Security Testing for the AI Systems You're Shipping
Every AI feature you ship is a new attack surface. We red-team your models and AI-integrated applications the same way attackers will — and help you govern AI risk before regulators ask you to.
Where AI introduces risk, we test it
LLM / GenAI Application Security Testing
End-to-end testing of chatbots, copilots and AI agents built on top of foundation models, covering the OWASP Top 10 for LLM Applications.
Prompt Injection & Jailbreak Testing
Direct and indirect prompt injection, jailbreak, and guardrail-bypass testing against your production prompts and system instructions.
AI Model Red Teaming
Adversarial testing of model behaviour, output manipulation, data leakage and unsafe content generation.
RAG Pipeline & Vector Database Security
Testing retrieval-augmented generation pipelines for data poisoning, unauthorised retrieval, and embedding-based leakage.
AI Supply Chain & Training Data Risk Review
Assessment of third-party model, plugin and dataset risk across your AI supply chain.
AI Governance & Risk Assessment
Risk assessments and control design aligned to ISO/IEC 42001 (AI management systems) and the NIST AI Risk Management Framework.
MLSecOps Integration
Embedding security testing and monitoring into your ML/LLMOps pipeline, not bolted on after deployment.
How the engagement runs
Scope
A short call to align on assets, timelines and compliance targets.
Test
Manual, expert-led testing augmented by AI-assisted analysis.
Report
Business-risk-ranked findings with reproducible proof-of-concept.
Retest
One included retest cycle plus an attestation letter.
Common questions
It builds on it. We still test the underlying application, APIs and infrastructure — but add AI-specific attack classes like prompt injection, model extraction, training-data leakage and unsafe output generation that traditional VAPT doesn't cover.
Both. Most of our engagements test how your application integrates with a third-party model provider — the risk usually lives in your prompts, guardrails, and data flow, not the base model itself.
OWASP Top 10 for LLM Applications for technical testing, and ISO/IEC 42001 or the NIST AI Risk Management Framework for governance and risk assessment work, depending on your regulatory context.
Yes, architecture-level AI threat modeling before development is one of the highest-leverage engagements we offer — it's far cheaper to design out a risk than to retrofit a fix.
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