Service

LLM / GenAI Security

Ship generative AI without shipping new attack surface.

LLM / GenAI Security assesses your generative AI deployments across the full stack, from the model interface and system prompts to retrieval pipelines, plugins, and output handling. We benchmark against the OWASP Top 10 for LLM applications and the threats unique to your use case, so you can move fast without leaving gaps behind.

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Coverage

What We Test

The specific areas we assess in this practice.

Model Interfaces

Prompt injection, jailbreaks, insecure output handling, and excessive agency across your LLM endpoints.

RAG Pipelines

Retrieval poisoning, context leakage, and access-control gaps in retrieval-augmented generation.

Plugins & Tools

Security review of tool-calling, plugins, and integrations that extend what your model can do.

Guardrails & Output

Validation of input/output filtering, content controls, and data-loss prevention around the model.

Methodology

How the engagement runs

A disciplined, repeatable process, so findings are reproducible and fixes are verified.

01
Scope
Map your GenAI architecture, data sources, and trust boundaries.
02
Test
Assess interfaces, RAG, plugins, and guardrails against the OWASP LLM Top 10.
03
Report
Deliver prioritised, reproducible findings with remediation guidance.
04
Remediate
Support fixes and re-test to confirm the attack surface is closed.