to the mainline LLVM CFI, ECCut achieves a substantial reduction of 94.8% and 90.3% in the average and the largest EC sizes.While compared to the state-of-the-art origin-aware CFI (i.e., OS-CFI), ECCut reduces the average and the largest EC sizes by 90.2% and 89.3% respectively.Additionally,

Leveraging a control flow hijacking primitive (CFHP) to gain root privileges is critical to attackers striving to exploit Linux kernel vulnerabilities. Such attack has become increasingly elusive as security researchers propose capable kernel security mitigations, leading to the development of complex (and, as a trade-off, brittle and unreliable) attack techniques to regain it. In this paper, we obviate the need for complexity by proposing RetSpill, a powerful yet elegant exploitation technique that employs user space data already present on the kernel stack for privilege escalation.

With the rapid technology evolution of the Internet of Things (IoT) and increasing user needs, IoT device re-using becomes more and more common nowadays. For instance, more than 300,000 used IoT devices are selling on Craigslist. During IoT re-using, sensitive data such as credentials and biometrics residing in these devices may face the risk of leakage if a user fails properly dispose of the data. Thus, a critical security concern is raised: do (or can) users properly dispose of the sensitive data in used IoT? To the best of our knowledge, it is still an unexplored problem that desires a systematic study.In this paper, we perform the first in-depth investigation on the user-data disposal of used IoT devices. Our investigation integrates multiple research methods to explore the status quo and the root causes of the user-data leakages with used IoT devices. First, we conduct a user study to investigate the user awareness and understanding of data disposal. Then, we conduct a large-scale analysis on 4,749 IoT firmware images to investigate user-data collection. Finally, we conduct a comprehensive empirical evaluation on 33 IoT devices to investigate the effectiveness of existing data disposal methods.Through the systematical investigation, we discover that IoT devices collect more sensitive data than users expect. Specifically, we detect 121,984 sensitive data collections in the tested firmware. Moreover, users usually do not or even cannot properly dispose of the sensitive data. Worse, due to the inherent characteristics of storage chips, 13.2% of the investigated firmware perform "shallow" deletion, which may allow adversaries to obtain sensitive data after data disposal. Given the large-scale IoT re-using, such leakage would cause a broad impact. We have reported our findings to world-leading companies. We hope our findings raise awareness of the failures of user-data disposal with IoT devices and promote the protection of users’ sensitive data in IoT devices.

Today's software programs are bloating and have become extremely complex. As there is typically no internal isolation among modules in a program, a vulnerability can be exploited to corrupt the memory and take control of the whole program. Program modularization is thus a promising security mechanism that splits a complex program into smaller modules, so that memory-access instructions can be constrained from corrupting irrelevant modules. A general approach to realizing program modularization is dependence analysis which determines if an instruction is independent of specific code or data; and if so, it can be modularized. Unfortunately, dependence analysis in complex programs is generally considered infeasible, due to problems in data-flow analysis, such as unknown indirect-call targets, pointer aliasing, and path explosion. As a result, we have not seen practical automated program modularization built on dependence analysis.This paper presents a breakthrough—Type-based dependence analysis for Program Modularization (TyPM). Its goal is to determine which modules in a program can never pass a type of object (including references) to a memory-access instruction; therefore, objects of this type that are created by these modules can never be valid targets of the instruction. The idea is to employ a type-based analysis to first determine which types of data flows can take place between two modules, and then transitively resolve all dependent modules of a memory-access instruction, with respect to the specific type. Such an approach avoids the data-flow analysis and can be practical. We develop two important security applications based on TyPM: refining indirect-call targets and protecting critical data structures. We extensively evaluate TyPM with various system software, including an OS kernel, a hypervisor, UEFI firmware, and a browser. Results show that on average TyPM additionally refines indirect-call targets produced by the state of the art by 31%-91%. TyPM can also remove 99.9% of modules for memory-write instructions to prevent them from corrupting critical data structures in the Linux kernel.

