Tommaso Frassetto (Technical University of Darmstadt), Patrick Jauernig (Technical University of Darmstadt), David Koisser (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Software vulnerabilities are one of the major threats to computer security and have caused substantial damage over the past decades. Consequently, numerous techniques have been proposed to mitigate the risk of exploitation of vulnerable programs. One of the most relevant defense mechanisms is Control-Flow Integrity (CFI): multiple variants have been introduced and extensively discussed in academia as well as deployed in the industry. However, it is hard to compare the security guarantees of these implementations as existing metrics (such as AIR) do not consider the different usefulness to the attacker of different basic blocks, which are the fundamental components that constitute the code of any application.

This paper introduces BlockInsulation and CFGInsulation, novel metrics designed to overcome this limitation by modeling the usefulness of basic blocks for an attacker trying to traverse the program’s control-flow graph. Moreover, we propose a new CFI policy generator, named NumCFI, which is orthogonal to existing policy generators and prevents the attacker from taking shortcuts from vulnerable code to a system call instruction. We evaluate NumCFI, as well as a number of other CFI policy generators, using BlockInsulation, CFGInsulation, and existing metrics. Lastly, we describe l+tCFI, our implementation that combines NumCFI and an existing label-based policy, with a performance overhead of just 1.27%.

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COOPER: Testing the Binding Code of Scripting Languages with...

Peng Xu (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences), Yanhao Wang (QI-ANXIN Technology Research Institute), Hong Hu (Pennsylvania State University), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences)

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Demo #15: Remote Adversarial Attack on Automated Lane Centering

Yulong Cao (University of Michigan), Yanan Guo (University of Pittsburgh), Takami Sato (UC Irvine), Qi Alfred Chen (UC Irvine), Z. Morley Mao (University of Michigan) and Yueqiang Cheng (NIO)

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MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing

Gen Zhang (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Tai Yue (National University of Defense Technology), Xiangdong Kong (National University of Defense Technology), Shan Huang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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