47 citations · 262 across the 38 of their papers we have counts for
40 papers
VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring
Hanjiang Hu, Yiyuan Pan, Jiaxing Li +5
As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors…
Stateful Online Monitoring Catches Distributed Agent Attacks
Davis Brown, Samarth Bhargav, Arav Santhanam +7
Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection, attackers frequently distribu…
Contextual Safety Reasoning and Grounding for Open-World Robots
Zachary Ravichandran, David Snyder, Alexander Robey +3
Robots are increasingly operating in open-world environments where safe behavior depends on context: the same hallway may require different navigation strategies when crowded versu…
Jailbreaking in the Haystack
Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena +3
Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety…
Preventing Robotic Jailbreaking via Multimodal Domain Adaptation
Francesco Marchiori, Rohan Sinha, Christopher Agia +4
Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to jailbreaking attacks that bypass safety me…
Algorithms for Adversarially Robust Deep Learning
Alexander Robey
Given the widespread use of deep learning models in safety-critical applications, ensuring that the decisions of such models are robust against adversarial exploitation is of funda…
Embodied AI: Emerging Risks and Opportunities for Policy Action
Jared Perlo, Alexander Robey, Fazl Barez +2
The field of embodied AI (EAI) is rapidly advancing. Unlike virtual AI, EAI systems can exist in, learn from, reason about, and act in the physical world. With recent advances in A…
Adversarial Attacks on Robotic Vision Language Action Models
Eliot Krzysztof Jones, Alexander Robey, Andy Zou +5
The emergence of vision-language-action models (VLAs) for end-to-end control is reshaping the field of robotics by enabling the fusion of multimodal sensory inputs at the billion-p…
Benchmarking Misuse Mitigation Against Covert Adversaries
Davis Brown, Mahdi Sabbaghi, Luze Sun +4
Existing language model safety evaluations focus on overt attacks and low-stakes tasks. In reality, an attacker can easily subvert existing safeguards by requesting help on small,…
Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang, Ahmed Elgohary, Xiawei Wang +5
Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a no…
Transferable Adversarial Attacks on Black-Box Vision-Language Models
Kai Hu, Weichen Yu, Li Zhang +5
Vision Large Language Models (VLLMs) are increasingly deployed to offer advanced capabilities on inputs comprising both text and images. While prior research has shown that adversa…
Safety Guardrails for LLM-Enabled Robots
Zachary Ravichandran, Alexander Robey, Vijay Kumar +2
Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from av…
Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks
Hanjiang Hu, Alexander Robey, Changliu Liu
Large language models (LLMs) are shown to be vulnerable to jailbreaking attacks where adversarial prompts are designed to elicit harmful responses. While existing defenses effectiv…
Jailbreaking LLM-Controlled Robots
Alexander Robey, Zachary Ravichandran, Vijay Kumar +2
The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains a…
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Patrick Chao, Edoardo Debenedetti, Alexander Robey +9
Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, w…
A Safe Harbor for AI Evaluation and Red Teaming
Shayne Longpre, Sayash Kapoor, Kevin Klyman +20
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by promi…
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Yutong He, Alexander Robey, Naoki Murata +7
Prompt engineering is an effective but labor-intensive way to control text-to-image (T2I) generative models. Its time-intensive nature and complexity have spurred the development o…
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey +5
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While…
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…
Data-Driven Modeling and Verification of Perception-Based Autonomous Systems
Thomas Waite, Alexander Robey, Hassani Hamed +2
This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical…
Jailbreaking Black Box Large Language Models in Twenty Queries
Patrick Chao, Alexander Robey, Edgar Dobriban +3
There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, whic…
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
Alexander Robey, Eric Wong, Hamed Hassani +1
Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adv…
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Alexander Robey, Fabian Latorre, George J. Pappas +2
One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are t…
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh +4
Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To ach…