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. The provider is responsible for its service layer, while your organization remains responsible for how the model is integrated and used. System prompts, retrieval sources, tool permissions, output handling, and access controls can introduce distinct risks that require application-specific review.
Often no. Many engagements can use black-box or grey-box access to the deployed application. If an organization trains or fine-tunes models and wants that pipeline in scope, the review can be extended to agreed data-provenance, poisoning-resistance, and supply-chain questions.
The application layer remains important. AI Security adds model- and integration-specific questions such as prompt injection, tool misuse, retrieval boundaries, and unsafe output handling. It can be scoped alongside web application testing when both layers require evidence.
Some tests can change state, so the rules of engagement define permitted techniques, environments, data, stop conditions, and recovery steps. State-changing tests should use a non-production environment or a scoped, reversible dataset unless a different approach is explicitly authorized.
Findings can include prioritized remediation guidance and a technical debrief. Deeper work on architecture and guardrail design can be scoped as Security Architecture.
Book a free, no-obligation consultation with our team.