In recent years, kernel fuzzing research has experienced a significant surge. Among various kernel fuzzers, Syzkaller stands out as the state-of-the-art tool, having identified over 5,000 bugs in the Linux kernel. Syzkaller’s success can be attributed to its utilization of manually-curated syscall specifications provided by kernel experts. However, this process is time-consuming and not scalable due to complex input structures and unknown dependencies among syscalls. Consequently, a substantial portion of the kernel codebase, specifically kernel drivers, lacks specifications, posing a significant security risk.In this paper, we introduce SyzGen++, an innovative approach for automatically inferring dependencies between syscalls and generating specifications without relying on existing test suites. Specifically, we define two fundamental building blocks of insertion and lookup operations and their pairing to accurately identify dependencies. We evaluated SyzGen++ against existing state-of-the-art techniques on both Linux and macOS drivers. Our results demonstrate that SyzGen++ uncovered 245 more dependencies. Furthermore, SyzGen++ outperforms DIFUZE, KSG, and SyzDescribe in terms of code coverage, achieving 71%, 67%, and 39% improvement on average, respectively. Notably, our evaluation discovered 10 previously unknown bugs in Linux Kernel 6.2 using specifications generated by SyzGen++, resulting in 6 CVEs, which demonstrates its effectiveness in identifying vulnerabilities.

Fuzz testing operating system kernels has been effective overall in recent years. For example, syzkaller manages to find thousands of bugs in the Linux kernel since 2017. One necessary component of syzkaller is a collection of syscall descriptions that are often provided by human experts. However, to our knowledge, current syscall descriptions are largely written manually, which is both time-consuming and error-prone. It is especially challenging considering that there are many kernel drivers (for new hardware devices and beyond) that are continuously being developed and evolving over time. In this paper, we present a principled solution for generating syscall descriptions for Linux kernel drivers. At its core, we summarize and model the key invariants or programming conventions, extracted from the "contract" between the core kernel and drivers. This allows us to understand programmatically how a kernel driver is initialized and how its associated interfaces are constructed. With this insight, we have developed a solution in a tool called SyzDescribe that has been tested for over hundreds of kernel drivers. We show that the syscall descriptions produced by SyzDescribe are competitive to manually-curated ones, and much better than prior work (i.e., DIFUZE and KSG). Finally, we analyze the gap between our descriptions and the ground truth and point to future improvement opportunities.

Online advertisers have been quite successful in circumventing traditional adblockers that rely on manually curated rules to detect ads. As a result, adblockers have started to use machine learning (ML) classifiers for more robust detection and blocking of ads. Among these, AdGraph which leverages rich contextual information to classify ads, is arguably, the state of the art ML-based adblocker. In this paper, we present a4, a tool that intelligently crafts adversarial ads to evade AdGraph. Unlike traditional adversarial examples in the computer vision domain that can perturb any pixels (i.e., unconstrained), adversarial ads generated by a4 are actionable in the sense that they preserve the application semantics of the web page. Through a series of experiments we show that a4 can bypass AdGraph about 81% of the time, which surpasses the state-of-the-art attack by a significant margin of 145.5%, with an overhead of <20% and perturbations that are visually imperceptible in the rendered webpage. We envision that a4’s framework can be used to potentially launch adversarial attacks against other ML-based web applications.

Static analysis is known to yield numerous false alarms when used in bug finding, especially for complex vulnerabilities in large code bases like the Linux kernel. One important class of such complex vulnerabilities is what we call "high-order taint style vulnerability", where the taint flow from the user input to the vulnerable site crosses the boundary of a single entry function invocation (i.e., syscall). Due to the large scope and high precision requirement, few have attempted to solve the problem. In this paper, we present SUTURE, a highly precise and scalable static analysis tool capable of discovering high-order vulnerabilities in OS kernels. SUTURE employs a novel summary-based high-order taint flow construction approach to efficiently enumerate the cross-entry taint flows, while incorporating multiple innovative enhancements on analysis precision that are unseen in existing tools, resulting in a highly precise inter-procedural flow-, context-, field-, index-, and opportunistically path-sensitive static taint analysis. We apply SUTURE to discover high-order taint vulnerabilities in multiple Android kernels from mainstream vendors (e.g., Google, Samsung, Huawei), the results show that SUTURE can both confirm known high-order vulnerabilities and uncover new ones. So far, SUTURE generates 79 true positive warning groups, of which 19 have been confirmed by the vendors, including a high severity vulnerability rated by Google. SUTURE also achieves a reasonable false positive rate (51.23%) perceived by users of our tool.