Communication protocols form the bedrock of our interconnected world, yet vulnerabilities
within their implementations pose significant security threats. Recent developments have …

Program semantics learning is the core and fundamental for various code intelligent tasks
eg, vulnerability detection, clone detection. A considerable amount of existing works …

Abstract LLM-based (Large Language Model) fuzz driver generation is a promising research
area. Unlike traditional program analysis-based method, this text-based approach is more …

Greybox fuzzing has become one of the most effective vulnerability discovery techniques.
However, greybox fuzzing techniques cannot be directly applied to applications in IoT …

Protocol implementations are essential components in network infrastructures. Flaws hidden
in the implementations can easily render devices vulnerable to adversaries. Therefore …

Crafting high-quality fuzz drivers not only is time-consuming but also requires a deep
understanding of the library. However, the state-of-the-art automatic fuzz driver generation …

Large Language Models and Multi-Modal LLMs have become pervasive, and so does the
importance of their security; yet, modern LLMs are known to be vulnerable to jailbreaking …

Despite the fact that the state-of-the-art fuzzers can generate inputs efficiently, existing fuzz
drivers still cannot adequately cover entries in libraries. Most of these fuzz drivers are crafted …

Directed grey-box fuzzing (DGF) is a security testing technique that aims to steer the fuzzer
towards predefined target sites in the program. To gain directedness, DGF prioritizes the …

WebGL is a set of standardized JavaScript APIs for GPU accelerated graphics. Security of
the WebGL interface is paramount because it exposes remote and unsandboxed access to …