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Cybersecurity & Compliance

AI & LLM Security Testing

Security testing for LLM applications, RAG systems and AI agents. We test for prompt injection, data leakage, unsafe tool use and broken access control, the ways AI features get abused in practice, and hand you reproducible findings with fixes.

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Overview

Where AI features get abused

An AI feature adds an attack surface that conventional testing does not cover: the model treats untrusted text as potential instructions. A customer message, an uploaded document, a web page your assistant summarises, or a record your agent reads can all carry instructions the model may follow. We test your LLM application the way an attacker would approach it, aligned to the OWASP Top 10 for LLM Applications: direct and indirect prompt injection, sensitive information and system-prompt disclosure, retrieval that ignores the user's permissions, model output that reaches HTML, SQL, shells or downstream APIs unvalidated, and agents with more privilege than their task needs. The application around the model stays in scope too: the APIs, authentication, rate limits and cost controls an attacker reaches first. Because our team also builds RAG systems and agents, remediation guidance is architectural: privilege separation, permission checks at retrieval and at the tool boundary, output validation, and human confirmation for consequential actions. A longer system prompt does not, on its own, stop a determined injection.

What's included

  • Direct and indirect prompt injection testing, including instructions hidden in documents, emails and web content
  • Data leakage checks: system prompts, other users' data and permission-scoped RAG retrieval
  • Agent and tool-use abuse: excessive permissions, unsafe actions and confirmation bypass
  • Insecure output handling where model output reaches HTML, SQL, shells or downstream APIs
  • Testing of the surrounding application: APIs, authentication, rate limits and cost abuse
Who it's for

Who needs AI security testing

  • Product teams shipping a customer-facing chatbot, copilot or AI agent built on a hosted or open-source model.
  • SaaS companies whose enterprise customers' security questionnaires now ask how AI features are protected and tested.
  • Organisations that have put a RAG assistant over internal documents with different access levels.
  • Teams giving an agent tools that act (sending email, changing records, calling internal APIs) who want to know the worst case before it happens in production.
When you need it

Signals it's time to test your AI

  • You are about to launch an AI feature to customers and want it tested before attackers test it for you.
  • An enterprise prospect has asked how your LLM feature is protected against prompt injection and data leakage.
  • Your RAG assistant indexes documents some users should never see, and nobody has checked whether retrieval enforces that.
  • An agent is moving from read-only to write access on real systems.
  • You have changed model, provider or prompt architecture and earlier assumptions about behaviour may no longer hold.
Deliverables

What you receive

Every engagement ends with documents and fixes your team can act on, rather than a presentation.

  • A prioritised report of findings with the exact prompts, payloads and evidence needed to reproduce each one
  • Mapping of every finding to the OWASP Top 10 for LLM Applications
  • Architectural remediation guidance covering privilege separation, retrieval-time permission checks and output validation, beyond prompt edits
  • An executive summary of business risk for stakeholders and customers
  • An optional retest once fixes are deployed
How it works

From scoping to retest

The steps are the same whether the work is an assessment or a build, so you always know what happens next.

  1. 1

    Discover

    We start by understanding your systems, goals, and constraints, including scope, risk tolerance, and what success looks like, so the work is aimed at your problem rather than a generic template.

  2. 2

    Assess or build

    For security work, we test and analyse against recognised standards. For development, we build in small, reviewable increments. Either way, you see progress early and can change direction.

  3. 3

    Report or ship

    You get clear, prioritised deliverables, either a report your engineers can act on or working software shipped to your environment, with the context to understand what was done and why.

  4. 4

    Support

    We stay available after delivery: retesting fixes, iterating on the product, and answering the questions that come up once real users and real traffic arrive.

FAQ

AI Security Testing: common questions

What is AI or LLM security testing?

It is security testing of an application built on a large language model: a chatbot, a RAG assistant, an AI agent, or an AI feature inside a product. It covers the ways language input can subvert the system (prompt injection, data leakage, unsafe tool use, unvalidated output), plus the conventional application around the model. The goal is the same as any penetration test: find what an attacker could do, and show you how to stop it.

Can prompt injection be fixed completely?

Not with any technique available today. A model cannot reliably tell content it was asked to read from instructions it was given. What you can control is what a successful injection is able to do: keep the model's privileges small, enforce authorisation at the tool and data boundary on the server, validate output before it reaches anything that executes it, and require human confirmation for consequential actions. Testing measures that blast radius and shows where it is larger than you assumed.

Do you test the AI model itself?

We test your application and how the model is wired into it: your prompts, retrieval, tools, data and users. We do not evaluate how a foundation model was trained or aligned; that belongs to the model provider. In practice almost every exploitable issue we look for lives in the integration rather than in the model, which is why this testing is useful whichever model you use.

How is this different from a regular VAPT?

A VAPT tests your applications, APIs and infrastructure. AI security testing adds the language-level attack surface a VAPT does not cover, such as injected instructions and leakage through model responses. The surrounding web application and APIs are tested in both, so the two are often scoped together into one engagement.

What access do you need?

We recommend a grey-box test: access to the application with representative user roles, plus visibility of system prompts, tool definitions and retrieval configuration. That finds more in less time than pure black-box testing. Black-box testing is possible when you want an outside attacker's view, and we agree scope and rules of engagement in writing before anything starts.
Related services

Often tested alongside

Teams that come to Safe Tech AI for AI security testing frequently need these too.

  • Web Application Security Testing

    Deep testing of your web applications against OWASP-class risks such as injection, broken access control and authentication flaws, with findings mapped to how your app works rather than to a generic checklist.

    Learn more

  • API Security Testing

    Testing REST, GraphQL, and internal APIs for authentication, authorization, injection, and abuse risks. APIs power your apps and integrations, and they are easy to expose by accident.

    Learn more

  • AI Agents

    Multi-step, tool-using AI systems that complete tasks rather than only answering questions, designed with the guardrails, permissions, and human oversight that make autonomy safe to deploy.

    Learn more
Further reading

Guides on AI security testing

Find out what your AI will do for an attacker.

Get a scoped test of your LLM application, RAG system or agent, with reproducible findings and fixes that change the architecture, not just the prompt.

Request an AI security assessment