Cover Image

Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Anwar, Usman, Von Oswald, Johannes, Kirsch, Louis, Krueger, David, Frei, Spencer
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning to Forget using Hypernetworks
by: Rangel, Jose Miguel Lara, et al.
Published: (2024)

Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
by: Takahashi, Tsubasa, et al.
Published: (2025)

One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
by: Zhang, Jiacheng, et al.
Published: (2025)

Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
by: Echeberria-Barrio, Xabier, et al.
Published: (2024)

Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
by: Eykholt, Kevin, et al.
Published: (2024)

Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory
by: Fu, Shaopeng, et al.
Published: (2026)

Hijacking Large Language Models via Adversarial In-Context Learning
by: Zhou, Xiangyu, et al.
Published: (2023)

Defending Jailbreak Prompts via In-Context Adversarial Game
by: Zhou, Yujun, et al.
Published: (2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees
by: Däubener, Sina, et al.
Published: (2024)

Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channels
by: Spielman, Jesse, et al.
Published: (2025)

Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation
by: Chanda, Dibaloke, et al.
Published: (2023)

Evaluating Tabular Representation Learning for Network Intrusion Detection
by: Butt, Muhammad Usman, et al.
Published: (2026)

Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
by: Angioni, Daniele, et al.
Published: (2024)

TA3: Testing Against Adversarial Attacks on Machine Learning Models
by: Jin, Yuanzhe, et al.
Published: (2024)

Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics
by: Weinberg, Abraham Itzhak, et al.
Published: (2024)

Machine Learning Models Have a Supply Chain Problem
by: Meiklejohn, Sarah, et al.
Published: (2025)

In-Context Learning Can Re-learn Forbidden Tasks
by: Xhonneux, Sophie, et al.
Published: (2024)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)

HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
by: Khan, Momin Ahmad, et al.
Published: (2024)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)

Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
by: Hrdonka, Lukáš, et al.
Published: (2026)

AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
by: Zhang, Sen, et al.
Published: (2025)

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows
by: Lin, Jie, et al.
Published: (2025)

Adversarial Contrastive Learning for LLM Quantization Attacks
by: Song, Dinghong, et al.
Published: (2026)

Temporal Analysis of Adversarial Attacks in Federated Learning
by: Mapakshi, Rohit, et al.
Published: (2025)

Trading Inference-Time Compute for Adversarial Robustness
by: Zaremba, Wojciech, et al.
Published: (2025)

Adversarial Suffix Filtering: a Defense Pipeline for LLMs
by: Khachaturov, David, et al.
Published: (2025)

CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers
by: Chen, Zhi, et al.
Published: (2026)

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs
by: Wheeler, Andrew, et al.
Published: (2026)

Adversarial training with restricted data manipulation
by: Benfield, David, et al.
Published: (2025)

Adversarial Inception Backdoor Attacks against Reinforcement Learning
by: Rathbun, Ethan, et al.
Published: (2024)

A Geometric Framework for Adversarial Vulnerability in Machine Learning
by: Bell, Brian
Published: (2024)

Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting
by: Chichifoi, Karina, et al.
Published: (2025)

A Comparison of Adversarial Learning Techniques for Malware Detection
by: Louthánová, Pavla, et al.
Published: (2023)

Poisoning the Watchtower: Prompt Injection Attacks Against LLM-Augmented Security Operations Through Adversarial Log Content
by: Pandey, Rohan, et al.
Published: (2026)

Efficient Optimization Algorithms for Linear Adversarial Training
by: RIbeiro, Antônio H., et al.
Published: (2024)

UTrace: Poisoning Forensics for Private Collaborative Learning
by: Rose, Evan, et al.
Published: (2024)

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
by: Schneider, Thomas, et al.
Published: (2024)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)