New capabilities, new attack surface

LLM applications fail in ways traditional security testing doesn’t catch: prompt injection turns your assistant against you, retrieval pipelines leak documents across trust boundaries, and fine-tuned models memorize the secrets in their training data. The OWASP LLM Top 10 exists because these failures are now routine — and most were found in production.

Agentic systems raise the stakes further. When a model can call tools, browse, execute code, and act on a user’s behalf, a single injected instruction can become data exfiltration or unauthorized action. The permission boundaries around your agents matter as much as the model itself.

Testing AI systems end to end

LLM application assessment

Adversarial testing of your AI-powered products: prompt injection (direct and indirect), jailbreaks, output handling, and the classic web vulnerabilities that AI features reintroduce. We test against the OWASP LLM Top 10 and beyond, then rank findings by what an attacker actually gains — not theoretical severity.

Data leakage & privacy review

Can your model be made to reveal training data, system prompts, other users’ context, or retrieved documents it shouldn’t? We find out before your users do. Assessments cover RAG pipelines, fine-tuned models, and the trust boundaries between tenants, sessions, and retrieval sources.

Agentic red-teaming

Full-chain attacks against autonomous workflows: tool abuse, permission escalation, cross-agent injection, and the blast radius when an agent is compromised. We map the tools, credentials, and permissions your agents hold, then chain realistic attacks to show how far one injected instruction can travel.

AI bill of materials & supply chain

An inventory of everything that shapes your model’s behavior — models, prompts, tools, retrieval sources, memory, agent topology, and the credentials behind each — with third-party model and dataset provenance verified as we go. Delivered as a CycloneDX ML-BOM, extended where the standard falls short.

What you get

AI bill of materials (AI-BOM) — every model, prompt, tool, data source, agent and credential that shapes what your system does, delivered as a CycloneDX ML-BOM

AI threat model — attack surface mapping specific to your architecture, not a generic checklist

Red-team findings report — reproducible attack chains with severity ranked by real-world impact

Guardrail & mitigation guidance — concrete controls — input handling, output filtering, permission design — matched to your stack

Safe deployment checklist — what to verify before your next model, feature, or agent ships

Artificial Intelligence Security