Detecting Memory Errors in Python Native Code by Tracking Object Lifecycle with Reference Count - Ma, Xutong; Yan, Jiwei; Zhang, Hao; Yan, Jun; Zhang, Jian (IEEE/ACM International Conference on Automated Software Engineering : [proceedings], 2023)
Third-party Python modules are usually implemented as binary extensions by using native code (C/C++) to provide additional features and runtime acceleration. In native code, the heap-allocated PyObjects are managed by the reference counting mechanism provided in Python/C APIs for automatic reclaiming. Hence, improper refcount manipulations can lead to memory leaks and use-after-free problems, and cannot be detected by simply pairing the occurrence of source and sink points. To detect such problems, state-of-the-art approaches have made groundbreaking contributions to identifying inappropriate final refcount values before returning from native code to Python. However, not all problems can be exposed at the end of a path. To detect those hidden in the middle of a path in native code, it is also crucial to track the lifecycle state of PyObjects through the refcount and lifecycle operations in API calls. To achieve this goal, we propose the PyObject State Transition Model (PSTM) recording the lifecycle states and refcount values of PyObjects to describe the effects of Python/C API calls and pointer operations. We track state transitions of PyObjects with symbolic execution based on the model, and report problems when a statement triggers a transition to buggy states. The program state is also expanded to handle pointer nullity checks and smart pointers of PyObjects. We conduct experiments on 12 open-source projects and detect 259 real problems out of 280 reports, which is twice as many bugs as state-of-the-art approaches. We submit 168 real bugs to those active projects, and 106 issues are either confirmed or resolved.