Saved in:
| Main Authors: | Kawasaki, Amelia, Davis, Andrew, Abbas, Houssam |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.03230 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024)
by: Wang, Jinbo, et al.
Published: (2024)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)
by: Wang, Yujing, et al.
Published: (2024)
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022)
by: Qiu, Pengyu, et al.
Published: (2022)
SPML: A DSL for Defending Language Models Against Prompt Attacks
by: Sharma, Reshabh K, et al.
Published: (2024)
by: Sharma, Reshabh K, et al.
Published: (2024)
Defending Against Indirect Prompt Injection Attacks With Spotlighting
by: Hines, Keegan, et al.
Published: (2024)
by: Hines, Keegan, et al.
Published: (2024)
MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
by: Li, Jiacheng, et al.
Published: (2023)
by: Li, Jiacheng, et al.
Published: (2023)
A Systematic Review of Poisoning Attacks Against Large Language Models
by: Fendley, Neil, et al.
Published: (2025)
by: Fendley, Neil, et al.
Published: (2025)
GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending Against Prompt Injection Attacks
by: Li, Rongchang, et al.
Published: (2024)
by: Li, Rongchang, et al.
Published: (2024)
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
by: Lowy, Andrew, et al.
Published: (2024)
by: Lowy, Andrew, et al.
Published: (2024)
Composite Backdoor Attacks Against Large Language Models
by: Huang, Hai, et al.
Published: (2023)
by: Huang, Hai, et al.
Published: (2023)
MEA-Defender: A Robust Watermark against Model Extraction Attack
by: Lv, Peizhuo, et al.
Published: (2024)
by: Lv, Peizhuo, et al.
Published: (2024)
From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks
by: Zhang, Zhexin, et al.
Published: (2024)
by: Zhang, Zhexin, et al.
Published: (2024)
SecAlign: Defending Against Prompt Injection with Preference Optimization
by: Chen, Sizhe, et al.
Published: (2024)
by: Chen, Sizhe, et al.
Published: (2024)
Lessons from Defending Gemini Against Indirect Prompt Injections
by: Shi, Chongyang, et al.
Published: (2025)
by: Shi, Chongyang, et al.
Published: (2025)
FL-Defender: Combating Targeted Attacks in Federated Learning
by: Jebreel, Najeeb, et al.
Published: (2022)
by: Jebreel, Najeeb, et al.
Published: (2022)
Defending against Backdoor Attack on Deep Neural Networks
by: Cheng, Hao, et al.
Published: (2020)
by: Cheng, Hao, et al.
Published: (2020)
Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)
by: Wang, Xiaoyang, et al.
Published: (2022)
Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models
by: Guo, Qiming, et al.
Published: (2025)
by: Guo, Qiming, et al.
Published: (2025)
SmoothGuard: Defending Multimodal Large Language Models with Noise Perturbation and Clustering Aggregation
by: Su, Guangzhi, et al.
Published: (2025)
by: Su, Guangzhi, et al.
Published: (2025)
Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
by: Halloran, John T., et al.
Published: (2026)
by: Halloran, John T., et al.
Published: (2026)
Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
by: Wang, Xiangwen, et al.
Published: (2026)
by: Wang, Xiangwen, et al.
Published: (2026)
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
by: Cao, Bochuan, et al.
Published: (2023)
by: Cao, Bochuan, et al.
Published: (2023)
Temporal Context Awareness: A Defense Framework Against Multi-turn Manipulation Attacks on Large Language Models
by: Kulkarni, Prashant, et al.
Published: (2025)
by: Kulkarni, Prashant, et al.
Published: (2025)
Defending Against Poisoning Attacks in Federated Learning with Blockchain
by: Dong, Nanqing, et al.
Published: (2023)
by: Dong, Nanqing, et al.
Published: (2023)
PubDef: Defending Against Transfer Attacks From Public Models
by: Sitawarin, Chawin, et al.
Published: (2023)
by: Sitawarin, Chawin, et al.
Published: (2023)
CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
by: Mu, Fangwen, et al.
Published: (2024)
by: Mu, Fangwen, et al.
Published: (2024)
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)
by: Tan, Qi, et al.
Published: (2024)
SOS! Soft Prompt Attack Against Open-Source Large Language Models
by: Yang, Ziqing, et al.
Published: (2024)
by: Yang, Ziqing, et al.
Published: (2024)
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
by: Xue, Lulu, et al.
Published: (2024)
by: Xue, Lulu, et al.
Published: (2024)
LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attacks
by: Costa, Joana C., et al.
Published: (2025)
by: Costa, Joana C., et al.
Published: (2025)
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)
by: Liu, Yige, et al.
Published: (2026)
Evaluating Apple Intelligence's Writing Tools for Privacy Against Large Language Model-Based Inference Attacks: Insights from Early Datasets
by: Soumik, Mohd. Farhan Israk, et al.
Published: (2025)
by: Soumik, Mohd. Farhan Israk, et al.
Published: (2025)
BadMerging: Backdoor Attacks Against Model Merging
by: Zhang, Jinghuai, et al.
Published: (2024)
by: Zhang, Jinghuai, et al.
Published: (2024)
Defending Against Unforeseen Failure Modes with Latent Adversarial Training
by: Casper, Stephen, et al.
Published: (2024)
by: Casper, Stephen, et al.
Published: (2024)
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization
by: Trillos, Nicolás García, et al.
Published: (2024)
by: Trillos, Nicolás García, et al.
Published: (2024)
Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)
by: Ward, Joshua, et al.
Published: (2025)
Prompt Stealing Attacks Against Text-to-Image Generation Models
by: Shen, Xinyue, et al.
Published: (2023)
by: Shen, Xinyue, et al.
Published: (2023)
Improved Membership Inference Attacks Against Language Classification Models
by: Shachor, Shlomit, et al.
Published: (2023)
by: Shachor, Shlomit, et al.
Published: (2023)
Jailbreak Attacks and Defenses Against Large Language Models: A Survey
by: Yi, Sibo, et al.
Published: (2024)
by: Yi, Sibo, et al.
Published: (2024)
Your Agent Can Defend Itself against Backdoor Attacks
by: Changjiang, Li, et al.
Published: (2025)
by: Changjiang, Li, et al.
Published: (2025)
Similar Items
-
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024) -
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024) -
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022) -
SPML: A DSL for Defending Language Models Against Prompt Attacks
by: Sharma, Reshabh K, et al.
Published: (2024) -
Defending Against Indirect Prompt Injection Attacks With Spotlighting
by: Hines, Keegan, et al.
Published: (2024)