Search Results for author:
Found 21 papers, 8 papers with code
Holmes: Automated Fact Check with Large Language Models
The rise of Internet connectivity has accelerated the spread of disinformation, threatening societal trust, decision-making, and national security.
ai.txt: A Domain-Specific Language for Guiding AI Interactions with the Internet
We introduce ai. txt, a novel domain-specific language (DSL) designed to explicitly regulate interactions between AI models, agents, and web content, addressing critical limitations of the widely adopted robots. txt standard.
A Rusty Link in the AI Supply Chain: Detecting Evil Configurations in Model Repositories
Recent advancements in large language models (LLMs) have spurred the development of diverse AI applications from code generation and video editing to text generation; however, AI supply chains such as Hugging Face, which host pretrained models and their associated configuration files contributed by the public, face significant security challenges; in particular, configuration files originally intended to set up models by specifying parameters and initial settings can be exploited to execute unauthorized code, yet research has largely overlooked their security compared to that of the models themselves; in this work, we present the first comprehensive study of malicious configurations on Hugging Face, identifying three attack scenarios (file, website, and repository operations) that expose inherent risks; to address these threats, we introduce CONFIGSCAN, an LLM-based tool that analyzes configuration files in the context of their associated runtime code and critical libraries, effectively detecting suspicious elements with low false positive rates and high accuracy; our extensive evaluation uncovers thousands of suspicious repositories and configuration files, underscoring the urgent need for enhanced security validation in AI model hosting platforms.
Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning
We propose a novel defense strategy, Safety Chain-of-Thought (SCoT), which harnesses the enhanced \textit{reasoning capabilities} of LLMs for proactive assessment of harmful inputs, rather than simply blocking them.
Indiana Jones: There Are Always Some Useful Ancient Relics
This paper introduces Indiana Jones, an innovative approach to jailbreaking Large Language Models (LLMs) by leveraging inter-model dialogues and keyword-driven prompts.
Safe + Safe = Unsafe? Exploring How Safe Images Can Be Exploited to Jailbreak Large Vision-Language Models
Recent advances in Large Vision-Language Models (LVLMs) have showcased strong reasoning abilities across multiple modalities, achieving significant breakthroughs in various real-world applications.
Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment
The recent advancements in large language models (LLMs) and pre-trained vision models have accelerated the development of vision-language large models (VLLMs), enhancing the interaction between visual and linguistic modalities.
GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models
By offering a realistic assessment and tailored reduction of gender biases, we hope that our GenderCARE can represent a significant step towards achieving fairness and equity in LLMs.
Efficient Detection of Toxic Prompts in Large Language Models
ToxicDetector achieves high accuracy, efficiency, and scalability, making it a practical method for toxic prompt detection in LLMs.
Image-Based Geolocation Using Large Vision-Language Models
Furthermore, our study highlights issues related to dataset integrity, leading to the creation of a more robust dataset and a refined framework that leverages LVLMs' cognitive capabilities to improve geolocation precision.
Continuous Embedding Attacks via Clipped Inputs in Jailbreaking Large Language Models
Security concerns for large language models (LLMs) have recently escalated, focusing on thwarting jailbreaking attempts in discrete prompts.
Source Code Summarization in the Era of Large Language Models
We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.
Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation
Large language models (LLMs) have transformed the field of natural language processing, but they remain susceptible to jailbreaking attacks that exploit their capabilities to generate unintended and potentially harmful content.
Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection
We present categorizations of the identified glitch tokens and symptoms exhibited by LLMs when interacting with glitch tokens.
A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
Large Language Models (LLMS) have increasingly become central to generating content with potential societal impacts.
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
With the prevalence of text-to-image generative models, their safety becomes a critical concern.
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
In this paper, we introduce a detailed framework designed to detect and assess the presence of content from potentially copyrighted books within the training datasets of LLMs.
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers
To improve the accessibility of ASR systems for stutterers, we need to expose and analyze the failures of ASR systems on stuttering speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Prompt Injection attack against LLM-integrated Applications
We deploy HouYi on 36 actual LLM-integrated applications and discern 31 applications susceptible to prompt injection.
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and (3) the resilience of ChatGPT against these jailbreak prompts.
Automatic Code Summarization via ChatGPT: How Far Are We?
To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet.
