HeapTherapy+: Efficient Handling of (Almost) All Heap Vulnerabilities Using Targeted Calling-Context Encoding
Qiang Zeng, Golam Kayas, Emil Mohammed, Lannan Luo, Xiaojiang Du, Junghwan Rhee · 2019
Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hence deterring users from deploying them. We propose a heap patching system HEAPTHERAPY+ that simultaneously has the following prominent advantages: (1) generating patches without manual efforts; (2) installing patches without altering the code (so called code-less patching); (3) handling various heap vulnerability types; (4) imposing a very low overhead; and (5) no dependency on specific heap allocators. As a separate contribution, we propose targeted calling context encoding, which is a suite of algorithms for optimizing calling context encoding, an important technique with applications in many areas. The system properly combines heavyweight offline attack analysis with lightweight online defense generation, and provides a new countermeasure against heap attacks. The evaluation shows that the system is effective and efficient.