The rapid adoption of large language models in enterprise workflows has outpaced the security industry’s ability to understand, test, and mitigate the risks they introduce. Unlike traditional software vulnerabilities, LLM attack surfaces are probabilistic, context-dependent, and often invisible to conventional scanning tools.
Prompt Injection: The Primary Vector
Prompt injection attacks allow adversaries to embed malicious instructions within user-supplied input, redirecting model behaviour in ways the integrating application never intended. In enterprise environments where LLMs have access to internal tools, APIs, or databases, the blast radius of a successful injection is significant.
The core problem is that current LLMs cannot reliably distinguish between instructions from the application developer and instructions embedded in untrusted data. Until that separation is solved at the architecture level, every LLM integration must be treated as an untrusted endpoint.
Data Exfiltration Through Model Output
When LLMs are given access to sensitive internal data for retrieval-augmented generation, the model’s output becomes a potential exfiltration channel. Without careful output filtering and context boundaries, a crafted query can cause the model to reproduce sensitive information in its responses.
What a Secure Integration Looks Like
Securing an LLM integration requires treating the model as an untrusted third-party service. Input sanitisation, output validation, strict permission scoping for any tools or APIs the model can access, and comprehensive audit logging are non-negotiable. The goal is to constrain what the model can see and what it can do — not to trust that it will behave correctly.
At RDB Tech, we conduct structured LLM security assessments that cover the full integration surface: from system prompt hardening to tool permission auditing to adversarial input testing. Contact us to discuss your specific deployment.