Intra-process memory isolation is a cornerstone technique of protecting the sensitive data in memory-corruption defenses, such as the shadow stack in control flow integrity (CFI) and the safe region in code pointer integrity (CPI). In this article, we proposeSEIMI, a highly efficient intra-process memory isolation technique for memory-corruption defenses. The core is to use the efficientSupervisor-mode Access Prevention (SMAP), a hardware feature that is originally used for preventing the kernel from accessing the user space, to achieve intra-process memory isolation. To leverage SMAP,SEIMIcreatively executes the user code in the privileged mode. In addition to enabling the new design of the SMAP-based memory isolation, we further develop multiple new techniques to ensure secure escalation of user code. Extensive experiments show thatSEIMIoutperforms existing isolation mechanisms, including theMemory Protection Keys(MPK) based scheme and theMemory Protection Extensions(MPX) based scheme.

Linux kernel introduces the memory control group (memcg) to account and confine memory usage at the process-level. Due to its flexibility and efficiency, memcg has been widely adopted by container platforms and has become a fundamental technique. While being critical, memory accounting is prone to missing-account bugs due to the diverse memory accounting interfaces and the massive amount of allocation/free paths. To our knowledge, there is still no systematic analysis against the memory missing-account problem, with respect to its security impacts, detection, etc.

Security bugs like memory errors are constantly introduced to software programs, and recent years have witnessed an increasing number of reported security bugs. Traditional detection approaches are mainly specification-based---detecting violations against a specified rule as security bugs. This often does not work well in practice because specifications are difficult to specify and generalize, leaving complicated and new types of bugs undetected. Recent research thus leans toward deviation-based detection which finds a substantial number of similar cases and detects deviating cases as potential bugs. This, however, suffers from two other problems. First, it requires enough similar cases to find deviations and thus cannot work for custom code that does not have similar cases. Second, code-similarity analysis is probabilistic and challenging, so the detection can be unreliable. Sometimes, similar cases can normally have deviating behaviors under different contexts.

Symbolic execution has become a foundational program analysis technique. Performing symbolic execution unavoidably encounters internal functions (e.g., library functions) that provide basic operations such as string processing. Many symbolic execution engines construct internal function models that abstract function behaviors for scalability and compatibility concerns. Due to the high complexity of constructing the models, developers intentionally summarize only partial behaviors of a function, namely modeled functionalities, in the models. The correctness of the internal function models is critical because it would impact all applications of symbolic execution, e.g., bug detection and model checking.

Existing tools for the automated detection of memory corruption bugs are not very effective in practice. They typically recognize only standard memory management (MM) APIs (e.g., malloc and free) and assume a naive paired-use model—an allocator is followed by a specific deallocator. However, we observe that programmers very often design their own MM functions and that these functions often manifest two major characteristics: (1) Custom allocator functions perform multi-object or nested allocation which then requires structure-aware deallocation functions. (2) Custom allocators and deallocators follow an unpaired-use model. A more effective detection thus needs to adapt those characteristics and capture memory bugs related to non-standard MM behaviors. In this paper, we present a MM function aware memory bug detection technique by introducing the concept of structure-aware and object-centric Memory Operation Synopsis (MOS). A MOS abstractly describes the memory objects of a given MM function, how they are managed by the function, and their structural relations. By utilizing MOS, a bug detection could explore much less code but is still capable of handling multi-object or nested allocations and does not rely on the paired-use model. In addition, to extensively find MM functions and automatically generate MOS for them, we propose a new identification approach that combines natural language processing (NLP) and data flow analysis, which enables the efficient and comprehensive identification of MM functions, even in very large code bases. We implement a MOS-enhanced memory bug detection system, Goshawk, to discover memory bugs caused by complex and custom MM behaviors. We applied Goshawk to well-tested and widely-used open source projects including OS kernels, server applications, and IoT SDKs. Goshawk outperforms the state-of-the-art data flow analysis driven bug detection tools by an order of magnitude in analysis speed and the number of accurately identified MM functions, reports the discovered bugs with a developer-friendly, MOS based description, and successfully detects 92 new double-free and use-after-free bugs.

