AI/ML Red Teaming & LLM Penetration Testing
Your AI features are a new attack surface. We test LLM applications, AI agents, RAG pipelines and machine learning models for prompt injection, data leakage, jailbreaks and abuse, before your customers or attackers find them.
The Evolving Threat Landscape
Why AI Systems Need Offensive Security Testing
Large language models follow instructions found in their input, so untrusted content can steer them. AI agents with tool access can be tricked into leaking data or taking actions, and RAG systems can expose documents across users and tenants. Scanners don’t find these issues. They need manual, adversarial testing by people who understand both offensive security and how models behave. Our AI red teaming is led by certified testers and mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Trusted by
Global Enterprises
Benefits of AI Red Teaming
AI red teaming shows how your models, prompts, agents and data pipelines behave when someone actively tries to misuse them, so you can ship AI features with confidence.
Our AI security testing follows the OWASP Top 10 for LLM Applications, MITRE ATLAS, the NIST AI Risk Management Framework and ISO/IEC 42001, and supports evidence for the robustness and cybersecurity requirements of the EU AI Act.
Find Prompt Injection Before Attackers Do
Prevent Sensitive Data Leakage
Secure AI Agents & Tool Use
Protect Models & Training Data
Support EU AI Act Readiness
Launch AI Features With Confidence
AI Red Teaming Approaches Based on Access and Risk
We tailor every AI engagement to how your system is built and what it can reach, from a public chatbot to an autonomous agent connected to internal tools.
Black Box AI Testing
Grey Box AI Testing
White Box AI Review
What We Test & How We Approach It
We cover the full AI stack: the application around the model, the agents and tools it can use, the data it retrieves and the models themselves. Every finding comes with reproducible prompts or payloads, a risk rating and clear remediation guidance. Related services: application penetration testing, cloud penetration testing and red teaming.
We test customer-facing and internal LLM apps for direct and indirect prompt injection, jailbreaks, system prompt extraction, insecure output handling (XSS and SSRF via model output) and abuse of business logic.
We assess agents and their tools, plug-ins and MCP servers for excessive agency, unsafe actions, privilege escalation and data exfiltration through tool calls.
We test retrieval-augmented generation for cross-tenant data access, sensitive data disclosure, document and embedding poisoning, and weak access controls on knowledge sources.
We evaluate models and the infrastructure around them for adversarial examples, model extraction, membership inference, training data poisoning and insecure inference APIs, model registries and notebooks.
