Model Inversion, Extraction, Poisoning

Model Inversion, Extraction, Poisoning

Model Inversion (Privacy): - Querying the model to reconstruct sensitive training data. - Example: Asking specifically crafted questions to force the model to leak PII (emails, SSNs) seen during training. - Mitigation: Differential Privacy during training to limit memorization of specific data points. Model Extraction (IP Theft): - Querying an API extensively to train a “Student Model” that mimics the proprietary “Teacher Model.” - Example: Using thousands of API calls to replicate a paid LLM service. - Mitigation: Rate limiting, monitoring unusual query patterns. Data Poisoning: - Manipulating the training data or fine-tuning data to introduce backdoors or bias. - Example: Injecting malicious code snippets into the training data of a coding assistant LLM. - Mitigation: Data validation, robust training techniques.