Sze Yiu Chau (Purdue University), Moosa Yahyazadeh (The University of Iowa), Omar Chowdhury (The University of Iowa), Aniket Kate (Purdue University), Ninghui Li (Purdue University)

We discuss how symbolic execution can be used to not only find low-level errors but also analyze the semantic correctness of protocol implementations. To avoid manually crafting test cases, we propose a strategy of meta-level search, which leverages constraints stemmed from the input formats to automatically generate concolic test cases. Additionally, to aid root-cause analysis, we develop constraint provenance tracking (CPT), a mechanism that associates atomic sub-formulas of path constraints with their corresponding source level origins. We demonstrate the power of symbolic analysis with a case study on PKCS#1 v1.5 signature verification. Leveraging meta-level search and CPT, we analyzed 15 recent open-source implementations using symbolic execution and found semantic flaws in 6 of them. Further analysis of these flaws showed that 4 implementations are susceptible to new variants of the Bleichenbacher low- exponent RSA signature forgery. One implementation suffers from potential denial of service attacks with purposefully crafted signatures. All our findings have been responsibly shared with the affected vendors. Among the flaws discovered, 6 new CVEs have been assigned to the immediately exploitable ones.

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Orcun Cetin (Delft University of Technology), Carlos Gañán (Delft University of Technology), Lisette Altena (Delft University of Technology), Takahiro Kasama (National Institute of Information and Communications Technology), Daisuke Inoue (National Institute of Information and Communications Technology), Kazuki Tamiya (Yokohama National University), Ying Tie (Yokohama National University), Katsunari Yoshioka (Yokohama National University), Michel van Eeten (Delft…

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TextBugger: Generating Adversarial Text Against Real-world Applications

Jinfeng Li (Zhejiang University), Shouling Ji (Zhejiang University), Tianyu Du (Zhejiang University), Bo Li (University of California, Berkeley), Ting Wang (Lehigh University)

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NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

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