VLDB 2026 Research / reviewers in the wild / expert
Yuanping Yu
dblp:311/8860
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0002-4531-085XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | HTFuzz: Heap Operation Sequence Sensitive FuzzingabstractHeap-based temporal vulnerabilities (i.e., use-after-free, double-free and null pointer dereference) are highly sensitive to heap operation (e.g., memory allocation, deallocation and access) sequences. To efficiently find such vulnerabilities, traditional code coverage-guided fuzzing solutions could be promoted by integrating heap operation sequence feedback. But current sequence sensitive solutions have limitations in practice. Yuanping Yu, Xiangkun Jia, Yuwei Liu 0001, Qian Sang, Chao Zhang 0008, Purui Su |
ASE | 1 |
| 2021 | InstruGuard: Find and Fix Instrumentation Errors for Coverage-based Greybox FuzzingabstractAs one of the most successful methods at vulnerability discovery, coverage-based greybox fuzzing relies on the lightweight compile-time instrumentation to achieve the fine-grained coverage feedback of the target program. Researchers improve it by optimizing the coverage metrics without questioning the correctness of the instrumentation. However, instrumentation errors, including missed instrumentation locations and redundant instrumentation locations, harm the ability of fuzzers. According to our experiments, it is a common and severe problem in various coverage-based greybox fuzzers and at different compiler optimization levels.In this paper, we design and implement InstruGuard, an open-source and pragmatic platform to find and fix instrumentation errors. It detects instrumentation errors by static analysis on target binaries, and fixes them with a general solution based on binary rewriting. To study the impact of instrumentation errors and test our solutions, we built a dataset of 15 real-world programs and selected 6 representative fuzzers as targets. We used InstruGuard to check and repair the instrumented binaries with different fuzzers and different compiler optimization options. To evaluate the effectiveness of the repair, we ran the fuzzers with original instrumented programs and the repaired ones, and compared the fuzzing results from aspects of execution paths, line coverage, and real bug findings. The results showed that InstruGuard had corrected the instrumentation errors of different fuzzers and helped to find more bugs in the dataset. Moreover, we discovered one new zero-day vulnerability missed by other fuzzers with fixed instrumentation but without any changes to the fuzzers. Yuwei Liu 0001, Purui Su, Yuanping Yu, Xiangkun Jia |
ASE | 4 |