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Mirawyn

Secure your AI

LLM Application Security

Design and code review for applications built on large language models, from the system prompt to the vector store.

Why it matters

Most LLM vulnerabilities are design decisions: model output trusted as if it were code, retrieval that ignores user permissions, secrets placed in prompts. They are cheapest to fix before launch.

Mapped to

  • OWASP Top 10 for LLM Applications
  • OWASP ASVS
  • NIST SSDF

What we cover

  • Threat modelling for RAG, chat and copilot architectures
  • Output handling: preventing model output from reaching shells, SQL, HTML or APIs unchecked
  • Retrieval access control and vector store permissions
  • System prompt design, secrets handling and prompt leakage
  • Guardrail and content-filter design that fits your risk, not a generic list
  • Secure-by-default patterns and reviews in your CI pipeline

How an engagement runs

  1. 01

    Understand

    Review the architecture, data flows and how the model is wired into your product.

  2. 02

    Review

    Inspect code, prompts, retrieval and integration points for exploitable weaknesses.

  3. 03

    Fix together

    Pair with your engineers on remediations and reusable patterns.

  4. 04

    Embed

    Add checks and guidance so the next feature ships secure by default.

What you receive

Clear outputs your engineers, leadership and auditors can each use.

  • Threat model with trust boundaries and abuse cases
  • Code and configuration findings with fixes
  • Reference architecture and secure design patterns for your stack
  • Developer checklist for future LLM features

Open-source first

We start with proven open-source tools to keep costs down, and recommend commercial options only when they add real value. How we choose

  • Semgrep CE
  • promptfoo
  • LLM Guard

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Ready to talk about LLM Application Security?

Tell us what you are building. We will come back with a short, honest view of where the risk is and what it would take to test it.

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