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Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 1–25 of 492 papers

TitleStatusHype
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks—0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning—0
Multi-Trigger Poisoning Amplifies Backdoor Vulnerabilities in LLMs—0
Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual LearningCode0
Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training—0
Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning—0
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
Data Shifts Hurt CoT: A Theoretical Study—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Securing Traffic Sign Recognition Systems in Autonomous Vehicles—0
VLMs Can Aggregate Scattered Training PatchesCode1
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Security Concerns for Large Language Models: A Survey—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
A Linear Approach to Data Poisoning—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?Code0
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
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