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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 1–50 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
Data Shifts Hurt CoT: A Theoretical Study—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—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
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents—0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift—0
A Geometric Approach to Problems in Optimization and Data Science—0
Investigating cybersecurity incidents using large language models in latest-generation wireless networks—0
ControlNET: A Firewall for RAG-based LLM System—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
Data Poisoning in Deep Learning: A SurveyCode1
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Optimizing ML Training with Metagradient Descent—0
Policy Teaching via Data Poisoning in Learning from Human Preferences—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models—0
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models—0
Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data—0
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
No, of course I can! Refusal Mechanisms Can Be Exploited Using Harmless Fine-Tuning Data—0
Atlas: A Framework for ML Lifecycle Provenance & Transparency—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoningCode0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL—0
Multi-Faceted Studies on Data Poisoning can Advance LLM DevelopmentCode0
A Robust Attack: Displacement Backdoor Attack—0
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