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Found 10 papers, 6 papers with code
JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
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 critical challenge.
Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
Building on the insights from the user study, we also developed a system using AI as the assistant to automate the process of jailbreak prompt generation.
AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting
However, with the integration of additional modalities, MLLMs are exposed to new vulnerabilities, rendering them prone to structured-based jailbreak attacks, where semantic content (e. g., "harmful text") has been injected into the images to mislead MLLMs.
Automatic and Universal Prompt Injection Attacks against Large Language Models
Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions.
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
In this paper, we introduce DeceptPrompt, a novel algorithm that can generate adversarial natural language instructions that drive the Code LLMs to generate functionality correct code with vulnerabilities.
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
In light of these challenges, we intend to answer this question: Can we develop an approach that can automatically generate stealthy jailbreak prompts?
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training
Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability.
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Deep neural networks are proven to be vulnerable to backdoor attacks.
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models.
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks.
