LLM08
Vector and Embedding Weaknesses Illustration

BONUS TECH DECODER

Vector: A mathematical description of information that AI can understand, like giving directions using only numbers.
Embedding: Converting words or images into number patterns an AI can work with, like translating books into a secret code.
Vector Database: A specialized library where AI stores information as number patterns, helping it quickly find similar things.

🧠 WHAT IS IT?

Vector and Embedding Weaknesses involve vulnerabilities in how AI systems store and retrieve information using mathematical representations (vectors). Think of it like a library where someone has placed misleading or harmful books on shelves next to legitimate resources. When the AI searches for information, it might pull these harmful elements alongside good information, leading to contaminated results or manipulated responses.

🔍 HOW IT HAPPENS

  • Attackers inject malicious or misleading content into the vector database used by an AI system
  • The embedding process fails to distinguish between legitimate and harmful content
  • When users make queries, the AI retrieves information based on similarity in the vector space
  • Similar but harmful content gets retrieved alongside legitimate information, poisoning results

🚨 WHY IT MATTERS

Confidentiality C
Integrity I
These weaknesses can lead to data poisoning, manipulated AI responses, and potential data leakage as the AI might be tricked into retrieving and revealing sensitive information it shouldn't access.

🛡️ HOW TO PREVENT IT

  • Implement robust validation when adding new content to vector databases
  • Create detection systems to identify anomalous or potentially malicious embeddings
  • Use secure generation and storage practices for vectors and embeddings
  • Regularly audit and clean vector databases to remove potentially harmful content
8/10