36 citations · 112 across the 7 of their papers we have counts for
7 papers
MirrorFuzz: Leveraging LLM and Shared Bugs for Deep Learning Framework APIs Fuzzing
Shiwen Ou, Yuwei Li, Lu Yu +6
Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can cascade into critical issues in…
Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulation
Wenlong Meng, Fan Zhang, Wendao Yao +4
Large language models (LLMs) have demonstrated significant utility in a wide range of applications; however, their deployment is plagued by security vulnerabilities, notably jailbr…
G-Fuzz: A Directed Fuzzing Framework for gVisor
Yuwei Li, Yuan Chen, Shouling Ji +6
gVisor is a Google-published application-level kernel for containers. As gVisor is lightweight and has sound isolation, it has been widely used in many IT enterprises \cite{Stripe,…
An Empirical Study on the Distance Metric in Guiding Directed Grey-box Fuzzing
Tingke Wen, Yuwei Li, Lu Zhang +2
Directed grey-box fuzzing (DGF) aims to discover vulnerabilities in specific code areas efficiently. Distance metric, which is used to measure the quality of seed in DGF, is a cruc…
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers
Yuwei Li, Shouling Ji, Yuan Chen +9
A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still cha…
V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing
Yuwei Li, Shouling Ji, Chenyang Lv +4
Fuzzing is a technique of finding bugs by executing a software recurrently with a large number of abnormal inputs. Most of the existing fuzzers consider all parts of a software equ…