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.

View More Papers

Generating API Parameter Security Rules with LLM for API...

Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of…

Read More

CounterSEVeillance: Performance-Counter Attacks on AMD SEV-SNP

Stefan Gast (Graz University of Technology), Hannes Weissteiner (Graz University of Technology), Robin Leander Schröder (Fraunhofer SIT, Darmstadt, Germany and Fraunhofer Austria, Vienna, Austria), Daniel Gruss (Graz University of Technology)

Read More

MOBIDOJO: A Virtual Security Combat Platform for 5G Cellular...

Hyunwoo Lee (Ohio State University), Haohuang Wen (Ohio State University), Phillip Porras (SRI), Vinod Yegneswaran (SRI), Ashish Gehani (SRI), Prakhar Sharma (SRI), Zhiqiang Lin (Ohio State University)

Read More

Automated Mass Malware Factory: The Convergence of Piggybacking and...

Heng Li (Huazhong University of Science and Technology), Zhiyuan Yao (Huazhong University of Science and Technology), Bang Wu (Huazhong University of Science and Technology), Cuiying Gao (Huazhong University of Science and Technology), Teng Xu (Huazhong University of Science and Technology), Wei Yuan (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University)

Read More