11 citations · 38 across the 14 of their papers we have counts for
14 papers
GIF: Locally Sound Geometric Information Flow Control for LLMs
Adam Storek, Nikolaus Holzer, Zhuo Zhang +1
Large language models increasingly mediate interactions between sensitive data, untrusted inputs, and privileged actions in agentic systems, creating security and privacy risks. Th…
ASPIRER: Bypassing System Prompts With Permutation-based Backdoors in LLMs
Lu Yan, Siyuan Cheng, Xuan Chen +4
Large Language Models (LLMs) have become integral to many applications, with system prompts serving as a key mechanism to regulate model behavior and ensure ethical outputs. In thi…
Threat Behavior Textual Search by Attention Graph Isomorphism
Chanwoo Bae, Guanhong Tao, Zhuo Zhang +1
Cyber attacks cause over $1 trillion loss every year. An important task for cyber security analysts is attack forensics. It entails understanding malware behaviors and attack orig…
CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking
Zian Su, Xiangzhe Xu, Ziyang Huang +4
Transformer based code models have impressive performance in many software engineering tasks. However, their effectiveness degrades when symbols are missing or not informative. The…
Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation and Echopraxia
Guangyu Shen, Siyuan Cheng, Kaiyuan Zhang +6
Large Language Models (LLMs) have become prevalent across diverse sectors, transforming human life with their extraordinary reasoning and comprehension abilities. As they find incr…
MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse Worlds
Xiaolong Jin, Zhuo Zhang, Xiangyu Zhang
Large Language Model (LLM) alignment aims to ensure that LLM outputs match with human values. Researchers have demonstrated the severity of alignment problems with a large spectrum…
Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs
Guanhong Tao, Siyuan Cheng, Zhuo Zhang +3
The emergence of large language models (LLMs) has significantly accelerated the development of a wide range of applications across various fields. There is a growing trend in the c…
Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs
Zhuo Zhang, Guangyu Shen, Guanhong Tao +2
Large Language Models (LLMs) are now widely used in various applications, making it crucial to align their ethical standards with human values. However, recent jail-breaking method…
PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model
Xiangzhe Xu, Zhou Xuan, Shiwei Feng +7
Binary similarity analysis determines if two binary executables are from the same source program. Existing techniques leverage static and dynamic program features and may utilize a…
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP
Lu Yan, Zhuo Zhang, Guanhong Tao +4
Backdoor attacks have emerged as a prominent threat to natural language processing (NLP) models, where the presence of specific triggers in the input can lead poisoned models to mi…
Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Xiangzhe Xu, Zhuo Zhang, Zian Su +8
Decompilation aims to recover the source code form of a binary executable. It has many security applications, such as malware analysis, vulnerability detection, and code hardening.…
Backdoor Vulnerabilities in Normally Trained Deep Learning Models
Guanhong Tao, Zhenting Wang, Siyuan Cheng +7
We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can b…
DECK: Model Hardening for Defending Pervasive Backdoors
Guanhong Tao, Yingqi Liu, Siyuan Cheng +5
Pervasive backdoors are triggered by dynamic and pervasive input perturbations. They can be intentionally injected by attackers or naturally exist in normally trained models. They…
Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor Defense
Guangyu Shen, Yingqi Liu, Guanhong Tao +5
We develop a novel optimization method for NLPbackdoor inversion. We leverage a dynamically reducing temperature coefficient in the softmax function to provide changing loss landsc…