Nicolas Badoux (EPFL), Flavio Toffalini (Ruhr-Universität Bochum, EPFL), Yuseok Jeon (UNIST), Mathias Payer (EPFL)

Type confusion, or bad casting, is a common C++ attack vector. Such vulnerabilities cause a program to interpret an object as belonging to a different type, enabling powerful attacks, like control-flow hijacking. C++ limits runtime checks to polymorphic classes because only those have inline type information. The lack of runtime type information throughout an object’s lifetime makes it challenging to enforce continuous checks and thereby prevent type confusion during downcasting. Current solutions either record type information for all objects disjointly, incurring prohibitive runtime overhead, or restrict protection to a fraction of all objects.
Our C++ dialect, type++, enforces the paradigm that each allocated object involved in downcasting carries type information throughout its lifetime, ensuring correctness by enabling type checks wherever and whenever necessary. As not just polymorphic objects but all objects are typed, all down-to casts can now be dynamically verified. Compared to existing solutions, our strategy greatly reduces runtime cost and enables type++ usage both during testing and as mitigation. Targeting SPEC CPU2006 and CPU2017, we compile and run 2,040 kLoC, while changing only 314 LoC. To help developers, our static analysis warns where code changes in target programs may be necessary. Running the compiled benchmarks results in negligible performance overhead (1.19% on SPEC CPU2006 and 0.82% on SPEC CPU2017) verifying a total of 90B casts (compared to 3.8B for the state-of-the-art, a 23× improvement). type++ discovers 122 type confusion issues in the SPEC CPU benchmarks among which 62 are new. Targeting Chromium, we change 229 LoC out of 35 MLoC to protect 94.6% of the classes that could be involved in downcasting vulnerabilities, while incurring only 0.98% runtime overhead compared to the baseline.

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Robert Dumitru (Ruhr University Bochum and The University of Adelaide), Thorben Moos (UCLouvain), Andrew Wabnitz (Defence Science and Technology Group), Yuval Yarom (Ruhr University Bochum)

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Xiaochen Li (University of Virginia), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chen Gong (University of Virginia), Shuya Feng (University of Connecticut), Yuan Hong (University of Connecticut), Tianhao Wang (University of Virginia)

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Yuejie Wang (Peking University), Qiutong Men (New York University), Yongting Chen (New York University Shanghai), Jiajin Liu (New York University Shanghai), Gengyu Chen (Carnegie Mellon University), Ying Zhang (Meta), Guyue Liu (Peking University), Vyas Sekar (Carnegie Mellon University)

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CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

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