AI & ML Security Testing
Specialised security assessments for artificial intelligence systems, large language models (LLMs), and machine learning applications to identify vulnerabilities in models, training pipelines, and inference systems.
Securing the Future of AI
As organisations increasingly deploy AI and machine learning systems, new attack surfaces and vulnerabilities emerge. Our AI security testing identifies risks unique to machine learning systems, from prompt injection and model poisoning to adversarial attacks and training data exposure.
We specialise in testing Large Language Models (LLMs), generative AI applications, computer vision systems, recommendation engines, and ML-powered APIs. Our assessments cover the entire ML lifecycle from data collection through model deployment and inference.
What We Test
- Large Language Model (LLM) applications and chatbots
- Generative AI systems (text, image, code generation)
- ML-powered APIs and microservices
- Computer vision and image recognition systems
- Natural language processing (NLP) applications
- Recommendation and decision-making systems
- AutoML platforms and model training pipelines
- AI agents and autonomous systems
AI-Specific Attack Vectors
Vulnerabilities unique to artificial intelligence and machine learning systems
Model Attacks
- Model extraction and stealing
- Adversarial example generation
- Model inversion attacks
- Membership inference
- Model poisoning
LLM-Specific Risks
- Prompt injection attacks
- Jailbreaking and guardrail bypass
- Training data extraction
- Insecure plugin integration
- Excessive agency vulnerabilities
Data & Pipeline
- Training data poisoning
- Data leakage and exposure
- Supply chain attacks (models/datasets)
- MLOps pipeline vulnerabilities
- Feature manipulation
Our AI Security Testing Process
System Discovery & Mapping
We analyse your AI/ML architecture including model types, training pipelines, data sources, API endpoints, and integration points. We identify the attack surface specific to your AI implementation.
LLM & Prompt Testing
For LLM applications, we test for prompt injection, jailbreaking, guardrail bypass, context manipulation, and training data extraction. We evaluate how the system handles malicious or unexpected inputs.
Model Security Assessment
We test for model extraction, adversarial examples, membership inference, and model inversion attacks. We assess model robustness and evaluate protections against model stealing.
Data & Pipeline Security
We review training data sources, MLOps pipelines, model versioning, and deployment processes. We test for data poisoning vectors and supply chain vulnerabilities in models and datasets.
Integration & API Testing
We assess APIs serving ML models, plugin integrations, RAG (Retrieval-Augmented Generation) systems, and vector databases. We test authentication, authorisation, and data handling in AI-powered endpoints.
Reporting & Remediation
Detailed findings with proof-of-concept attacks, risk ratings, and specific remediation guidance for AI/ML vulnerabilities. Includes retest validation within 30 days.
Common AI Vulnerabilities We Identify
Prompt Injection & Manipulation
Direct and indirect prompt injection attacks that bypass system instructions, extract sensitive data, or cause unintended model behaviour. Includes testing for context hijacking and instruction override.
Training Data Exposure
Techniques to extract training data, personally identifiable information (PII), or proprietary information memorised by the model. Includes membership inference and data reconstruction attacks.
Model Denial of Service
Resource exhaustion attacks that exploit expensive model operations, infinite loops in agents, or excessive API calls. Testing for rate limiting and resource management vulnerabilities.
Insecure Plugin Design
Vulnerabilities in LLM plugins, function calling, and tool use. Testing for insufficient input validation, excessive permissions, and unsafe code execution in AI agent ecosystems.
Adversarial Examples
Crafted inputs that cause misclassification or unexpected behaviour in ML models. Testing computer vision, NLP, and decision-making systems for robustness against adversarial attacks.
Supply Chain Vulnerabilities
Risks from third-party models, pre-trained weights, datasets, and ML libraries. Assessment of model provenance, integrity verification, and dependency security.
Why AI Security Testing Matters
Protect Sensitive Data
Prevent training data leakage, PII exposure, and unauthorised extraction of proprietary information embedded in AI models.
Model Integrity
Ensure your ML models behave as intended and are protected against model stealing, poisoning, and adversarial manipulation.
Regulatory Compliance
Prepare for emerging AI regulations including the EU AI Act, UK AI Regulation, and industry-specific requirements for AI systems.
Secure Your AI Systems
Get expert security assessment for your AI/ML applications before vulnerabilities are exploited.