1 citations · 1 across the 12 of their papers we have counts for
13 papers
Building Trustworthy Household Robots through Open-World Safety
Yu-Lin Tsai, Muxi Lyu, Roei Herzig +3
Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in complex environments. These ad…
Twin Agent: Context Residual Compression for Privilege Separated Agents
Zhanhao Hu, Dennis Jacob, Xiao Huang +3
Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate downstream reasoning and tool use. Exist…
RECEIPT: Deterministic, Reward-Hacking-Resistant Verification for White-Box Agentic XSS Discovery
Muxi Lyu, Karen Shieh, Yiwei Hou +3
Cross-Site Scripting (XSS) remains one of the most prevalent and damaging classes of web vulnerabilities. LLM-based coding agents offer a promising approach to XSS discovery by com…
Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases
Yiwei Hou, Hao Wang, Muxi Lyu +6
Memory safety vulnerabilities remain a significant threat even for projects with extensive fuzzing and manual auditing. Recent results suggest that large language models hold great…
GradShield: Alignment Preserving Finetuning
Zhanhao Hu, Xiao Huang, Patrick Mendoza +4
Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implicitly harmful data. Even some…
Parser-Free Querying of Security Logs
Evan Luo, Julien Piet, David Wagner
Security analysts routinely query system logs to detect threats and investigate incidents, but each log source uses its own semi-structured format: logs are cheap to produce, but e…
Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents
Debeshee Das, Julien Piet, Darya Kaviani +3
Memory systems enable otherwise stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. The Trojan Hippo attack is a class of per…
Web Agents Should Adopt the Compile-and-Execute Paradigm
Julien Piet, Annabella Chow, Yiwei Hou +5
ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default for web agents. Instead, web a…
Trident: Improving Malware Detection with LLMs and Behavioral Features
Rebecca Saul, Jingzhi Jiang, Elliott Chia +1
Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to inc…
Preventing Prompt Injection with Type-Directed Privilege Separation
Dennis Jacob, Emad Alghamdi, Zhanhao Hu +2
Modern language models have enabled the development of agentic systems that achieve strong performance on reasoning-intensive tasks. Unfortunately, this has come with a security co…
JULI: Jailbreak Large Language Models by Self-Introspection
Jesson Wang, Zhanhao Hu, David Wagner
Large Language Models (LLMs) are trained with safety alignment to prevent generating malicious content. Although some attacks have highlighted vulnerabilities in these safety-align…
JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift
Julien Piet, Xiao Huang, Dennis Jacob +7
Safety and security remain critical concerns in AI deployment. Despite safety training through reinforcement learning with human feedback (RLHF) [ 32], language models remain vulne…
PromptShield: Deployable Detection for Prompt Injection Attacks
Dennis Jacob, Hend Alzahrani, Zhanhao Hu +2
Application designers have moved to integrate large language models (LLMs) into their products. However, many LLM-integrated applications are vulnerable to prompt injections. While…