Fuzzing has become an essential means of finding software bugs. Bug finding through fuzzing requires two parts—exploring code paths to reach bugs and exposing bugs when they are reached. Existing fuzzing research has primarily focused on improving code coverage but not on exposing bugs. Sanitizers such as AddressSanitizer (ASAN) and MemorySanitizer (MSAN) have been the dominating tools for exposing bugs. However, sanitizer-based bug exposing has the following limitations. (1) sanitizers are not compatible with each other. (2) sanitizers incur significant runtime overhead. (3) sanitizers may generate false positives, and (4) exposed bugs may not be exploitable. To address these limitations, we proposeExpozzer, a fuzzing system that can expose bugs comprehensively, efficiently, and faithfully. The intuition ofExpozzeris to detect bugs through divergences in a properly diversified dual-execution environment, which does not require maintaining or checking execution metadata. We design a practical and deterministic dual-execution engine, a co-design for dual-execution and fuzzers, bug-sensitive diversification, comprehensive, and efficient divergence detection to ensure the effectiveness ofExpozzer. The results of evaluations show thatExpozzercan detect not only CVE-assigned vulnerabilities reliably, but also new vulnerabilities in well-tested real-world programs.Expozzeris 10 times faster than MemorySanitizer and is similar to AddressSanitizer.

In the test case generation process of combinatorial testing, particle swarm optimization (PSO) is widely concerned for its simple implementation and fast convergence rate; however, it often falls into local optimum due to premature convergence. To attack this problem, a novel adaptive value measurement strategy is adopted by weighing the relationship between current test cases and historical test cases. The test case with the minimum average hamming distance is selected as the optimal test case, and the inertial weight linear differential decrease strategy is developed to ensure better inertial weight in different search stages, further to improve the capability of generating smaller covering arrays. In addition, we integrate the simulated annealing strategy into the improved PSO to improve the ability of particles jumping out of the local optimum, and an innovative approach for generating a better covering array is proposed. Experiments on 16 classical random strength covering arrays suggest that our approach outperforms six other techniques in terms of effectiveness.

To reduce the development costs, IoT vendors tend to construct IoT kernels by customizing the Linux kernel. Code pruning is common in this customization process. However, due to the intrinsic complexity of the Linux kernel and the lack of long-term effective maintenance, IoT vendors may mistakenly delete necessary security operations in the pruning process, which leads to various bugs such as memory leakage and NULL pointer dereference. Yet detecting bugs caused by code pruning in IoT kernels is difficult. Specifically, (1) a significant structural change makes precisely locating the deleted security operations (DSO ) difficult, and (2) inferring the security impact of a DSO is not trivial since it requires complex semantic understanding, including the developing logic and the context of the corresponding IoT kernel.

Missing a security operation such as a bound check has been a major cause of security-critical bugs. Automatically checking whether the code misses a security operation in large programs is challenging since it has to understand whether the security operation is indeed necessary in the context. Recent methods typically employ cross-checking to identify deviations as security bugs, which collects functionally similar program slices and infers missed security operations through majority-voting. An inherent limitation of such approaches is that they heavily rely on a substantial number of similar code pieces to enable cross-checking. In practice, many code pieces are unique, and thus we may be unable to find adequate similar code snippets to utilize cross-checking.

