AI Security
Design AI systems that protect your data and your users.
We test your AI the way an adversary would — inputs, retrieval, tools and permissions — and hand you every finding, ranked by what it actually reaches.
Your model will be fooled. What matters is what it is holding when that happens.
The scanners already in your pipeline were built to read code, and none of them can read a prompt. Meanwhile the feature has a retriever pointed at your document store and a set of permissions nobody has looked at since the day it shipped.
No inventory of what it can reach
Nobody has listed every input the system reads or every system it is wired into, so the attack surface is an assumption, not a document.
Permissions that didn't travel with the data
Your retriever pulls from the document store, but the access controls that lived in the source system rarely make the trip with the chunk it returns.
Findings with no blast radius attached
A prompt injection and a permissions leak get the same severity label until someone works out what each one can actually reach.
The AI layer becomes something you can actually assess.
The part of the stack your existing tooling cannot see gets the same treatment as the rest of it — mapped, attacked, and written up in a form you can work from.
The attack surface, mapped
We document every input the system reads and every system it can reach, so you are working from an inventory rather than an assumption.
Authority you can audit
We test what the system retrieves and what it is permitted to act on, so the distance between the access it was given and the access it needs stops being invisible.
Findings ranked by reach
We rate every finding by what a successful attack actually touches, so you know which three matter this sprint and which can wait.
Why measure your AI's exposure with Avipra?
We test the system you built — your prompts, your retrieval, your tools, your permissions — using a method we would put in front of your board.
We attack your system, not the model
The foundation model has a vendor doing that work; we test the application you wrapped around it, which is where your exposure actually sits.
Retrieval is an access-control problem
We check what your retriever returns to which user, because the permissions that lived in the source system rarely travel with the chunk.
Every finding carries its blast radius
We report what a successful attack reaches and what it does not, so each fix gets sized against its consequence rather than its category.
Sign off with the evidence already in hand.
When the feature reaches you for approval, you know what it can do, what it cannot, and what has changed since the last time you looked.
Clarity
You know the full reach of the system and the exact boundary you are defending.
Alignment
Your engineers, your leadership and the security questionnaire on your desk are all answered from one document.
Scalability
The threat model holds as the system gains tools, so new capability arrives with its exposure already described.
AI Security — questions
How is this different from a normal penetration test?
A pentest looks at your infrastructure. This looks at what the model can be talked into: prompt injection, jailbreaks, instructions hidden inside the documents your system reads, and what happens when it holds a tool it shouldn't have reached for.
Do you test against production?
Against your real deployment, with scope agreed in writing first — a test against a sanitised staging copy tells you about staging. Where production can't be touched, we test the nearest environment and say plainly what that leaves uncovered.
Can you guarantee it can't be jailbroken?
No, and neither can anyone else — that's not the state of the field. The work is to bound what a successful jailbreak reaches, prove one tenant's question can't return another's data, and make anything unusual easy to spot early.
Do you fix what you find, or just write it up?
The findings specify the limits: which actions need human approval, which stay scoped to a tenant, what sits outside the model's reach. Your engineers implement them. We'll re-test the boundaries afterwards to confirm they hold.
Will a buyer's security team accept this?
The tenant and permission boundary evidence is the part that decides deals, and it's written to be handed over directly. We go at that first for exactly that reason. It supports their review — it doesn't replace one they insist on running.
Let's find out what it can reach.
Tell us what your AI feature touches today — running, mid-build, or still a design doc — and we will show you what testing it would look like.