NuvynAI is an independent AI security practice. We test the layer standard security reviews miss: prompt injection, jailbreaks, PII leakage, and the failure modes that only appear under adversarial pressure.
Every assessment runs on tooling built in-house: 31 deterministic detection patterns across 10 threat categories, each match returning a named pattern, a severity, a CWE reference, and a remediation string.
A hands-on adversarial teardown of your LLM surface, no cost and no obligation. Run manually against a public demo, a sandbox, or a system prompt you share, whichever you are comfortable with. You get a written summary of what broke, how, and what to do about it, with findings referenced to the OWASP LLM Top 10 where they apply.
30 minutes. We map your LLM stack, identify your highest-risk surfaces — inference endpoints, RAG pipelines, agent integrations — and scope the engagement precisely. No sales pitch. Technical from minute one.
Manual adversarial testing against the surface you share. Prompt injection, jailbreaks, data extraction and PII leakage, probed by hand rather than by a scanner. A 31-pattern detection framework supports the work, covering 3 of the OWASP LLM Top 10 (LLM01, LLM02, LLM10), but the findings come from the testing.
Severity-rated findings, proof-of-concept exploits for each vulnerability, and a prioritised remediation roadmap. A document you can share with your board, enterprise clients, or legal team. No vague recommendations.
Optional hands-on implementation of fixes. Verified re-testing confirms each finding is resolved. You leave with a clean posture document, not just a to-do list.
A sample of prompt-injection patterns the engine flags, shown with its real severity output. Full adversarial testing is scoped per engagement.
Your security team tests OWASP Top 10. Your AI team knows the model. Neither knows adversarial LLM behaviour at scale. That gap is where prompt injection, PII context bleeds, and jailbreaks land. That's exactly what we test.
The detection tooling is mine. I built it, I adversarially tested it, and I know exactly where it fails, which is why the testing is manual and hands-on rather than a scanner run. That is where an assessment starts, not where it ends.
Enterprise clients run due diligence on your AI stack. Investors ask about AI risk. A clean written security report isn't just a technical artefact — it's a sales asset that removes blockers and shortens deal cycles.
A security layer designed to sit between your users and your LLM. Every match returns a named pattern, a severity, a CWE reference, and a remediation string.
AI workflow automation with security built in from the start — not bolted on after an incident. Purpose-built for teams running agents across internal systems where a single compromised step cascades downstream.
The governance layer for engineering teams shipping with AI-assisted development. C4's security gates are modelled on Claude Code's native hook architecture — PreToolUse intercepts before execution, exit code 2 blocks the operation entirely. Enforcement at the tool-call level, before anything reaches your codebase.
Start with a free Tier 0 teardown: a hands-on manual adversarial teardown of one LLM-facing surface, run against a public demo, a sandbox, or a system prompt you share, whichever you are comfortable with. It starts with 30 minutes to agree scope and consent; the testing is run by hand afterwards, and you get a written summary within five working days. No cost, no obligation, and nothing is touched without your consent.
Prefer email? nuvyn@nuvynai.com · Book directly: cal.com/nuvyn