Due to its faster start-up speed and better resource utilization efficiency, OS-level virtualization has been widely adopted and has become a fundamental technology in cloud computing. Compared to hardware virtualization, OS-level virtualization leverages the shared-kernel design to achieve high efficiency and runs multiple user-space instances (a.k.a., containers) on the shared kernel. However, in this paper, we reveal a new attack surface that is intrinsic to OS-level virtualization, affecting Linux, FreeBSD, and Fuchsia. The root cause is that the shared-kernel design in OS-level virtualization results containers in sharing thousands of kernel variables and data structures directly and indirectly. Without exploiting any kernel vulnerabilities, a non-privileged container can easily exhaust the shared kernel variables and data structure instances to cause DoS attacks against other containers. Compared with the physical resources, these kernel variables or data structure instances (termed abstract resources) are more prevalent but under-protected. To show the importance of confining abstract resources, we conduct abstract resource attacks that target different aspects of the OS kernel. The results show that attacking abstract resources is highly practical and critical. We further conduct a systematic analysis to identify vulnerable abstract resources in the Linux kernel, which successfully detects 1,010 abstract resources and 501 of them can be repeatedly consumed dynamically. We also conduct the attacking experiments in the self-deployed shared-kernel container environments on the top 4 cloud vendors. The results show that all environments are vulnerable to abstract resource attacks. We conclude that containing abstract resources is hard and give out multiple strategies for mitigating the risks.

IoT devices are abnormally prone to diverse errors due to harsh environments and limited computational capabilities. As a result, correct error handling is critical in IoT. Implementing correct error handling is non-trivial, thus requiring extensive testing such as fuzzing. However, existing fuzzing cannot effectively test IoT error-handling code. First, errors typically represent corner cases, thus are hard to trigger. Second, testing error-handling code would frequently crash the execution, which prevents fuzzing from testing following deep error paths.In this paper, we propose IFIZZ, a new bug detection system specifically designed for testing error-handling code in Linux-based IoT firmware. IFIZZ first employs an automated binary-based approach to identify realistic runtime errors by analyzing errors and error conditions in closed-source IoT firmware. Then, IFIZZ employs state-aware and bounded error generation to reach deep error paths effectively. We implement and evaluate IFIZZ on 10 popular IoT firmware. The results show that IFIZZ can find many bugs hidden in deep error paths. Specifically, IFIZZ finds 109 critical bugs, 63 of which are even in widely used IoT libraries. IFIZZ also features high code coverage and efficiency, and covers 67.3% more error paths than normal execution. Meanwhile, the depth of error handling covered by IFIZZ is 7.3 times deeper than that covered by the state-of-the-art method. Furthermore, IFIZZ has been practically adopted and deployed in a worldwide leading IoT company. We will open-source IFIZZ to facilitate further research in this area.

Symbolic execution has been widely applied in detecting vulnerabilities in web applications. Modeling language-specific built-in functions is essential for symbolic execution. Since built-in functions tend to be complicated and are typically implemented in low-level languages, a common strategy is to manually translate them into the SMT-LIB language for constraint solving. Such translation requires an excessive amount of human effort and deep understandings of the function behaviors. Incorrect translation can invalidate the final results. This problem aggravates in PHP applications because of their cross-language nature, i.e., , the built-in functions are written in C, but the rest code is in PHP.

With the wide deployment of security mechanisms such as Address Space Layout Randomization (ASLR), memory disclosures have become a prerequisite for critical memory-corruption attacks (e.g., code-reuse attack)-adversaries are forced to exploit memory disclosures to circumvent ASLR as the first step. As a result, the security threats of memory disclosures are now significantly aggravated-they break not only data confidentiality but also the effectiveness of security mechanisms. In this paper, we propose a general detection methodology and develop a system to stop memory disclosures. We observe that memory disclosures are not root causes but rather consequences of a variety of hard-to-detect program errors such as memory corruption and uninitialized read. We thus propose a replicated execution-based methodology to generally detect memory disclosures, regardless of their causes. We realize this methodology with Buddy: By seamlessly maintaining two identical running instances of a target program and diversifying only its target data, Buddy can accurately detects memory disclosures of the data, as doing so will result in the two instances outputting different values. Extensive evaluation results show that Buddy is reliable and efficient while stopping real memory disclosures such as the Heartbleed leak.

