AI Penetration Testing New
AI systems introduce a new attack surface — one where a convincing sentence can be an exploit. We test large language model applications, machine-learning pipelines and AI-enabled platforms the way an adversary would: creatively, persistently and from the outside in.
Why AI needs its own kind of testing
An LLM-backed application can be perfectly secure at the infrastructure layer and still leak data, execute instructions or manipulate users through its own interface. Traditional testing barely touches these failure modes: prompts are inputs, responses are behaviour, and the attack surface is written in natural language.
Our AI penetration testing combines application security craft with adversarial ML techniques — aligned to the OWASP Top 10 for LLM Applications — to find what breaks when your model meets someone hostile.
OWASP LLM Top 10LLM application testing
Hands-on attack testing of LLM-backed features: chat assistants, summarisation, generation pipelines and anything that lets users talk to your model.
- Prompt injection and jailbreak resistance
- Sensitive data disclosure and training-data leakage
- Output handling: what the model's text can trigger downstream
- System-prompt and instruction confidentiality
Beyond chatAgentic & tool-integrated systems
Agents that browse, call APIs and take actions multiply the stakes: a manipulated step becomes an executed action. We test the boundaries between model, tools and permissions.
- Excessive agency and over-privileged tool use
- Human-in-the-loop bypass scenarios
- Injection via retrieved content and untrusted sources
- Chained attacks across multi-step workflows
UpstreamML pipeline & model security
The model is only as trustworthy as the pipeline behind it. We review how data flows in, how models are versioned and served, and what an attacker can reach.
- Training and inference data integrity
- Model-serving infrastructure exposure
- Supply-chain review of models, frameworks and plugins
- Access control on ML operations and endpoints
AdversarialAI red teaming
Objective-driven exercises against your AI-enabled product: realistic, multi-vector attacks testing what the system will actually do under pressure.
- Abuse and misuse scenario development
- Multi-turn social-engineering against AI features
- Safety-control bypass testing
- Findings framed as product and engineering actions
Need AI Penetration Testing expertise? Let's talk.
Tell us about your environment and goals. We'll come back with a recommended approach — no obligation, no sales pitch.