This research demonstrates that textual embeddings are vulnerable to inversion attacks that can reconstruct sensitive information with remarkably little training data.

• Novel few-shot attack method that requires significantly less training data than previous approaches
• Combines alignment techniques with generative capabilities of Large Language Models
• Exposes security vulnerabilities in vector databases and embedding-based systems
• Highlights urgent need for stronger privacy-preserving measures in text embedding systems

This work is critical for security professionals as it reveals how easily malicious actors could extract sensitive information from supposedly secure embedding representations used in many modern AI systems.

ALGEN: Few-shot Inversion Attacks on Textual Embeddings using Alignment and Generation