AI & LLM Security Assessment Services

Your AI features introduce a new class of vulnerabilities. Are you testing for them?

LLM-powered applications don’t behave like traditional software – and they don’t fail like it either. AMATAS AI/LLM security assessment uncovers the vulnerabilities unique to AI systems, from prompt injection to model manipulation, before attackers discover them first.

What we assess

Three layers of AI security coverage

AI systems are more than just an LLM – they span your application layer, your APIs, and the infrastructure running your models. We assess all three.

LLM-powered applications

The user-facing layer – chatbots, AI assistants, and document processors built on top of LLMs.

Prompt injection via user input
System prompt extraction
Access control bypass via LLM
Insecure output handling
Indirect prompt injection via external content

AI APIs & integrations

The integration layer – APIs connecting your application to LLM providers, vector databases, and external tools.

API authentication & authorisation
Excessive data exposure in API responses
Insecure tool/plugin integrations
Vector database access controls
Third-party LLM provider trust boundaries

Model infrastructure

The hosting layer – self-hosted models, fine-tuning pipelines, and the infrastructure serving your AI systems.

Model serving endpoint security
Training data access & exposure
Fine-tuning pipeline integrity
Model file access controls
Inference infrastructure hardening

AI-specific threat vectors

What we test for - and what makes AI different

Prompt injection

Crafted inputs that override system instructions, manipulate model behaviour, or cause the LLM to act against the application’s intent.

Indirect prompt injection

Malicious instructions embedded in external content – documents, web pages, emails – that the LLM processes and executes without the user’s knowledge.

Sensitive data leakage

Training data, system prompts, or other users’ data surfaced through the model’s outputs – a risk unique to LLM memory and context handling.

Insecure output handling

LLM outputs passed to downstream systems without sanitisation – enabling XSS, command injection, or SSRF via model-generated content.

Excessive agency

AI agents granted too many permissions or operating without sufficient human oversight – allowing malicious inputs to trigger unintended real-world actions.

Model denial of service

Inputs engineered to consume disproportionate compute resources – degrading performance or driving up API costs at scale.

When to use it

The right moments for an AI/LLM security assessment

Before launching an AI feature

Validate security before your LLM-powered product or feature ships to users – before they find the issues first.

When integrating a new LLM provider

Each new LLM integration brings new trust boundaries and data flows – assess them before going live.

Before deploying AI agents

Autonomous agents with tool access carry significantly higher risk – assess permissions, oversight, and injection exposure before deployment.

For regulatory or compliance purposes

The EU AI Act and emerging AI security frameworks are requiring documented risk assessments for high-risk AI systems.

Compliance

Standards and frameworks addressed

AI security is entering the regulatory mainstream. Our assessments provide documented evidence of AI risk management for the following frameworks and emerging mandates.

FAQ

AI/LLM security assessment - common questions

How is AI/LLM security testing different from standard application penetration testing?

Standard penetration testing targets code-level vulnerabilities – injection flaws, authentication weaknesses, access control issues. AI/LLM security testing targets a fundamentally different attack surface: the model’s ability to be manipulated through natural language. Prompt injection, indirect prompt injection, and excessive agency are not detectable by traditional testing tools or methodologies. They require a dedicated approach built specifically for AI systems.

We use a third-party LLM provider (OpenAI, Anthropic, etc.) - does that mean we're secure?

No – the LLM provider secures the model itself, but the security of your application is entirely your responsibility. How you construct prompts, handle outputs, grant tool access, and expose the model to users introduces vulnerabilities that have nothing to do with the underlying model provider. Most AI security incidents occur at the application layer, not the model layer.

What is indirect prompt injection and why is it particularly dangerous?

Indirect prompt injection is one of the most dangerous AI-specific attack vectors. Unlike direct prompt injection (where a user crafts malicious input themselves), indirect injection embeds malicious instructions in external content that your AI processes – a document, a webpage, an email. If your AI agent retrieves and processes that content, the hidden instructions can hijack its behaviour entirely – causing it to exfiltrate data, take unauthorised actions, or deceive users – without the user or the system being aware.

Is AI security testing relevant if we're only using AI internally?

Yes – internal AI tools often carry higher risk than customer-facing ones, because they typically have access to sensitive corporate data, internal systems, and privileged credentials. An internal copilot that can read documents, send emails, or query databases is a high-value target. If an attacker can influence that tool’s inputs – through phishing, document injection, or indirect prompt injection – the potential impact is significant.

Building with AI? Let's make sure it's secure.

Talk to our team – we’ll scope the right assessment for your AI architecture, use case, and risk profile.

Often combined with AI/LLM security assessment

Web & API Penetration Testing

Full-scope testing of the web application and APIs your AI system is built on.

Source Code Review

Static analysis of your AI integration code – prompt construction, output handling, and tool permission logic.

Secure Architecture Review

Strategic review of your AI system’s design – trust boundaries, data flows, access controls at architecture level.