Operating system (OS) kernels frequently encounter various errors due to invalid internal states or external inputs. To ensure the security and reliability of OS kernels, developers propose a diverse set of mechanisms to conservatively capture and handle potential errors. Existing research has thus primarily focused on the completeness and adequacy of error handling to not miss the attention. However, we find that handling an error with an over-severe level (e.g., unnecessarily terminating the execution) instead hurts the security and reliability. In this case, the error-handling consequences are even worse than the error it attempts to resolve. We call such a case Exaggerated Error Handling (EEH). The security impacts of EEH bugs vary, including denial-of-service, data losses, broken control-flow integrity, memory leaks, etc. Despite its significance, detecting EEH remains an unexplored topic.

Intel Software Guard extensions (SGX), a hardware-based Trusted Execution Environment (TEE), has become a promising solution to stopping critical threats such as insider attacks and remote exploits. SGX has recently drawn extensive research in two directions---using it to protect the confidentiality and integrity of sensitive data, and protecting itself from attacks. Both the applications and defense mechanisms of SGX have a fundamental need---flexible memory protection that updates memory-page permissions dynamically and enforces the least-privilege principle. Unfortunately, SGX does not provide such a memory-protection mechanism due to the lack of hardware support and the untrustedness of operating systems.

Memory-corruption attacks such as code-reuse attacks and data-only attacks have been a key threat to systems security. To counter these threats, researchers have proposed a variety of defenses, including control-flow integrity (CFI), code-pointer integrity (CPI), and code (re-)randomization. All of them, to be effective, require a security primitive—intra-process protection of confidentiality and/or integrity for sensitive data (such as CFI’s shadow stack and CPI’s safe region).In this paper, we propose SEIMI, a highly efficient intra-process memory isolation technique for memory-corruption defenses to protect their sensitive data. The core of SEIMI is to use the efficient Supervisor-mode Access Prevention (SMAP), a hardware feature that is originally used for preventing the kernel from accessing the user space, to achieve intra-process memory isolation. To leverage SMAP, SEIMI creatively executes the user code in the privileged mode. In addition to enabling the new design of the SMAP-based memory isolation, we further develop multiple new techniques to ensure secure escalation of user code, e.g., using the descriptor caches to capture the potential segment operations and configuring the Virtual Machine Control Structure (VMCS) to invalidate the execution result of the control registers related operations. Extensive experimental results show that SEIMI outperforms existing isolation mechanisms, including both the Memory Protection Keys (MPK) based scheme and the Memory Protection Extensions (MPX) based scheme, while providing secure memory isolation.

System software commonly uses indirect calls to realize dynamic program behaviors. However, indirect-calls also bring challenges to constructing a precise control-flow graph that is a standard pre-requisite for many static program-analysis and system-hardening techniques. Unfortunately, identifying indirect-call targets is a hard problem. In particular, modern compilers do not recognize indirect-call targets by default. Existing approaches identify indirect-call targets based on type analysis that matches the types of function pointers and the ones of address-taken functions. Such approaches, however, suffer from a high false-positive rate as many irrelevant functions may share the same types.

Operating system kernels carry a large number of security checks to validate security-sensitive variables and operations. For example, a security check should be embedded in a code to ensure that a user-supplied pointer does not point to the kernel space. Using security-checked variables is typically safe. However, in reality, security-checked variables are often subject to modification after the check. If a recheck is lacking after a modification, security issues may arise, e.g., adversaries can control the checked variable to launch critical attacks such as out-of-bound memory access or privilege escalation. We call such cases lacking-recheck (LRC) bugs, a subclass of TOCTTOU bugs, which have not been explored yet. In this paper, we present the first in-depth study of LRC bugs and develop LRSan, a static analysis system that systematically detects LRC bugs in OS kernels. Using an inter-procedural analysis and multiple new techniques, LRSan first automatically identifies security checks, critical variables, and uses of the checked variables, and then reasons about whether a modification is present after a security check. A case in which a modification is present but a recheck is lacking is an LRC bug. We apply LRSan to the latest Linux kernel and evaluate the effectiveness of LRSan. LRSan reports thousands of potential LRC cases, and we have confirmed 19 new LRC bugs. We also discuss patching strategies of LRC bugs based on our study and bug-fixing experience.

