How LLMs Leak Training Data
Models memorize fragments word-for-word and hand them back to anyone who asks the right way.
Models memorize fragments word-for-word and hand them back to anyone who asks the right way.
Sensitive data now flows into AI tools constantly—and legacy security tools can't stop it.
Three attack pathways expose customer data in AI chatbots, each requiring separate defenses.
Logging AI prompts captures personal data companies didn't mean to store.
Attackers exploit tool-calling to steal data through the exact channels your AI agent needs.
RAG systems treat retrieved data as instructions, turning documents into attack vectors.
Platforms claiming framework alignment vary widely in what that actually means.
LLM tool calls create data exfiltration that looks like legitimate operation.
System prompts leak easily, and what's inside matters more than secrecy itself.
Enterprises are deploying RAG faster than they can secure it.
Enterprise RAG systems leak proprietary data through unprotected vector databases.
The system, not the user, is where LLM security risk actually lives.