Visible to the public Biblio

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2022-05-19
Kwon, Seongkyeong, Woo, Seunghoon, Seong, Gangmo, Lee, Heejo.  2021.  OCTOPOCS: Automatic Verification of Propagated Vulnerable Code Using Reformed Proofs of Concept. 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :174–185.
Addressing vulnerability propagation has become a major issue in software ecosystems. Existing approaches hold the promise of detecting widespread vulnerabilities but cannot be applied to verify effectively whether propagated vulnerable code still poses threats. We present OCTOPOCS, which uses a reformed Proof-of-Concept (PoC), to verify whether a vulnerability is propagated. Using context-aware taint analysis, OCTOPOCS extracts crash primitives (the parts used in the shared code area between the original vulnerable software and propagated software) from the original PoC. OCTOPOCS then utilizes directed symbolic execution to generate guiding inputs that direct the execution of the propagated software from the entry point to the shared code area. Thereafter, OCTOPOCS creates a new PoC by combining crash primitives and guiding inputs. It finally verifies the propagated vulnerability using the created PoC. We evaluated OCTOPOCS with 15 real-world C and C++ vulnerable software pairs, with results showing that OCTOPOCS successfully verified 14 propagated vulnerabilities.
2018-06-11
Shu, Rui, Gu, Xiaohui, Enck, William.  2017.  A Study of Security Vulnerabilities on Docker Hub. Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy. :269–280.
Docker containers have recently become a popular approach to provision multiple applications over shared physical hosts in a more lightweight fashion than traditional virtual machines. This popularity has led to the creation of the Docker Hub registry, which distributes a large number of official and community images. In this paper, we study the state of security vulnerabilities in Docker Hub images. We create a scalable Docker image vulnerability analysis (DIVA) framework that automatically discovers, downloads, and analyzes both official and community images on Docker Hub. Using our framework, we have studied 356,218 images and made the following findings: (1) both official and community images contain more than 180 vulnerabilities on average when considering all versions; (2) many images have not been updated for hundreds of days; and (3) vulnerabilities commonly propagate from parent images to child images. These findings demonstrate a strong need for more automated and systematic methods of applying security updates to Docker images and our current Docker image analysis framework provides a good foundation for such automatic security update. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer