# membership-inference
10 articlestagged with “membership-inference”
Model Extraction & Privacy Assessment
Test your advanced knowledge of model extraction, model stealing, membership inference, and intellectual property theft attacks against AI systems with 9 questions.
Membership Inference Defenses
Evaluating and implementing defenses against membership inference attacks that determine whether specific samples were in a model's training set.
Practical Membership Inference Attacks
Practical guide to conducting membership inference attacks against deployed language models.
Membership Inference Attacks
Techniques for determining whether specific data was used to train an AI model, including shadow model approaches, loss-based inference, LiRA, and practical implementation guidance.
Embedding Privacy
What embeddings reveal about source data — covering embedding inversion attacks, membership inference, attribute inference, privacy-preserving embedding techniques, and regulatory implications.
Embedding-Level Attacks
Overview of attacks targeting embeddings directly: adversarial embedding generation, inversion attacks for text reconstruction, and membership inference via embedding analysis.
Membership Inference via Embeddings
Determining if specific data was in an embedding model's training set through distance-based inference, statistical tests, and embedding behavior analysis.
Membership Inference Against Production LLMs
Implement membership inference attacks to determine whether specific data was used in training an LLM.
Extracting Training Data
Techniques for extracting memorized training data, system prompts, and private information from LLMs through targeted querying and membership inference attacks.
Security of Training Data Attribution Methods
Analysis of vulnerabilities in training data attribution techniques including influence functions, membership inference, and data provenance tracking, with implications for privacy and security.