4 citations · 18 across the 22 of their papers we have counts for
7 papers · 1 filter
From Poisoned to Aware: Fostering Backdoor Self-Awareness in LLMs
Guangyu Shen, Siyuan Cheng, Xiangzhe Xu +4
Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret triggers appear in the input. Exi…
When Clear Skies Cloud Trust: Environmental Cues and the Paradox of Confidence in Government
Xiangzhe Xu, Ran Wu
Government trust, as a core concept in political economy and public policy research, serves as a fundamental cornerstone of democratic legitimacy and state capacity. This paper exa…
Position: Intelligent Coding Systems Should Write Programs with Justifications
Xiangzhe Xu, Shiwei Feng, Zian Su +2
Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven co…
ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants
Xiangzhe Xu, Guangyu Shen, Zian Su +9
AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecuri…
MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
Lu Yan, Zhuo Zhang, Xiangzhe Xu +5
Large language models (LLMs) have democratized software development, reducing the expertise barrier for programming complex applications. This accessibility extends to malicious so…
TAI3: Testing Agent Integrity in Interpreting User Intent
Shiwei Feng, Xiangzhe Xu, Xuan Chen +5
LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation o…
RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing
Jinyao Guo, Chengpeng Wang, Xiangzhe Xu +2
Code auditing is the process of reviewing code with the aim of identifying bugs. Large Language Models (LLMs) have demonstrated promising capabilities for this task without requiri…