Saved in:
| Main Authors: | Jia, Hengrui, Wyllie, Sierra, Sediq, Akram Bin, Ibrahim, Ahmed, Papernot, Nicolas |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00170 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)
by: Thudi, Anvith, et al.
Published: (2023)
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
by: Zhang, Han, et al.
Published: (2024)
by: Zhang, Han, et al.
Published: (2024)
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024)
by: Langford, Harry, et al.
Published: (2024)
Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023)
by: Boenisch, Franziska, et al.
Published: (2023)
Fast Exact Unlearning for In-Context Learning Data for LLMs
by: Muresanu, Andrei I., et al.
Published: (2024)
by: Muresanu, Andrei I., et al.
Published: (2024)
LLM Dataset Inference: Did you train on my dataset?
by: Maini, Pratyush, et al.
Published: (2024)
by: Maini, Pratyush, et al.
Published: (2024)
The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation
by: Jia, Hengrui, et al.
Published: (2025)
by: Jia, Hengrui, et al.
Published: (2025)
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
by: Liu, Ken Ziyu, et al.
Published: (2025)
by: Liu, Ken Ziyu, et al.
Published: (2025)
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
by: Pichler, Georg, et al.
Published: (2024)
by: Pichler, Georg, et al.
Published: (2024)
PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection
by: Li, Wei, et al.
Published: (2024)
by: Li, Wei, et al.
Published: (2024)
Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation
by: Küchler, Nicolas, et al.
Published: (2025)
by: Küchler, Nicolas, et al.
Published: (2025)
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
by: Zhao, Mengnan, et al.
Published: (2026)
by: Zhao, Mengnan, et al.
Published: (2026)
Fast and Lightweight Backdoor Detection via Head Random Probing
by: Yu, Yinbo, et al.
Published: (2026)
by: Yu, Yinbo, et al.
Published: (2026)
Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems
by: Jean, Abile, et al.
Published: (2026)
by: Jean, Abile, et al.
Published: (2026)
Detecting and Eliminating Neural Network Backdoors Through Active Paths with Application to Intrusion Detection
by: Høyheim, Eirik, et al.
Published: (2026)
by: Høyheim, Eirik, et al.
Published: (2026)
TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
by: Nguyen, Quang Duc, et al.
Published: (2026)
by: Nguyen, Quang Duc, et al.
Published: (2026)
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
by: Rabanser, Stephan, et al.
Published: (2025)
by: Rabanser, Stephan, et al.
Published: (2025)
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
by: Jiang, Haoyu, et al.
Published: (2023)
by: Jiang, Haoyu, et al.
Published: (2023)
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
by: Pawlak, Stanisław, et al.
Published: (2025)
by: Pawlak, Stanisław, et al.
Published: (2025)
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
by: Vyas, Sanyam, et al.
Published: (2025)
by: Vyas, Sanyam, et al.
Published: (2025)
Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking
by: Kong, Cong, et al.
Published: (2024)
by: Kong, Cong, et al.
Published: (2024)
Backdoor Graph Condensation
by: Wu, Jiahao, et al.
Published: (2024)
by: Wu, Jiahao, et al.
Published: (2024)
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
by: Hossain, Khondoker Murad, et al.
Published: (2024)
by: Hossain, Khondoker Murad, et al.
Published: (2024)
Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models
by: Shin, Jeongjin, et al.
Published: (2024)
by: Shin, Jeongjin, et al.
Published: (2024)
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency
by: Pal, Soumyadeep, et al.
Published: (2024)
by: Pal, Soumyadeep, et al.
Published: (2024)
The Curse of Recursion: Training on Generated Data Makes Models Forget
by: Shumailov, Ilia, et al.
Published: (2023)
by: Shumailov, Ilia, et al.
Published: (2023)
How to Backdoor the Knowledge Distillation
by: Wu, Chen, et al.
Published: (2025)
by: Wu, Chen, et al.
Published: (2025)
Heterogeneous Graph Backdoor Attack
by: Chen, Jiawei, et al.
Published: (2025)
by: Chen, Jiawei, et al.
Published: (2025)
Backdoor defense, learnability and obfuscation
by: Christiano, Paul, et al.
Published: (2024)
by: Christiano, Paul, et al.
Published: (2024)
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models
by: Nie, Yuzhou., et al.
Published: (2024)
by: Nie, Yuzhou., et al.
Published: (2024)
Injecting Universal Jailbreak Backdoors into LLMs in Minutes
by: Chen, Zhuowei, et al.
Published: (2025)
by: Chen, Zhuowei, et al.
Published: (2025)
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
by: Li, Nan, et al.
Published: (2024)
by: Li, Nan, et al.
Published: (2024)
How to Craft Backdoors with Unlabeled Data Alone?
by: Wang, Yifei, et al.
Published: (2024)
by: Wang, Yifei, et al.
Published: (2024)
Compromising Embodied Agents with Contextual Backdoor Attacks
by: Liu, Aishan, et al.
Published: (2024)
by: Liu, Aishan, et al.
Published: (2024)
Magnitude-based Neuron Pruning for Backdoor Defens
by: Li, Nan, et al.
Published: (2024)
by: Li, Nan, et al.
Published: (2024)
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models
by: Li, Ding, et al.
Published: (2024)
by: Li, Ding, et al.
Published: (2024)
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
by: Ren, Zhiyao, et al.
Published: (2025)
by: Ren, Zhiyao, et al.
Published: (2025)
Pay Attention to the Triggers: Constructing Backdoors That Survive Distillation
by: De Muri, Giovanni, et al.
Published: (2025)
by: De Muri, Giovanni, et al.
Published: (2025)
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
by: Braun, Tobias, et al.
Published: (2025)
by: Braun, Tobias, et al.
Published: (2025)
PBP: Post-training Backdoor Purification for Malware Classifiers
by: Nguyen, Dung Thuy, et al.
Published: (2024)
by: Nguyen, Dung Thuy, et al.
Published: (2024)
Similar Items
-
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023) -
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
by: Zhang, Han, et al.
Published: (2024) -
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024) -
Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023) -
Fast Exact Unlearning for In-Context Learning Data for LLMs
by: Muresanu, Andrei I., et al.
Published: (2024)