Our Artificial Intelligence (AI) Security Testing service analyses the security of AI models, AI-enabled applications, and their supporting infrastructure.
AI systems are being increasingly adopted. AI is integrated into critical business processes, decision-making systems, and customer-facing applications. From large language models and AI-powered chatbots to machine learning models embedded in platforms and products, these systems introduce new and unique security risks that traditional security testing does not address.
Our AI Security Testing uses controlled offensive testing. We identify vulnerabilities such as prompt injection, model manipulation, data leakage, and insecure integrations. The goal is to help organisations secure AI systems before attackers exploit them.
What we offer
Contact us for our Artificial Intelligence Security Testing if you want to ensure your AI systems are secure.
Increase use of AI and security
As organisations increasingly rely on AI for automation and decision-making, securing these systems is essential to protect business operations, intellectual property, and user trust.
New attack surfaces and threat models
Simultaneously AI systems introduce new attack surfaces and threat models that differ significantly from traditional software systems.
Methodology
We combine traditional application security testing with AI-specific adversarial techniques.
What you receive
Our AI Security Testing will give you oversight of your AI infrastructure and how this could be exploited by attackers.
Our methodology includes scoping and reviewing your AI architecture, undertaking threat modelling and adversarial and integration security testing, and validating any impacts.
We also provide you with a comprehensive and actionable report, which includes:
A summary highlighting AI security risks
Technical findings with reproduction steps
Proof-of-concept demonstration of vulnerabilities
Risks prioritisation and potential business impacts
Remediation guidance for developers, ML engineers, and security teams
Recommendations for secure AI development and deployment
What we assess
Our Artificial Intelligence Security Testing evaluates the entire AI ecosystem. AI-powered systems consist of multiple components. These include models, training pipelines, APIs, infrastructure, and integrations.
Here’s what we assess:
We evaluate how AI models behave under adversarial conditions. Can they can be manipulated? Vulnerabilities include prompt injection attacks manipulating model outputs, jailbreaking techniques bypassing AI safeguards, extracting sensitive training data, model inversion attacks which recover private information, adversarial inputs manipulating model predictions, unauthorised model access or replication.
Many AI models are integrated into web applications, chatbots, or enterprise tools. Vulnerabilities include, prompt injection through user inputs, sensitive information leaked through responses, insecurely handled user prompts, AI-powered automation features, improper validation of AI-generated outputs.
Machine learning models rely heavily on training datasets. Vulnerabilities include data poisoning attacks during model training, manipulated datasets affecting model behaviour, leaked sensitive data in training sets, lack of validation or monitoring of model updates
Many AI systems rely on APIs for model interaction and integration. Vulnerabilities include unauthorised access to AI APIs, lack of authentication or rate limiting, exposed API keys or model endpoints, abuse of AI APIs for automated attacks.
AI models run on cloud platforms, containers, and Machine Learning (ML) pipelines. Vulnerabilities include cloud infrastructure hosting models, insecure model storage or repositories, weak access controls for ML pipelines, exposed internal model services.
AI models often process large volumes of sensitive data. Vulnerabilities include personally identifiable information (PII) leakage, corporate data exposure through model responses, insecure logging of prompts and responses, lack of data minimisation or output filtering.
What frameworks we follow
Our Artificial Intelligence Security Testing aligns with emerging best practices and security frameworks, including:
OWASP Top 10 for Large Language Model Applications
OWASP AI security guidance
National Institute of Standards and Technology AI Risk Management Guidance
NIST AI Risk Management Framework
ISO/IEC 27001
These frameworks help ensure AI systems are tested using industry-recognised best practices.
FAQ
Find answers to common questions about our services and what to expect from your experience with us.
Do you test Large Language Models (LLMs)?
Yes. We assess LLM-based applications, including prompt injection risks, data
leakage, and model manipulation.
Can AI models leak sensitive data?
Yes. Poorly designed AI systems can expose sensitive training data or internal
information through model responses.
Is AI security testing different from traditional pentesting?
Yes. AI systems introduce new risks such as prompt injection, adversarial inputs,
and model manipulation that require specialised testing techniques.
When should AI systems be tested?
AI systems should be tested before deployment, after major model updates, and
periodically as the system evolves.
Let’s work together
Are you looking to test the security of your Artificial Intelligence systems?
You’re in the right place.