AI Security

AI Security Consulting

Prompt injection, data exfiltration through model output, tool abuse and poisoned retrieval are not variants of existing web vulnerabilities. We threat-model AI systems specifically and design the architecture and controls that contain what we find.

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The business problem

The trust boundary moved and nobody redrew it

In conventional applications, data and instructions are separate. In systems built around language models they arrive in the same channel, so any content the model reads is potentially an instruction. That single property breaks assumptions throughout a security model: a document, a web page, an email or a support ticket can carry an instruction the model will follow. Add tools and system access, and the consequence stops being a wrong answer and becomes an action.

What we do

Threat-model, then constrain by design

We establish what an attacker would target and what they would gain, then design so that the achievable damage is bounded regardless of whether the model behaves. That means least-privilege tool scopes, validated tool calls, treating retrieved content as untrusted data, approval gates on consequential actions, and output controls where model text reaches another system. We also specify what to log, because containment without detection is half a control.

AI Security Consulting

Capabilities

AI Threat Modeling
LLM Security Architecture
Agent Security Architecture
AI Risk Assessment
Prompt Injection Risk
Data Leakage Assessment
AI Supply Chain Risk
Secure AI Architecture

Common use cases

Threat-model an AI application before it goes to production.
Establish a secure reference architecture other teams build against.
Assess an existing assistant that has system access nobody has reviewed.
Define what a supplier must evidence before their AI feature is approved.

How we deliver

Threat model

Establish what an attacker would target, and what they would gain by reaching it.
Test

Adversarial testing against realistic abuse, not a checklist of known strings.
Report

Findings with reproduction steps, severity and the fix, ranked by exploitability.
Retest

Verify the fixes hold, and leave the tests behind so regressions surface.

Technology

OWASP LLM Top 10
MITRE ATLAS
Garak
Burp Suite
ISO/IEC 27001

Security & governance

Testing is authorised in writing, scoped to agreed targets and run against a non-production environment unless you decide otherwise. Findings are handled as confidential and disclosed to you before anyone else. Nothing is retained beyond the engagement except the report and the regression tests you asked us to leave behind.

Independently audited against

Engagement models

AI Advisory

Expert consultants provide strategy, architecture, assessment and transformation guidance.

AI Project

We take responsibility for designing and delivering a defined AI solution.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

Architecture is the only durable control

Filters and guardrails are worth having and will eventually be bypassed. The controls that hold are architectural: what the system is permitted to reach, what it can do there, and what requires a person. We design from that premise, which is why our recommendations tend to be about permissions and boundaries rather than about detection rules.

Selected clients

Frequently asked questions

Can prompt injection be solved?

Not eliminated. It is mitigated by architecture: treat all retrieved content as untrusted, scope tools narrowly, gate consequential actions behind approval, and design so a successful injection reaches something bounded. Anyone offering complete prevention is selling a filter.

Do our existing penetration tests cover this?

Only partially. Conventional testing covers the application surface around the model. It does not typically cover injection through retrieved content, exfiltration via model output, or tool abuse, because those are not conventional vulnerability classes.

What is the single most common finding?

Over-scoped tool permissions. Systems are given broad access during development because it is convenient, and the scope is never narrowed before production. It is also usually the cheapest finding to fix.

How does this fit with our existing security function?

It extends it. We work with your security team rather than around them, and the output is intended to become their standard rather than our report. Where they lack AI-specific depth, we build it with them.

Related capabilities

AI Governance, Risk & Compliance

AI Governance

Establish the policies, controls and responsibilities required to deploy AI safely at enterprise scale.

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AI Infrastructure & LLMOps

AI Architecture

Design the reference architecture your AI systems share: models, retrieval, orchestration, data and controls.

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AI Governance, Risk & Compliance

EU AI Act Readiness

Classify your AI systems, map the obligations that follow, and close the gaps before they become binding.

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Discuss Your AI Initiative

Threat-model AI systems and design the architecture, controls and boundaries that contain their risk.

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