LLM Cybersecurity Overview
LLM (Large Language Model) cybersecurity protects AI systems designed to interact with natural language. Unlike traditional applications, which handle structured data, LLMs face unique vulnerabilities like prompt injection and training data poisoning, identified in the OWASP Top 10 for LLM Applications. Securing LLMs mandates specialized controls, continuous monitoring, and rigorous scrutiny of their outputs.
Security teams leverage LLMs to enhance threat intelligence, automate incident responses, and analyze security logs efficiently. However, these models can also be exploited; a compromised LLM risks revealing defense strategies to attackers. Effective security measures include regular model evaluations, controlled access, and comprehensive monitoring of input/output data.
The shift towards probabilistic responses challenges conventional security paradigms, necessitating advanced defenses and frequent testing against evolving threats. Frameworks like NIST AI Risk Management and MITRE ATLAS provide essential guidance. Organizations must approach LLM security as an ongoing process rather than a one-time setup to safeguard their critical data and operations.
