LLM Security: The Attack Surface Your Enterprise Is Ignoring

Enterprise AI deployments have outpaced the security frameworks designed to protect them. Most organisations have robust controls for traditional application security - SQL injection, XSS, access control - but almost none have equivalent controls for the attack surface introduced by large language models.

Prompt injection is the most immediate and underestimated threat. When user input is concatenated into a system prompt, an attacker can override the intended behaviour of the AI, exfiltrate data from connected tools, or use the model as a pivot point into internal systems. Tests should check whether untrusted content can cause unauthorised tool actions or data disclosure, within an explicitly agreed assessment scope.

Data exfiltration via model output is the second major category. LLMs trained on or given access to sensitive data can be coerced into reproducing it through carefully crafted inputs. Organisations deploying RAG (Retrieval-Augmented Generation) architectures against internal knowledge bases need strict output filtering and access control at the retrieval layer - not just at the model boundary.

Model supply chain attacks are an emerging threat vector. Fine-tuned models downloaded from public repositories, third-party embeddings, and open-source pipelines can contain adversarial backdoors. We assess the full model provenance during AI security engagements - from training data sources to inference infrastructure.

The path forward isn't to avoid AI deployment - it's to deploy with adversarial thinking baked in from day one. Red team your AI systems before launch, not after the first incident. Implement LLM-specific monitoring that detects anomalous input patterns. Build AI governance policies that define acceptable use boundaries and enforcement mechanisms.