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

Mens Sana In Corpore Sano: Sound Firmware Corpora for...

René Helmke (Fraunhofer FKIE), Elmar Padilla (Fraunhofer FKIE, Germany), Nils Aschenbruck (University of Osnabrück)

Read More

Trim My View: An LLM-Based Code Query System for...

Sima Arasteh (University of Southern California), Pegah Jandaghi, Nicolaas Weideman (University of Southern California/Information Sciences Institute), Dennis Perepech, Mukund Raghothaman (University of Southern California), Christophe Hauser (Dartmouth College), Luis Garcia (University of Utah Kahlert School of Computing)

Read More

Modeling End-User Affective Discomfort With Mobile App Permissions Across...

Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

Read More

LAMP: Lightweight Approaches for Latency Minimization in Mixnets with...

Mahdi Rahimi (KU Leuven), Piyush Kumar Sharma (University of Michigan), Claudia Diaz (KU Leuven)

Read More