AI & LLM security testing.
Your AI feature takes instructions from anyone who can type, and from any document it reads. We test whether it can be tricked into leaking data, ignoring its rules or taking actions it shouldn't.
The ways AI features get abused.
Mapped to the OWASP Top 10 for LLM Applications, plus the classic web and API flaws that AI features often reintroduce.
Prompt injection
Direct jailbreaks, and indirect injection hidden in web pages, emails, files or tickets the model reads.
Data & prompt leakage
Extraction of system prompts, secrets, other users' conversations or documents outside the user's permissions.
Agent & tool abuse
Over-permissioned tools, MCP servers and API calls the model can be talked into making for an attacker.
RAG security
Cross-tenant retrieval, missing document-level access control, and poisoning of the knowledge base.
Output handling
Model output that reaches browsers, shells, SQL or downstream APIs unsanitised: XSS, SSRF and injection.
Abuse & cost
Rate limits, denial-of-wallet, authentication on AI endpoints, and logs good enough to investigate misuse.
Proof, not opinions.
Threat model
- Data flows and trust boundaries
- Every tool and permission the model has
- Where untrusted content gets in
Adversarial testing
- Reproducible prompts and payloads
- Testing of the APIs behind the feature
- Risk-ranked findings
Fixes that hold
- Guardrail, permission and architecture fixes
- Optional retest after fixes
- Updated report to share
6 AI platforms tested.
Our profiles include AI assistants, an AI and analytics platform, AI workflow tools and an enterprise AI application suite, tested for access control, session handling and AI-related controls.
Enterprise AI suite (P14)
11–20 confirmed findings across user, admin and API surfaces. Most were closed or fixed after our retest.
AI business platform (P02)
Every critical and high finding was confirmed fixed at retest.
AI operations platform (P07)
Tested every year, with every finding fixed at the final retest.
All profilesCommon questions.
Do you need access to our model or training data?
No. We test the deployed feature the way users and attackers reach it. System prompts, tool definitions and architecture docs make the test faster and deeper, but aren't required.
We use OpenAI, Anthropic or Gemini. Isn't the provider responsible?
The provider secures the model. You own everything around it: what data it can reach, what tools it can call, who can talk to it and what happens to its output. That's where most real-world AI incidents happen.
Can you also help us build it securely?
Yes. See secure AI engineering for design reviews and builds of RAG and agent systems.
Get secured.
Tell us what you're shipping. You'll get a scoped, fixed quote within one business day.