LLM & AI System Security Audit
We perform technical security assessments of self-hosted and private LLM environments — from the model and its infrastructure to prompts, RAG pipelines, and AI agents — against real AI-specific attacks like prompt injection, data extraction, and tool misuse.
Business First
Code Next
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New technology, new attack surface
Traditional security tools weren’t built for AI systems. LLM deployments face attack classes of their own — prompt injection (the #1 risk in the OWASP Top 10 for LLM Applications), sensitive data disclosure, system prompt leakage, poisoning of RAG knowledge bases, and “excessive agency” when AI agents can execute actions.
If your LLM connects to internal data or tools, these aren’t theoretical risks.
What we test
Model & application security
Jailbreaks, prompt injection (direct and indirect), system prompt leakage, unsafe output handling.
RAG & data security
Can the model be tricked into revealing documents, credentials, or other users’ data? Is the knowledge base protected from poisoning?
Agent & integration security
Tool-calling permissions, MCP/plugin integrations, abuse of connected systems.
Infrastructure & access
Hosting environment hardening, API security, authentication, secrets, logging and monitoring.
Governance alignment
Findings mapped to OWASP LLM Top 10
What you get
- Technical findings with reproducible attack evidence
- Risk assessment in business terms
- Hardening recommendations for the full AI stack
- Retesting after fixes
- Compliance-ready documentation for AI governance frameworks.
Who it’s for
Private and self-hosted LLMs
For companies operating private or self-hosted LLM environments.
AI product companies
For teams building products and services powered by large language models.
LLMs in internal workflows
For enterprises embedding LLMs, RAG systems, or AI agents into internal processes.
FAQ
We are working with both types.
The assessment can cover private, self-hosted, and cloud-hosted LLM environments, subject to the agreed scope and the testing permissions allowed by the relevant platform or provider.
Yes. A pre-launch assessment can evaluate the application, model interactions, prompts, RAG pipelines, agents, integrations, access controls, infrastructure, and relevant AI-specific attack paths before production release.
A standard penetration test focuses primarily on applications, APIs, infrastructure, authentication, authorization, and traditional attack paths. An LLM security audit includes those areas where relevant and adds AI-specific testing such as prompt injection, sensitive data disclosure, system prompt leakage, RAG poisoning, unsafe tool use, and excessive agent permissions.