Adversarial testing of machine learning systems, LLM applications, and AI-integrated products — mapped to the OWASP Top 10 for LLM Applications, MITRE ATLAS, and the NIST AI Risk Management Framework.
Get a Scoped Quote Our ApproachAI security testing examines the systems you have built on top of machine learning — chat assistants, retrieval-augmented applications, autonomous agents, classifiers, and the pipelines that train and serve them. We test the model, the application wrapped around it, and the trust boundaries between them.
That means treating the model as an untrusted component: what can an attacker make it say, reveal, or do, and what happens downstream when it produces something malicious?
Why It MattersShipping an LLM feature quietly grants a probabilistic component access to your data, your tools, and sometimes your customers. Traditional application testing does not cover prompt injection, insecure handling of model output, over-permissive tool use, or training-data exposure — and conventional scanners do not detect any of them.
The failure mode that matters is rarely the model saying something embarrassing. It is the model being used as a confused deputy to reach systems the attacker could not reach directly.
Structured against the OWASP Top 10 for LLM Applications and MITRE ATLAS, and reportable against the NIST AI Risk Management Framework. Manual adversarial work, not a scanner run — automated probes establish a baseline, but the findings that matter come from a human reasoning about your specific system.
Map models, data flows, tools, and trust boundaries.
Identify what an attacker would want the model to do.
Injection, jailbreaks, tool abuse, and data extraction.
Reproducible findings ranked by business impact.
Walk your ML and engineering teams through every result.
Your system's attack surface, trust boundaries, and abuse cases.
Every issue with the exact prompts and steps to reproduce it.
Coverage mapped to the LLM Top 10 and MITRE ATLAS techniques.
Where your filters and system prompts hold — and where they do not.
Prioritized fixes across model, application, and architecture.
Verification of remediated critical findings.
Yes — usually more than teams expect. The provider secures the model; you are responsible for everything around it. Your system prompt, retrieval sources, tool permissions, output handling, and access controls are all yours, and that is where most real findings live.
No. Most engagements are black-box or grey-box against your deployed application. If you train or fine-tune your own models and want the pipeline in scope, we can extend to data provenance, poisoning resistance, and supply chain — just raise it during scoping.
The application layer is tested the same way, and we cover it. What is added is the adversarial-ML layer: prompt injection, guardrail bypass, tool abuse, and data extraction. Many clients scope AI Security alongside a web application test so both layers are covered in one engagement.
No. Testing is read-oriented and agreed in advance under signed rules of engagement. Where a test could alter state — poisoning a retrieval index, for example — we run it against a non-production environment or a scoped, reversible dataset with your written approval.
Yes. Findings come with a prioritized remediation roadmap, and the debrief walks your engineers through each result. For deeper work on architecture and guardrail design, that falls under Security Architecture.
Book a free, no-obligation consultation with our team.