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DBMS bugs can cause serious consequences, posing severe security and privacy concerns. This paper works towards the detection of memory bugs and logic bugs in DBMSs, and aims to solve the two innate challenges, including how to generate semantically correct SQL queries in a test case, and how to propose effective oracles to capture logic bugs. To this end, our system proposes two key techniques. The first key technique is called context-sensitive instantiation, which considers all static semantic requirements (including but not limited to the identifier type used by existing systems) to generate semantically valid SQL queries. The second key technique is called multi-plan execution, which can effectively capture logic bugs. Given a test case, multi-plan execution makes the DBMS execute all query plans instead of the default optimal one, and compares the results. A logic bug is detected if a difference is found among the execution results of the executed query plans. We have implemented a prototype system called Kangaroo and applied it to three widely used and well-tested DBMSs, including SQLite, PostgreSQL, and MySQL. Our system successfully detected 50 new bugs. The comparison between our system with the state-of-the-art systems shows that our system outperforms them in terms of the number of generated semantically valid SQL queries, the explored code paths during testing, and the detected bugs.

Node.js provides Node.js applications with system interaction capabilities using system calls. However, such convenience comes with a price, i.e., the attack surface of JavaScript arbitrary code execution (ACE) vulnerabilities is expanded to the system call level. There lies a noticeable gap between existing protection techniques in the JavaScript code level (either by code debloating or read-write-execute permission restriction) and a targeted defense for emerging critical system call level exploitation. To fill the gap, we design and implement HODOR, a lightweight runtime protection system based on enforcing precise system call restrictions when running a Node.js application. HODOR achieved this by addressing several nontrivialial technical challenges. First, HODOR requires to construct high-quality call graphs for both the Node.js application (in JavaScript) and its underlying Node.js framework (in JavaScript and C/C++). Specifically, HODOR incorporates several important optimizations in both the JavaScript and C/C++ level to improve the state-of-the-art tools for building more precise call graphs. Then, HODOR creates the main-thread whitelist and the thread-pool whitelist respectively containing the identified necessary system calls based on the call graphs mappings. Finally, with the whitelists, HODOR implements lightweight system call restriction using the Linux kernel feature Secure Computing Mode (seccomp) to shrink the attack surface. We utilize HODOR to protect 83 real-world Node.js applications compromised by arbitrary code/command execution attacks. HODOR could reduce the attack surface to 16.75% on average with negligible runtime overhead (i.e., <3%).

Speaker recognition systems (SRSs) have recently been shown to be vulnerable to adversarial attacks, raising significant security concerns. In this work, we systematically investigate transformation and adversarial training based defenses for securing SRSs. According to the characteristic of SRSs, we present 22 diverse transformations and thoroughly evaluate them using 7 recent promising adversarial attacks (4 white-box and 3 black-box) on speaker recognition. With careful regard for best practices in defense evaluations, we analyze the strength of transformations to withstand adaptive attacks. We also evaluate and understand their effectiveness against adaptive attacks when combined with adversarial training. Our study provides lots of useful insights and findings, many of them are new or inconsistent with the conclusions in the image and speech recognition domains, e.g., variable and constant bit rate speech compressions have different performance, and some non-differentiable transformations remain effective against current promising evasion techniques which often work well in the image domain. We demonstrate that the proposed novel feature-level transformation combined with adversarial training is rather effective compared to the sole adversarial training in a complete white-box setting, e.g., increasing the accuracy by 13.62% and attack cost by two orders of magnitude, while other transformations do not necessarily improve the overall defense capability. This work sheds further light on the research directions in this field. We also release our evaluation platform SPEAKERGUARD to foster further research.