PPTFI: Patch Presence Test for Function-Irrelevant Patches
In the past decades, downstream manufacturers often failed to timely adopt the security patches, resulting in some discovered vulnerabilities still posing serious risks. In the currently popular field of blockchain smart contracts, this is also a thorny issue. Although some new methods have been proposed to update and patch smart contracts deployed in blockchain networks, the binary codes of most vulnerable smart contracts are still being executed without patching. To detect the unpatched binaries as soon as possible, signature based patch presence tests and software similarity based patch presence tests have been proposed to check whether a certain patch is applied to the released software binaries. However, a large number of bug-fix patches are irrelevant to functions. They are small in size and only modify program entities other than functions. Existing signature-based patch detection methods and software similarity-based tools have limitations in detecting such patches. In this paper, we propose PPTFI, a patch presence test for function-irrelevant patches. PPTFI understands these patches and extracts code and data information as patch signatures for scanning target binaries. Being evaluated on 62 different versions of 31 real-world function-irrelevant patches and 512 binaries across 16 various compilation environments, PPTFI achieves an accuracy of 77.54%, significantly outperforming existing techniques.
Authors
- Juzheng Zhang (ORCID: https://orcid.org/0000-0001-7614-7592)
- Daojing He (ORCID: https://orcid.org/0000-0002-3820-8128)
- Sammy Chan (ORCID: https://orcid.org/0000-0002-8524-229X)
- Hongyi Li (ORCID: https://orcid.org/0000-0003-1383-8972)
- Sencun Zhu (ORCID: https://orcid.org/0000-0002-1047-7967)
Institutions
- Pennsylvania State University (US)
- City University of Hong Kong (HK)
- Harbin Institute of Technology (CN)
- Jiangxi University of Science and Technology (CN)
- East China Normal University (CN)
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/msn63567.2024.00070
- Primary Topic
- Neurological disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fok Ying Tung Education Foundation
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities