AI Security
We test the layer most security firms still don't know how to attack. From prompt injection to model extraction, from RAG poisoning to autonomous agent abuse.
Most enterprises are deploying AI systems faster than they can secure them. The attack surface is new, the tooling is immature, and traditional security firms lack the expertise to test it properly.
Systematic adversarial testing of large language models deployed in production, covering jailbreaks, output manipulation, and data leakage vectors.
Full-scope attack simulation against AI-powered systems. We emulate real-world threat actors targeting your models, pipelines, and inference endpoints.
We test retrieval-augmented generation stacks for poisoning, context window manipulation, and indirect prompt injection through ingested documents.
Autonomous agents introduce tool-use risks, privilege escalation paths, and chain-of-thought exploitation. We map every abuse scenario before production.
Direct and indirect prompt injection remains the most exploited AI vulnerability. We validate every input surface your model is exposed to.
We assess model hosting configurations, API exposure, weight access controls, and serialization risks across your ML infrastructure.
Third-party models, datasets, and frameworks carry hidden risks. We audit your AI supply chain from Hugging Face repos to production containers.
We map your AI systems against the EU AI Act risk classifications and help you build the technical documentation the regulation requires.
A customer-facing chatbot for a financial services firm used a system prompt to enforce content restrictions.
We bypassed the content filter entirely using indirect prompt injection embedded in user-supplied input, forcing the model to reveal internal instructions and produce policy-violating outputs. The filter was redesigned with input sanitisation and output validation layers.
A legal tech company's AI assistant ingested documents from external sources into its knowledge base without validation.
We injected adversarial instructions into a document that, once ingested, caused the model to override its retrieval behaviour and leak confidential case summaries to unprivileged users. The fix required isolating ingestion pipelines and adding semantic anomaly detection.
An internal AI assistant had been granted access to internal APIs, a database query tool, and an email-sending capability.
Through a sequence of crafted prompts, we caused the agent to chain its tool calls — querying the employee database and exfiltrating results via the email tool to an external address. Tool permission scoping and confirmation gates were implemented.
A company had deployed a proprietary fine-tuned model behind an API, with the model weights representing significant IP.
Using systematic query strategies, we reconstructed a functional replica of the model's decision boundaries with fewer than 50,000 queries, well within free-tier API limits. Rate limiting, query fingerprinting, and output perturbation were introduced as countermeasures.
Our AI Risk Assessment Methodology follows five structured phases designed for repeatable, evidence-based AI security testing.
AI security assessment is relevant to any organisation deploying models in production — whether you built the AI or bought it.
If your product or internal tooling is powered by a large language model, it has an attack surface that traditional penetration testing won't cover. We test it end to end.
Retrieval-augmented generation introduces data ingestion, context injection, and retrieval trust issues that require specialist assessment methodology.
AI agents that can browse the web, query databases, send emails, or call external APIs carry compounded risk. We map every tool-abuse and privilege-escalation path before they reach production.
High-risk AI systems under the EU AI Act require documented security testing. Our assessments produce the technical evidence your compliance programme needs.
