Tell us what AI you've shipped. A senior engineer, not a salesperson, replies with scope, timeline, and price.
Fixed price after a 15-minute call · Try before you buy · Your details are never shared
Not a model scan. Proven AI attacks and fixes your engineers can act on.
A redacted sample web-application report — the finding write-ups, proof-of-concept evidence and remediation your team would receive. No form required.
Download the sample report (PDF) →Enough for a small web application and its API, free retest included. Need more coverage? Buy more credits and spend them across web, API, mobile, cloud and network as priorities change; unused credits roll over quarterly. Continuous PTaaS subscriptions are quoted on scope.
Working to a fixed number? Tell us what it is. We scope a timeboxed test to fit it and agree what gets tested first, so you get the most coverage your budget allows — in writing, before testing starts.
Get my scoping callAttacker-led testing of your LLM apps and agents, mapped to the standards auditors cite — plus validation that your AI controls hold.
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A fixed process with dates you can plan around.
Everything here is verifiable on a third-party site or in your own report.
AI penetration testing is an authorized attack on your AI and LLM systems — applications, agents, RAG pipelines and the models behind them. Engineers test for prompt injection, jailbreaks, tool and agent abuse, insecure output handling, and model and data leakage, and prove each finding with a working example.
Engagements start at $6,400 — 40 credits at $160 each — covering a focused LLM application test with a free retest. Agentic systems and multi-model pipelines use more credits, and unused credits roll over quarterly. Every quote is a fixed price in writing after a 15-minute scoping call.
LLM-powered applications and chatbots, autonomous and multi-agent systems, RAG and vector-search pipelines, model APIs and fine-tuned models, and the integrations and tools your agents can call.
The OWASP Top 10 for LLM Applications, OWASP Agentic security guidance, MITRE ATLAS, and the NIST AI Risk Management Framework (AI RMF) — so your findings line up with what auditors and enterprise customers reference.
Separate from testing an AI app, control validation checks whether your AI-use policy actually holds — for example, whether an employee can get customer data into a public AI tool. We test every path (paste, upload, browser, API) with uniquely-marked synthetic data, so your real data is never touched.
Yes. We test with synthetic, uniquely-marked records — never your real PII, PHI, secrets or source code. Your data never leaves your environment or reaches ours.
A focused LLM application test takes 1–3 weeks including reporting; agentic systems and multi-model pipelines take longer. Free retesting confirms your fixes.
Tell us what AI you've shipped — apps, agents or pipelines — and we'll scope a test mapped to the standards that matter. No commitment, no sales call before the engineer call.
