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Benjin Liu

dblp:431/9741 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0000-7883-6332ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 77% Network security · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security
vulnerability discovery
1.012026
SemFuzz: A Semantics-Aware Fuzzing Framework for Network Protocol Implementations · WWW 2026
Network security › protocol security
network protocol security
0.312026
SemFuzz: A Semantics-Aware Fuzzing Framework for Network Protocol Implementations · WWW 2026

Methods — techniques the papers use, named apart from their topics

large language model · 1.0fuzzing · 1.0RFC semantic rule extraction · 1.0
YearPublicationVenuePosition
2026 SemFuzz: A Semantics-Aware Fuzzing Framework for Network Protocol Implementations
abstract
Network protocols are the foundation of modern communication, yet their implementations often contain semantic vulnerabilities stemming from inadequate understanding of specification semantics. Existing gray-box and black-box testing approaches lack semantic modeling of protocols, making it difficult to precisely express testing intent and cover boundary conditions. Moreover, they typically rely on coarse-grained oracles such as crashes, which are inadequate for identifying deep semantic vulnerabilities. To address these limitations, we present a semantics-aware fuzzing framework, SemFuzz. The framework leverages large language models to extract structured semantic rules from RFC documents and generates test cases that intentionally violate these rules to encode specific testing intents. It then detects deep semantic vulnerabilities by comparing the observed responses with the expected ones. Evaluation on seven widely deployed protocol implementations shows that SemFuzz identified sixteen potential vulnerabilities, ten of which have been confirmed. Among the confirmed vulnerabilities, five were previously unknown and four have been assigned CVEs. These results demonstrate the effectiveness of SemFuzz in detecting semantic vulnerabilities.
Yanbang Sun, Quan Luo, Yuelin Wang, Benjin Liu, Xiaohong Li 0001, Junjie Wang 0007
WWW5