Cover Image

Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Yugeng, Cong, Tianshuo, Zhao, Zhengyu, Backes, Michael, Shen, Yun, Zhang, Yang
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Watermarking LLM-Generated Datasets in Downstream Tasks
by: Liu, Yugeng, et al.
Published: (2025)

$\texttt{ModSCAN}$: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities
by: Jiang, Yukun, et al.
Published: (2024)

Composite Backdoor Attacks Against Large Language Models
by: Huang, Hai, et al.
Published: (2023)

Amplifying Machine Learning Attacks Through Strategic Compositions
by: Liu, Yugeng, et al.
Published: (2025)

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples
by: Han, Sicong, et al.
Published: (2025)

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
by: Shen, Xinyue, et al.
Published: (2023)

The Challenge of Identifying the Origin of Black-Box Large Language Models
by: Yang, Ziqing, et al.
Published: (2025)

Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models
by: Chen, Zeyuan, et al.
Published: (2026)

JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
by: Chu, Junjie, et al.
Published: (2024)

Peering Behind the Shield: Guardrail Identification in Large Language Models
by: Yang, Ziqing, et al.
Published: (2025)

IrisFP: Adversarial-Example-based Model Fingerprinting with Enhanced Uniqueness and Robustness
by: Geng, Ziye, et al.
Published: (2026)

Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency
by: Jiang, Yukun, et al.
Published: (2025)

On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
by: Wu, Yixin, et al.
Published: (2023)

Image-Perfect Imperfections: Safety, Bias, and Authenticity in the Shadow of Text-To-Image Model Evolution
by: Wu, Yixin, et al.
Published: (2024)

Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
by: Kozák, Matouš, et al.
Published: (2025)

When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
by: Shen, Xinyue, et al.
Published: (2025)

Adversarial Example Based Fingerprinting for Robust Copyright Protection in Split Learning
by: Lin, Zhangting, et al.
Published: (2025)

Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
by: Yang, Yulong, et al.
Published: (2023)

Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path
by: Ren, Yuchen, et al.
Published: (2024)

Adversarially Robust Assembly Language Model for Packed Executables Detection
by: Li, Shijia, et al.
Published: (2025)

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training
by: Zhang, Rui, et al.
Published: (2026)

Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
by: Cong, Tianshuo, et al.
Published: (2024)

A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations
by: Yang, Yulong, et al.
Published: (2024)

Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data
by: Song, Tianle, et al.
Published: (2025)

Understanding LLM Behavior When Encountering User-Supplied Harmful Content in Harmless Tasks
by: Chu, Junjie, et al.
Published: (2026)

Efficient Data-Free Model Stealing with Label Diversity
by: Liu, Yiyong, et al.
Published: (2024)

SoK: Understanding Vulnerabilities in the Large Language Model Supply Chain
by: Wang, Shenao, et al.
Published: (2025)

Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
by: Qu, Yiting, et al.
Published: (2025)

ExplainableGuard: Interpretable Adversarial Defense for Large Language Models Using Chain-of-Thought Reasoning
by: Guan, Shaowei, et al.
Published: (2025)

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples
by: Pu, Shi, et al.
Published: (2025)

Improving Sustainability of Adversarial Examples in Class-Incremental Learning
by: Liu, Taifeng, et al.
Published: (2025)

Masked Language Model Based Textual Adversarial Example Detection
by: Zhang, Xiaomei, et al.
Published: (2023)

Matrix Kloosterman Sums, Random Matrix Statistics, and Cryptography
by: Yang, Tianshuo
Published: (2026)

SOS! Soft Prompt Attack Against Open-Source Large Language Models
by: Yang, Ziqing, et al.
Published: (2024)

Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
by: Wen, Rui, et al.
Published: (2024)

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

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
by: Bostani, Hamid, et al.
Published: (2024)

Prompt Stealing Attacks Against Text-to-Image Generation Models
by: Shen, Xinyue, et al.
Published: (2023)

On the Adversarial Robustness of Instruction-Tuned Large Language Models for Code
by: Hossen, Md Imran, et al.
Published: (2024)

JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models
by: Ran, Delong, et al.
Published: (2024)