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Evaluate the risks your GenAI deployments introduce before an attacker does.
A technical and governance review of your organisation's large language model and generative AI deployments — assessing prompt injection vulnerabilities, data leakage paths, model poisoning risks, and access control weaknesses.
Large language models and generative AI tools introduce a new category of security risk that traditional application security testing does not cover. Prompt injection — where malicious input manipulates model behaviour — is now a recognised attack vector with real-world exploitation cases. Data leakage through model outputs, retrieval-augmented generation (RAG) architecture weaknesses, and insufficient access controls on AI APIs are emerging as significant risk areas for organisations that have deployed LLM-powered applications. The LLM & GenAI Risk Review applies OWASP's LLM Top 10 framework alongside CYVOXAI's own methodology to assess the specific risks of your AI deployments. Whether you have built a custom LLM application, integrated a third-party GenAI tool, or deployed a RAG-based internal knowledge system — we assess the full risk picture: prompt security, data handling, API controls, model access, and operational logging. This is a technical engagement, but outputs are framed for both security teams and business stakeholders.
A structured 4-step engagement designed to deliver clear, measurable outcomes — not just activity.
We review your LLM architecture — model type, hosting arrangement, RAG configuration, API structure, integration points, and data flows — to understand the full attack surface before testing begins.
Structured testing of the model's resistance to prompt injection attacks, jailbreak attempts, indirect prompt injection through RAG sources, and system prompt extraction — covering both direct and indirect attack vectors.
Review of data handling practices: what data the model has access to, how outputs are filtered, whether training data can be extracted, and whether access controls on the AI API prevent unauthorised queries.
Findings are rated by severity and business impact, with specific, implementable remediation guidance — input validation approaches, output filtering, system prompt hardening, and architectural recommendations.
Every engagement produces tangible outputs your organisation can use — not just a workshop and a verbal debrief.
This engagement is specifically designed for the following types of organisations.
Technology and SaaS businesses that have built or are building LLM-powered products
Organisations that have deployed internal AI assistants or RAG-based knowledge tools
Security teams needing independent validation of an AI vendor's security claims
Other AI security services that complement this engagement.
Start with a conversation — no commitment, no lengthy forms. Our AI security advisors will assess your current position and explain what this engagement would involve for your specific context.