LLMSecOps: Securing Large Language Models

The AI Threat Landscape (OWASP Top 10)

Security is no longer just about firewalls; it's about semantic firewalls. The OWASP Top 10 for LLMs highlights new attack vectors like 'Prompt Injection' (tricking the model into ignoring its instructions) and 'Insecure Output Handling' (executing malicious code generated by the LLM).

A particular risk for SMEs is 'Data Leakage'. If your RAG pipeline accidentally indexes sensitive HR documents, a simple question like 'What is the CEO's salary?' could expose confidential data without any traditional hacking involved.

Defense in Depth Architecture

• Level 1 (Gateway): Rate limiting and PI redacting before the prompt enters your VPC.
• Level 2 (Guardrails): Semantic analysis using tools like NVIDIA NeMo or Guardrails AI to block toxic inputs.
• Level 3 (Model): Fine-tuning models on 'refusal examples' to harden them against jailbreaks.
• Level 4 (Audit): Continuous Red Teaming to proactively find vulnerabilities before attackers do.

Why Red Teaming?

Automated scanners miss context-specific flaws. Red Teaming involves human experts (and adversarial agents) trying to break your application. They simulate real-world attacks to validate that your guardrails actually hold up under pressure.