LLM Security: Threats & Production Controls (October 2026)

LLM security addresses risks unique to language-model systems, whose probabilistic behavior, inability to reliably distinguish instructions from data, immature model supply chains, and capacity for irreversible agent actions make conventional application-security controls insufficient. The material outlines the OWASP 2025 Top 10 LLM risks, including prompt injection, sensitive-data disclosure, supply-chain compromise, poisoning, unsafe output handling, excessive agency, system-prompt leakage, RAG weaknesses, misinformation, and resource abuse, emphasizing direct and indirect prompt injection, training-data poisoning, memorization, and model extraction as major threats. It recommends defense in depth across input screening, output validation, least-privilege tool permissions, RAG ingestion and retrieval controls, data-layer authorization, audit logs, red-teaming, predeployment evaluation, and continuous monitoring for drift and novel attacks. Agentic systems require particular safeguards because compromised outputs can trigger database changes, API calls, or other unauthorized actions before logging occurs. The discussion also connects these controls to EU AI Act obligations effective for high-risk financial-service uses in August 2026, as well as NIST AI RMF and ISO 42001 requirements, and presents Openlayer as a platform that actively blocks or escalates unsafe prompts, outputs, retrieved content, and tool calls while generating compliance evidence.

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