AI Penetration Testing Cost
You are not buying hours. You are buying chains.
An AI penetration test is priced on scope, not on a rate card. Here is how scope is actually measured, why AI testing costs more than a network pentest, and what a suspiciously cheap quote leaves out.
The cost of an AI penetration test is set by the size of the attack surface under test and the depth of the adversarial work: how many AI components are in scope, how deeply the testers are allowed to chain across them, and whether the deliverable is a finding list or reproducible attack chains with a retest.
This page is buyer guidance from an adversary practice, not a price list. Root Access Protection prices engagements and scopes each one to the environment. AI testing is the wedge we are loudest about right now. It is one engagement inside a full-spectrum practice: pentesting, red teaming, adversary emulation.
Four scope dimensions
What Moves the Price
Every honest quote is built from these four. If a vendor cannot tell you how each one shaped their number, their number is a guess.
Attack surface under test
One chatbot is a different engagement than a fleet of agents with tool access, an MCP server tier, and a RAG pipeline behind it. Each component is its own surface with its own test plan. Surface count is the single biggest cost lever.
Agentic depth
Testing the model is one thing. Testing what the agent is allowed to do with tools, memory, and permissions is where the real chains live, and it takes longer. Depth costs more because that is where the leverage is.
Protocol and integration coverage
MCP servers, agent-to-agent protocols, plugin and function-calling layers: each integration point is tested the way an attacker would fingerprint it. More protocols, more test surface, more cost.
Evidence and retest
A PDF of findings is cheap. Reproducible attack chains, hop by hop, plus a retest that proves the fixes held, is the actual product. Ask which one the quote covers. The price difference is the value difference.
What a Cheap AI Pentest Actually Buys
Not a smaller version of the real thing.
Automated scanning has a real job: cheap regression over known classes, run often. It does not think like an adversary, and it does not chain. A scanner quote and a pentest quote are buying different products with the same name.
Not model-only testing at pentest prices.
Prompt-injection spot checks against the chatbot have a real job: a fast read on the obvious. But the model is one component. If the quote never touches the tool tier, the MCP servers, or the permission grants, it tested the lobby and skipped the vault.
Not findings without chains.
Inventory has a real job: knowing what exists is the start of every program. But a list of isolated issues is inventory, and inventory is cheap to produce. The expensive part is proving the chain: how issue one becomes access two becomes the thing you actually care about. If the quote has no chaining, you are paying for the inventory and doing the analysis yourself.
Why it costs more than a network pentest
A network pentest tests a surface the industry has mapped for decades, with tooling to match. AI testing has no such map. Every engagement builds its own: the model, the harness around it, the tools the agent can call, the protocols between them. Testing the model without the harness measures the wrong thing, and the harness is different at every company.
That is the honest reason the quotes look bigger. It is also why the cheapest quote is usually the one that skipped the part that matters. Read how the work is actually done in how we pentest AI agents.
Scoped, not packaged
Engagement Shapes
No fixed packages. These are the shapes engagements take once scope is set. Yours gets priced on your surface, not on a tier.
For calibration: a typical AI pentest engagement runs $15–25K depending on the four dimensions above. That is the market's working range, not a quote. Your number gets built from your scope.
Focused assessment
One AI system, bounded scope: the model, its direct integrations, and the obvious chains. A first read on where you stand, with reproducible evidence for what we find.
Protocol-deep assessment
The agent toolchain as the target: MCP servers, tool poisoning, permission and trust boundaries, attack chains across the whole tier. This is where the leverage is.
Adversary emulation
Full-scope, objective-driven: an adversary campaign against your AI systems under written rules of engagement, with the methodology behind it on our methodology page.
Vendor qualification
Ask Before You Sign
Five questions that separate an adversary engagement from a scanner rental. Ask all five before you compare prices.
What is in scope, by component? The quote should name the systems, the tools, the protocols. If it says "AI pentest" without an inventory, there is no scope.
Do you test the tool tier or just the model? The model is the lobby. The agents, MCP servers, and permission grants are the vault. Know which one you are paying for.
Are findings chained or listed? Isolated findings are inventory. Chains are the product. Ask for a sample chain from a past engagement, names filed off.
Is a retest included? Without one, you paid for a point-in-time opinion. The retest is what turns the assessment into a control.
What are the rules of engagement? Pacing, depth gating, and authorization boundaries should be written before testing starts, not improvised during it.
Get a number built from your scope.
Tell us what your AI footprint looks like and we will scope it honestly. If the right answer is a scanner and a retest, we will say that. We would rather lose the engagement than sell you the wrong one.