28 citations · 29 across the 9 of their papers we have counts for
9 papers
Recompilation Is Not Enough: Test-Guided Decompiled-C Repair
Yuhan Huang, Puzhuo Liu, Jianlei Chi
Decompiled C often becomes recompilable only after repair, but recompilation alone does not establish test-observed behavior. A recompiled command-line binary can still parse optio…
ShadowProbe: Language-Extensible Detection of Hidden Algorithmic Complexity Vulnerabilities
Yuanmin Xie, Xiangfan Wu, Wenhao Wu +6
Algorithmic Complexity Vulnerabilities (ACVs) arise when adversarial inputs trigger worst-case execution behavior, causing severe performance degradation or Denial-of-Service condi…
Semantic-aware and Self-improving Program Reduction via Agentic Large Language Models
Xintong Zhou, Hongxu Xu, Chunhao Liao +3
Reducing bug-triggering programs to their minimal essential form is a fundamental task in debugging language processors such as compilers and interpreters. Existing reduction techn…
MemPoison: Bypassing Selective Memory Mechanisms to Plant Backdoors in LLM Agents
Hongtao Wang, Se Yang, Yu Chen +1
Large language model (LLM) agents increasingly leverage long-term memory to support persistent and autonomous task execution. However, this capability also introduces a new attack…
CODEFUSE-DEBENCH: An Empirical Study on Readability, Recompilability, and Functionality
Puzhuo Liu, Yuhan Huang, Jianlei Chi +2
Binary decompilation aims to recover binaries into high-level source code, but existing evaluations mainly rely on syntactic similarity or single-axis readability metrics, which fa…
Weaver: Fuzzing JavaScript Engines at the JavaScript-WebAssembly Boundary
Lingming Zhang, Binbin Zhao, Puzhuo Liu +4
The security of modern JavaScript (JS) engines is critical since they provide the primary defense mechanism for executing untrusted code on the web. The recent integration of WebAs…
CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection
Qingyu Zhang, Puzhuo Liu, Peng Di +1
Version control relies on commit messages to convey the rationale for code changes, but these messages are often low quality and, more critically, inconsistent with their diffs-kno…
MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security
Yongjian Guo, Puzhuo Liu, Wanlun Ma +5
The Model Context Protocol (MCP) has emerged as a universal standard that enables AI agents to seamlessly connect with external tools, significantly enhancing their functionality.…
Harnessing the Power of LLM to Support Binary Taint Analysis
Puzhuo Liu, Chengnian Sun, Yaowen Zheng +8
This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter…