Cover Image

The Resurgence of GCG Adversarial Attacks on Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tan, Yuting, Li, Xuying, Li, Zhuo, Shu, Huizhen, Hu, Peikang
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models
by: Li, Xiao, et al.
Published: (2024)

Checkpoint-GCG: Auditing and Attacking Fine-Tuning-Based Prompt Injection Defenses
by: Yang, Xiaoxue, et al.
Published: (2025)

Mask-GCG: Are All Tokens in Adversarial Suffixes Necessary for Jailbreak Attacks?
by: Mu, Junjie, et al.
Published: (2025)

GCG Attack On A Diffusion LLM
by: Neyroud, Ruben, et al.
Published: (2025)

Jailbreak Attacks and Defenses Against Large Language Models: A Survey
by: Yi, Sibo, et al.
Published: (2024)

Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation
by: Huang, Tiansheng, et al.
Published: (2025)

Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes
by: Hu, Xiaomeng, et al.
Published: (2024)

LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models
by: Lin, Shi, et al.
Published: (2024)

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
by: Liu, Fuqiang, et al.
Published: (2024)

On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks
by: Liu, Zesen, et al.
Published: (2024)

Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
by: Shahariar, G M, et al.
Published: (2024)

Adversarial Text Purification: A Large Language Model Approach for Defense
by: Moraffah, Raha, et al.
Published: (2024)

PAL: Proxy-Guided Black-Box Attack on Large Language Models
by: Sitawarin, Chawin, et al.
Published: (2024)

Adversarial Attacks on Large Language Models Using Regularized Relaxation
by: Chacko, Samuel Jacob, et al.
Published: (2024)

RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models
by: Chugh, Rishit
Published: (2026)

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
by: Reddy, Aashray, et al.
Published: (2025)

Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
by: Chang, Hongyan, et al.
Published: (2024)

Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models
by: Bai, Yang, et al.
Published: (2024)

On Adversarial Robustness of Language Models in Transfer Learning
by: Turbal, Bohdan, et al.
Published: (2024)

Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
by: Yuan, Hongbang, et al.
Published: (2024)

Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
by: Li, Jianwei, et al.
Published: (2023)

Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization
by: Tang, Haochun, et al.
Published: (2026)

REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
by: Liang, Buyun, et al.
Published: (2026)

SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
by: Li, Lijun, et al.
Published: (2024)

OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
by: Xu, Xiaoyu, et al.
Published: (2025)

Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models via Automated Adversarial Prompting
by: To, Bang Trinh Tran, et al.
Published: (2025)

Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models
by: Liu, Xiao, et al.
Published: (2024)

SVIP: Towards Verifiable Inference of Open-source Large Language Models
by: Sun, Yifan, et al.
Published: (2024)

On the Role of Attention Heads in Large Language Model Safety
by: Zhou, Zhenhong, et al.
Published: (2024)

Detecting Training Data of Large Language Models via Expectation Maximization
by: Kim, Gyuwan, et al.
Published: (2024)

CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion
by: Ren, Qibing, et al.
Published: (2024)

FIT to Forget: Robust Continual Unlearning for Large Language Models
by: Xu, Xiaoyu, et al.
Published: (2026)

Toward a Safer Web: Multilingual Multi-Agent LLMs for Mitigating Adversarial Misinformation Attacks
by: Aldahoul, Nouar, et al.
Published: (2025)

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
by: Hua, Peichun, et al.
Published: (2025)

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
by: Liang, Zi, et al.
Published: (2025)

Probing the Robustness of Large Language Models Safety to Latent Perturbations
by: Gu, Tianle, et al.
Published: (2025)

Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
by: Lu, Ning, et al.
Published: (2023)

RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
by: Yuan, Zhuowen, et al.
Published: (2024)

Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking
by: Xu, Yijie, et al.
Published: (2025)

Instructional Fingerprinting of Large Language Models
by: Xu, Jiashu, et al.
Published: (2024)