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As artificial intelligence becomes deeply embedded in business-critical operations, traditional cybersecurity testing approaches fall short of identifying AI-specific vulnerabilities. Our Artificial Intelligence Offensive Testing service employs advanced adversarial techniques to systematically attack your AI systems, uncovering security weaknesses before malicious actors can exploit them.
Our specialised red team simulates real-world attacks against AI systems, machine learning models, and AI-powered applications. We combine cutting-edge adversarial AI techniques with traditional penetration testing methodologies to provide comprehensive security assessment of your AI ecosystem.
AI Application Layer: Comprehensive testing of AI-powered applications, APIs, and user interfaces. This includes prompt injection attacks, input manipulation, and testing of authentication and authorisation controls specific to AI services. We conduct targeted assessments of enterprise AI tools such as Microsoft Copilot integration with SharePoint to identify unauthorised information exposure, testing whether AI assistants can be manipulated to reveal sensitive documents, confidential data, or restricted content through carefully crafted queries.
AI Infrastructure: Testing of underlying infrastructure supporting AI workloads including cloud AI services, GPU clusters, and containerised ML environments. We assess network segmentation, access controls, and potential lateral movement opportunities.
Model Context Protocol (MCP) Systems: Comprehensive security assessment of MCP implementations that enable AI agents to interact with external tools and data sources. MCP is an emerging standard that allows AI systems to securely connect to databases, APIs, file systems, and other resources through standardised server-client communication protocols. We test MCP server configurations, authentication mechanisms, resource access controls, and data exchange security to identify vulnerabilities in these critical AI integration points that could lead to unauthorised data access or system compromise.
Enterprise AI Assistant Testing: Assessment of AI productivity tools like Microsoft Copilot, Google Workspace AI, and custom chatbots. We test whether these systems can be manipulated to expose sensitive information, bypass access controls, or leak confidential data through social engineering techniques and prompt manipulation.
SharePoint Copilot Information Exposure: Specific testing scenarios targeting Microsoft Copilot’s integration with SharePoint environments. We assess whether unauthorised users can extract sensitive documents, customer data, or proprietary information through crafted prompts that exploit Copilot’s access to SharePoint repositories, even when users lack direct permissions to the underlying content.
AI-Powered Document Processing: Testing of intelligent document processing systems to identify whether attackers can inject malicious content, extract sensitive information from processed documents, or manipulate AI classification systems to mishandle confidential materials.
Customer Service AI Testing: Assessment of AI chatbots and virtual assistants used in customer-facing applications. We test for information leakage, unauthorised access to customer records, and potential manipulation of AI responses to extract business intelligence or personal data.
Our methodology incorporates state-of-the-art adversarial techniques including prompt injection, jailbreak attempts, and evasion attacks. We test across multiple AI domains including natural language processing systems, computer vision applications, and AI-powered business processes.
Adversarial Examples: Creation of carefully crafted inputs designed to fool AI models whilst appearing normal to human observers. We test robustness across different attack vectors and assess the real-world impact of successful adversarial attacks.
Jailbreak Testing: Systematic attempts to bypass AI safety guardrails and content filters through sophisticated prompt engineering techniques. We test whether AI systems can be manipulated to generate prohibited content, ignore security policies, or perform actions outside their intended scope through role-playing scenarios, hypothetical frameworks, and multi-step persuasion techniques.
Model Stealing and Inversion: Attempts to reverse-engineer proprietary models through query-based attacks and assess whether sensitive information about training data can be extracted.
Cutting-Edge Expertise: Our team combines advanced AI research capabilities with practical penetration testing experience, staying current with the latest adversarial AI techniques and emerging threats.
Industry-Specific Knowledge: Deep understanding of AI applications across finance, healthcare, retail, and critical infrastructure, enabling contextually relevant testing scenarios.
Comprehensive Coverage: Testing spans the entire AI lifecycle from development through production, ensuring no security gaps remain unaddressed.
Regulatory Alignment: Testing methodologies designed to support compliance with emerging AI regulations and industry standards.
Measurable Outcomes: Clear metrics demonstrating security posture improvement and quantifiable risk reduction across your AI systems.
Our Artificial Intelligence Offensive Testing service provides the assurance that your AI systems can withstand real-world attacks whilst maintaining the performance and reliability your business depends upon.