Put AI to work. We’ll make sure it’s secure.
AI should be moving your business forward, taking on real work with real data. Our job is to make that safe. We test the models, copilots and agents you’re building against the ways they can be misused, so you can ship them and sleep.
Not sure this is the one? Find your fix →You secure the whole AI stack.
We test the whole AI stack: model, prompts, agents, the data pipeline and the infrastructure it runs on.
You catch a poisoned source before it spreads.
We follow where training and retrieval data comes from, and what a tampered source could do to what your model says and does.
You fix the exploits that actually reach data.
Prompt injection and jailbreaks only matter when they reach data or actions. We prove the ones that do, and rank the rest.
Attack the model, harden the pipeline.
Incenter continuously tests AI-facing exposure as your prompts, tools, and models change.
See the platform →Specialist operators run deep, manual AI red-team assessments of agents, models, and pipelines.
Meet the team →We start with the OWASP LLM Top 10.
The ten risks every AI deployment shares — our baseline, before we test deeper into your specific stack. Hover or tap any risk for what it means.
Instructions hidden in content the AI reads (an email, a document, a web page) get obeyed like commands. A booby-trapped email has been enough to make a major copilot quietly send internal data out, no click required.
The AI hands back data it shouldn't — secrets, internal details, or another user's information. Providers have left whole chat histories and API keys exposed by getting this wrong.
The model, its training data, or a plugin you pulled in is already compromised. Researchers found 100+ backdoored models on public hubs, infecting anyone who loaded them.
Tampered training or fine-tuning data quietly bends the model — planting bias, blind spots, or a hidden trigger that makes it fail exactly where an attacker wants.
Your app trusts the model output and pipes it straight into a browser, shell, or database. Output carrying a script or a query becomes a classic injection bug in your own app.
An agent holds more access or autonomy than its task needs. One tricked instruction turns 'summarise this ticket' into 'export the customer table and email it out.'
The hidden system prompt, with its rules, logic, sometimes keys, gets coaxed out of the model. Now the attacker has the map to bypass every guardrail you set.
A RAG assistant's retrieval layer can be poisoned or leaked: planted documents steer the answers everyone sees, or one user's query reaches another's data.
The model states something false with complete confidence and a person acts on it. A tribunal has already held a company liable for refund terms its chatbot simply invented.
Nothing caps how hard the model can be pushed. Attackers run up huge bills, starve real users of service, or lift the model itself, one query at a time.
The questions we actually get asked.
Can we safely let AI work with our data and customers?
Yes, once it's been properly tested. The risk usually isn't the AI itself. It's everything you connect it to. We check what it could be talked into doing before it ever touches a customer or your data.
What could actually go wrong?
Three things that land on the business: confidential information getting out, the AI taking an action it should never have taken, and the AI confidently telling someone something that simply is not true. All three have already happened to real companies.
Aren't our policy and our AI provider enough?
A policy guides how people behave; it can't stop the system itself from being misused. And your provider only secures their part, not the way you've set it up and connected it. The only way to know is to test it the way an attacker would.
If our AI gets it wrong, are we liable, and where do we start?
Yes. You are responsible for what your AI says and does, and a company has already been held legally liable for advice its chatbot gave a customer. Start with a short assessment of the AI you already use or are about to launch: what it can reach, how it could be misused, and what a single mistake would cost.
How big a project is this?
Smaller than you'd expect. It's a focused assessment scoped to the AI you actually use, not an open-ended programme. You get clear findings and a short list of what to fix, for a fraction of what a single incident would cost.
Will security slow down our AI plans?
It's what lets you move faster. Knowing it's safe is what turns “we're not sure” into “yes, ship it.” We test quickly, so AI moves the business forward instead of stalling in caution.