17 citations · 58 across the 25 of their papers we have counts for
21 papers · 1 filter
Discovery or Memorization? A Faithful Benchmark for Real-World Business-Logic Vulnerability Discovery
Rui Yang, Yichao Xu, Zhuohao Zhang +4
The rapid advancement of cybersecurity agents in vulnerability discovery has increased the need for benchmarks that evaluate their capabilities accurately. However, synthetic bench…
EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents
Nanxi Li, Yingzi Ma, Yulong Cao +7
Large language model (LLM) agents can translate natural-language requests into real-world actions, creating safety risks from both indirect prompt injections and directly harmful r…
KeyPooling: Measuring Where LLM API Relay Paths Collapse Prompt Cache Isolation
Bowen Sun, Yixi Cai, Xiaogeng Liu +3
Large language model (LLM) API relays authenticate customers separately but often forward requests through shared provider credentials. Providers scope prompt caches to upstream pr…
Decomposition Attacks Across Unlinkable Identities: Limits of Stateful Defenses for LLM Services
Bowen Sun, Zhengyue Zhao, Xiaogeng Liu +2
Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks exploit this limitation by splitting…
When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents
Yechao Zhang, Shiqian Zhao, Jiawen Zhang +5
Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution. Thi…
MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models
Yingzi Ma, Zhengyue Zhao, Xiaogeng Liu +3
Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autor…
LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails
Nanxi Li, Zhengyue Zhao, Chaowei Xiao
Guardrails are a critical safety layer for modern AI systems, but their operating regime is changing. As LLMs are deployed as customized assistants, safety policies are increasingl…
AutoDAN-Reasoning: Enhancing Strategies Exploration based Jailbreak Attacks with Test-Time Scaling
Xiaogeng Liu, Chaowei Xiao
Recent advancements in jailbreaking large language models (LLMs), such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery. AutoDAN-Turbo employs a lifelo…
Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent
Weidi Luo, Qiming Zhang, Tianyu Lu +9
Command-line interface (CLI) agents powered by large language models (LLMs) can interpret natural-language requests, plan multi-step tasks, execute shell commands, and modify files…
DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents
Hao Li, Xiaogeng Liu, Hung-Chun Chiu +3
Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities. By interacting with external environments through…
OET: Optimization-based prompt injection Evaluation Toolkit
Jinsheng Pan, Xiaogeng Liu, Chaowei Xiao
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, enabling their widespread adoption across various domains.…
Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models
Weidi Luo, Tianyu Lu, Qiming Zhang +8
Recent advances in multi-modal large reasoning models (MLRMs) have shown significant ability to interpret complex visual content. While these models enable impressive reasoning cap…
RePD: Defending Jailbreak Attack through a Retrieval-based Prompt Decomposition Process
Peiran Wang, Xiaogeng Liu, Chaowei Xiao
In this study, we introduce RePD, an innovative attack Retrieval-based Prompt Decomposition framework designed to mitigate the risk of jailbreak attacks on large language models (L…
System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective
Fangzhou Wu, Ethan Cecchetti, Chaowei Xiao
Large Language Model-based systems (LLM systems) are information and query processing systems that use LLMs to plan operations from natural-language prompts and feed the output of…
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
Xiaogeng Liu, Peiran Li, Edward Suh +7
In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that can automatically discover as many jailbreak strategies as possible from scratch, without any human inter…
JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
Weidi Luo, Siyuan Ma, Xiaogeng Liu +2
With the rapid advancements in Multimodal Large Language Models (MLLMs), securing these models against malicious inputs while aligning them with human values has emerged as a criti…
Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
Zhiyuan Yu, Xiaogeng Liu, Shunning Liang +3
Recent advancements in generative AI have enabled ubiquitous access to large language models (LLMs). Empowered by their exceptional capabilities to understand and generate human-li…
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
Fangzhou Wu, Ning Zhang, Somesh Jha +2
Large Language Model (LLM) systems are inherently compositional, with individual LLM serving as the core foundation with additional layers of objects such as plugins, sandbox, and…
WIPI: A New Web Threat for LLM-Driven Web Agents
Fangzhou Wu, Shutong Wu, Yulong Cao +1
With the fast development of large language models (LLMs), LLM-driven Web Agents (Web Agents for short) have obtained tons of attention due to their superior capability where LLMs…
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
Fangzhou Wu, Xiaogeng Liu, Chaowei Xiao
With the advancement of Large Language Models (LLMs), significant progress has been made in code generation, enabling LLMs to transform natural language into programming code. Thes…
Exploring the Limits of ChatGPT in Software Security Applications
Fangzhou Wu, Qingzhao Zhang, Ati Priya Bajaj +4
Large language models (LLMs) have undergone rapid evolution and achieved remarkable results in recent times. OpenAI's ChatGPT, backed by GPT-3.5 or GPT-4, has gained instant popula…