Search Results for author:
Found 13 papers, 8 papers with code
Virtual Array for Dual Function MIMO Radar Communication Systems using OTFS Waveforms
The transmit antennas can use the Doppler-delay (DD) domain bins in a shared fashion.
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models
Vision-language pre-training (VLP) models, trained on large-scale image-text pairs, have become widely used across a variety of downstream vision-and-language (V+L) tasks.
GlitchProber: Advancing Effective Detection and Mitigation of Glitch Tokens in Large Language Models
In this work, we aim to enhance the understanding of glitch tokens and propose techniques for their detection and mitigation.
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
In this work, we implement NeuSemSlice, a novel framework that introduces the semantic slicing technique to effectively identify critical neuron-level semantic components in DNN models for semantic-aware model maintenance tasks.
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.
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.
Large Language Models for Cyber Security: A Systematic Literature Review
Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.
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.
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years.
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.
Large Language Models for Software Engineering: A Systematic Literature Review
Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages.
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.
