5 citations · 7 across the 8 of their papers we have counts for
9 papers · 1 filter
To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt
Zhilong Wang, Neha Nagaraja, Lan Zhang +3
LLM agents are widely used as agents for customer support, content generation, and code assistance. However, they are vulnerable to prompt injection attacks, where adversarial inpu…
Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Carrier Articles
Zhilong Wang, Haizhou Wang, Nanqing Luo +4
Large Language Model (LLM) jailbreak refers to a type of attack aimed to bypass the safeguard of an LLM to generate contents that are inconsistent with the safe usage guidelines. B…
Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Logic Chain Injection
Zhilong Wang, Yebo Cao, Peng Liu
Jailbreak attacks on Language Model Models (LLMs) entail crafting prompts aimed at exploiting the models to generate malicious content. Existing jailbreak attacks can successfully…
A hybrid LLM workflow can help identify user privilege related variables in programs of any size
Haizhou Wang, Zhilong Wang, Peng Liu
Many programs involves operations and logic manipulating user privileges, which is essential for the security of an organization. Therefore, one common malicious goal of attackers…
How Does Naming Affect LLMs on Code Analysis Tasks?
Zhilong Wang, Lan Zhang, Chen Cao +3
The Large Language Models (LLMs), such as GPT and BERT, were proposed for natural language processing (NLP) and have shown promising results as general-purpose language models. An…
Identifying Non-Control Security-Critical Data through Program Dependence Learning
Zhilong Wang, Haizhou Wang, Hong Hu +1
As control-flow protection gets widely deployed, it is difficult for attackers to corrupt control-data and achieve control-flow hijacking. Instead, data-oriented attacks, which man…