:: Library Catalog

Buchumschlag

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Afane, Mohamed, Satyam, Abhishek, Chen, Ke, Li, Tao, Farooq, Junaid, Chen, Juntao
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Cryptography and Security Computation and Language
Online-Zugang:	https://arxiv.org/abs/2512.10998
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Next-Generation Phishing: How LLM Agents Empower Cyber Attackers
von: Afane, Khalifa, et al.
Veröffentlicht: (2024)

Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
von: Zhao, Shuai, et al.
Veröffentlicht: (2024)

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
von: Paracha, Anum, et al.
Veröffentlicht: (2025)

Denial-of-Service Poisoning Attacks against Large Language Models
von: Gao, Kuofeng, et al.
Veröffentlicht: (2024)

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

Data Defenses Against Large Language Models
von: Agnew, William, et al.
Veröffentlicht: (2024)

Privacy in Large Language Models: Attacks, Defenses and Future Directions
von: Li, Haoran, et al.
Veröffentlicht: (2023)

Window-based Membership Inference Attacks Against Fine-tuned Large Language Models
von: Chen, Yuetian, et al.
Veröffentlicht: (2026)

DINA: A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing
von: Chuang, Ko-Wei, et al.
Veröffentlicht: (2025)

FlexLLM: Exploring LLM Customization for Moving Target Defense on Black-Box LLMs Against Jailbreak Attacks
von: Chen, Bocheng, et al.
Veröffentlicht: (2024)

Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
von: Ye, Rui, et al.
Veröffentlicht: (2024)

$PD^3F$: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models
von: Zhang, Yuanhe, et al.
Veröffentlicht: (2025)

From Theory to Practice: Evaluating Data Poisoning Attacks and Defenses in In-Context Learning on Social Media Health Discourse
von: Jhuma, Rabeya Amin, et al.
Veröffentlicht: (2025)

Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems
von: Qu, Yubin, et al.
Veröffentlicht: (2026)

Prompt Stealing Attacks Against Large Language Models
von: Sha, Zeyang, et al.
Veröffentlicht: (2024)

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

RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation
von: Zhao, Tianzhe, et al.
Veröffentlicht: (2025)

Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
von: Brown, Hannah, et al.
Veröffentlicht: (2024)

DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack
von: Li, Hao, et al.
Veröffentlicht: (2025)

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
von: Chowdhury, Arijit Ghosh, et al.
Veröffentlicht: (2024)

Cross-Task Defense: Instruction-Tuning LLMs for Content Safety
von: Fu, Yu, et al.
Veröffentlicht: (2024)

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
von: Fu, Tingchen, et al.
Veröffentlicht: (2024)

LoRA-Leak: Membership Inference Attacks Against LoRA Fine-tuned Language Models
von: Ran, Delong, et al.
Veröffentlicht: (2025)

Enhance Robustness of Language Models Against Variation Attack through Graph Integration
von: Xiong, Zi, et al.
Veröffentlicht: (2024)

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
von: Fu, Wenjie, et al.
Veröffentlicht: (2023)

Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks
von: Chen, Yiyi, et al.
Veröffentlicht: (2024)

Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services
von: Li, Yansong, et al.
Veröffentlicht: (2023)

JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
von: Feng, Yingchaojie, et al.
Veröffentlicht: (2024)

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs
von: Zhao, Shuai, et al.
Veröffentlicht: (2025)

EvoDefense: Co-Evolving Black-Box Defense with Large Language Models
von: Li, Yu, et al.
Veröffentlicht: (2026)

Stop Tracking Me! Proactive Defense Against Attribute Inference Attack in LLMs
von: Yan, Dong, et al.
Veröffentlicht: (2026)

Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement
von: Kim, Heegyu, et al.
Veröffentlicht: (2024)

May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks
von: Pandya, Nishit V., et al.
Veröffentlicht: (2025)

Securing Large Language Models (LLMs) from Prompt Injection Attacks
von: Suri, Omar Farooq Khan, et al.
Veröffentlicht: (2025)

Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs
von: Liu, Fan, et al.
Veröffentlicht: (2024)

Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution
von: Zhang, Xiaozhe, et al.
Veröffentlicht: (2026)

FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
von: Wang, Jiongxiao, et al.
Veröffentlicht: (2024)

RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models
von: Wang, Jiongxiao, et al.
Veröffentlicht: (2023)

Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
von: He, Haorui, et al.
Veröffentlicht: (2025)

CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning
von: Yi, Biao, et al.
Veröffentlicht: (2025)