Attack surface reduction is a security technique that secures the operating system by removing the unnecessary code or features of a program. By restricting the system calls that programs can use, the system call sandbox is able to reduce the exposed attack surface of the operating system and prevent attackers from damaging it through vulnerable programs. Ideally, programs should only retain access to system calls they require for normal execution. Many researchers focus on adopting static analysis to automatically restrict the system calls for each program. However, these methods do not adjust the restriction policy along with program execution. Thus, they need to permit all system calls required for program functionalities.
We observe that some system calls, especially security-sensitive ones, are used a few times in certain stages of a program's execution and then never used again. This motivates us to minimize the set of required system calls dynamically. In this paper, we propose \Model, which gradually disables access to unnecessary system calls throughout the program's execution. To accomplish this, we utilize partial order analysis to transform the program into a partially ordered graph, which enables efficient identification of the necessary system calls at any given point during program execution. Once a system call is no longer required by the program, \Model can restrict it immediately. To evaluate \Model, we applied it to seven widely-used programs with an average of 615 KLOC, including web servers and databases. With partial order analysis, \Model restricts an average of 23.50, 16.86, and 15.89 more system calls than the state-of-the-art Chestnut, Temporal Specialization, and the configuration-aware sandbox, C2C, respectively. For mitigating malicious exploitations, on average, \Model defeats 83.42% of 1726 exploitation payloads with only a 5.07% overhead.
Thu 26 OctDisplayed time zone: Lisbon change
16:00 - 17:30 | |||
16:00 18mTalk | Historia: Refuting Callback Reachability with Message-History Logics OOPSLA Shawn Meier University of Colorado at Boulder, Sergio Mover École Polytechnique, Gowtham Kaki University of Colorado at Boulder, Bor-Yuh Evan Chang University of Colorado at Boulder; Amazon DOI | ||
16:18 18mTalk | Exploiting the Sparseness of Control-Flow and Call Graphs for Efficient and On-Demand Algebraic Program Analysis OOPSLA Giovanna Kobus Conrado Hong Kong University of Science and Technology, Amir Kafshdar Goharshady Hong Kong University of Science and Technology, Kerim Kochekov Hong Kong University of Science and Technology, Yun Chen Tsai Hong Kong University of Science and Technology, Ahmed Khaled Zaher Hong Kong University of Science and Technology DOI | ||
16:36 18mTalk | A Cocktail Approach to Practical Call Graph Construction OOPSLA Yuandao Cai Hong Kong University of Science and Technology, Charles Zhang Hong Kong University of Science and Technology DOI | ||
16:54 18mTalk | Building Dynamic System Call Sandbox with Partial Order Analysis OOPSLA Quan Zhang Tsinghua University, Chijin Zhou Tsinghua University, Yiwen Xu Tsinghua University, Zijing Yin Tsinghua University, Mingzhe Wang Tsinghua University, Zhuo Su Tsinghua University, Chengnian Sun University of Waterloo, Yu Jiang Tsinghua University, Jiaguang Sun Tsinghua University DOI | ||
17:12 18mTalk | Improving Oracle-Guided Inductive Synthesis by Efficient Question Selection OOPSLA Ruyi Ji Peking University, Chaozhe Kong Peking University, Yingfei Xiong Peking University, Zhenjiang Hu Peking University DOI |