ML02
Data Poisoning Attack
Poisoning training data to compromise ML models
315 papers Browse all papers
Monthly publications
Paper types
defense 169
attack 107
survey 18
benchmark 17
tool 4
Domains
federated-learning 141
nlp 113
vision 97
multimodal 23
graph 19
tabular 18
generative 15
reinforcement-learning 15
timeseries 7
audio 4
Co-occurring categories
Other OWASP categories that appear on the same papers
ML10 Model Poisoning
71 LLM03 Training Data Poisoning
42 LLM01 Prompt Injection
32 ML01 Input Manipulation Attack
27 ML03 Model Inversion Attack
23 ML09 Output Integrity Attack
9 LLM06 Sensitive Information Disclosure
7 ML07 Transfer Learning Attack
6 ML06 AI Supply Chain Attacks
5 LLM08 Excessive Agency
5 ML04 Membership Inference Attack
4 ML08 Model Skewing
2 LLM07 Insecure Plugin Design
1 ML05 Model Theft
1Top cited papers
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Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
2025 attack
A Survey of Secure Semantic Communications
2025 survey
Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs
2025 attack
TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
2025 defense
Secure Retrieval-Augmented Generation against Poisoning Attacks
2025 defense
FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs
2025 defense
Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
2025 defense
MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
2025 attack
SoK: Systematic analysis of adversarial threats against deep learning approaches for autonomous anomaly detection systems in SDN-IoT networks
2025 survey
Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
2025 defense