During system call execution, it is common for operating system kernels to read userspace memory multiple times (multi-reads). A critical bug may exist if the fetched userspace memory is subject to change across these reads, i.e., a race condition, which is known as a double-fetch bug. Prior works have attempted to detect these bugs both statically and dynamically. However, due to their improper assumptions and imprecise definitions regarding double-fetch bugs, their multi-read detection is inherently limited and suffers from significant false positives and false negatives. For example, their approach is unable to support device emulation, inter-procedural analysis, loop handling, etc. More importantly, they completely leave the task of finding real double-fetch bugs from the haystack of multi-reads to manual verification, which is expensive if possible at all. In this paper, we first present a formal and precise definition of double-fetch bugs and then implement a static analysis system - Deadline - to automatically detect double-fetch bugs in OS kernels. Deadline uses static program analysis techniques to systematically find multi-reads throughout the kernel and employs specialized symbolic checking to vet each multi-read for double-fetch bugs. We apply Deadline to Linux and FreeBSD kernels and find 23 new bugs in Linux and one new bug in FreeBSD. We further propose four generic strategies to patch and prevent double-fetch bugs based on our study and the discussion with kernel maintainers.

Operating system kernel is the de facto trusted computing base for most computer systems. To secure the OS kernel, many security mechanisms, e.g., kASLR and StackGuard, have been increasingly deployed to defend against attacks (e.g., code reuse attack). However, the effectiveness of these protections has been proven to be inadequate-there are many information leak vulnerabilities in the kernel to leak the randomized pointer or canary, thus bypassing kASLR and StackGuard. Other sensitive data in the kernel, such as cryptographic keys and file caches, can also be leaked. According to our study, most kernel information leaks are caused by uninitialized data reads. Unfortunately, existing techniques like memory safety enforcements and dynamic access tracking tools are not adequate or efficient enough to mitigate this threat.

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.

We propose a novel dynamic software watermarking design based on Return-Oriented Programming (ROP). Our design formats watermarking code into well-crafted data arrangements that look like normal data but could be triggered to execute. Once triggered, the pre-constructed ROP execution will recover the hidden watermark message. The proposed ROP-based watermarking technique is more stealthy and resilient over existing techniques since the watermarking code is allocated dynamically into data region and therefore out of reach of attacks based on code analysis. Evaluations show that our design not only achieves satisfying stealth and resilience, but also causes significantly lower overhead to the watermarked program.

Many software obfuscation techniques have been proposed to hide program instructions or logic and to make reverse engineering hard. In this paper, we introduce a new property in software obfuscation, namely program steganography, where certain instructions are "diffused" in others in such a way that they are non-existent until program execution. Program steganography does not raise suspicion in program analysis, and conforms to the W⊕X and mandatory code signing security mechanisms. We further implement RopSteg, a novel software obfuscation system, to provide (to a certain degree) program steganography using return-oriented programming. We apply RopSteg to eight Windows executables and evaluate the program steganography property in the corresponding obfuscated programs. Results show that RopSteg achieves program steganography with a small overhead in program size and execution time. RopSteg is the first attempt of driving return-oriented programming from the "dark side", i.e., using return-oriented programming in a non-attack application. We further discuss limitations of RopSteg in achieving program steganography.

Over the last few years, malware analysis has been one of the hottest areas in security research. Many techniques and tools have been developed to assist in automatic analysis of malware. This ranges from basic tools like disassemblers and decompilers, to static and dynamic tools that analyze malware behaviors, to automatic malware clustering and classification techniques, to virtualization technologies to assist malware analysis, to signature- and anomaly-based malware detection, and many others. However, most of these techniques and tools would not work on new attacking techniques, e.g., attacks that use return-oriented programming (ROP).