AI and LLM Security Testing

LLMs struggle to separate instructions from data in natural language. A user message, an uploaded document or a fetched web page can carry hidden instructions. Once the model is connected to databases, email or internal systems, this becomes a direct security vulnerability.

We build the scope around the OWASP Top 10 for LLM Applications 2025. Key areas include:

• Prompt injection: Bypassing the model's rules through direct or indirect inputs.
• Sensitive information disclosure: Customer data, internal documents or credentials leaking into responses.
• System prompt leakage: Exposure of hidden instructions and business rules.
• Excessive agency: An agent performing unauthorized actions through tools or APIs.
• Improper output handling: Model output leading to XSS, SQL or command injection in the application.
• Vector and embedding weaknesses: Unauthorized document access or poisoning in the RAG store.
• Unbounded consumption: Abuse affecting cost and availability.

1. Architecture review: The model, system prompt, data sources, tools and permissions are mapped.
2. Threat scenarios: Attack scenarios are built around user roles and workflows.
3. Manual and tool-assisted testing: Scenarios are executed in a controlled way and successful attempts are documented.
4. Reporting: Findings are prioritized by impact and likelihood, with recommendations at prompt, permission and architecture level.
5. Retesting: The effectiveness of fixes is verified.

Organizations running customer-facing chatbots, internal knowledge assistants, coding assistants or AI agents that make decisions in business processes.

• Executive summary and risk overview.
• Findings evidenced with successful attack inputs and model responses.
• Risk ratings mapped to the OWASP Top 10 for LLM Applications 2025.
• Recommendations for the system prompt, permission design, input/output filtering and monitoring.
• Retest report.