Security Testing of AI Solutions
We verify whether chatbots, RAG systems, and AI agents are resistant to prompt injection, data leaks, and permission errors.
What are the benefits of AI solution security testing?
Detection of AI Vulnerabilities
We assess the solution’s resistance to prompt injection, jailbreaks, system prompt disclosure, data leaks, and context handling errors.
Protection of Company Data
We verify whether the chatbot, RAG system, or AI agent does not disclose user data, internal documents, conversation history, or confidential information.
Secure AI Implementation
We evaluate the security of the application, model, API, integrations, and data access before or after the solution’s deployment within the organization.
Control over AI Agent Operations
We check whether the AI agent does not perform unauthorized actions, does not use excessive permissions, and correctly handles integrated tools.
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Security Testing of AI Solutions
Through our tests, you reduce the risk of data leaks, model manipulation, and uncontrolled AI operations.
During testing, we check:
- Data and context security – we analyze what data flows into the model, logs, conversation history, knowledge bases, and company systems.
- Prompt injection and jailbreaks – we test whether system instructions can be bypassed, model behavior changed, or information disclosure forced.
- Authorization and data separation – we verify that users only have access to data and functions consistent with their role.
- RAG systems and knowledge bases – we verify access to documents, knowledge sources, vector indexes, and data used by AI.
- AI agents and integrations – we assess whether AI does not perform unauthorized actions in connected systems.
- API and backend – we check whether the application and integrations with the model do not disclose data and are not vulnerable to attacks.
Tools we use:
- OWASP Top 10 for LLM Applications – a reference point for assessing risks in LLM and GenAI applications.
- OWASP Testing Guide and OWASP API Security Top 10 – the basis for testing applications, backend, API, and access control.
- Burp Suite and OWASP ZAP – analysis of traffic, endpoints, requests, responses, and application vulnerabilities.
- Postman or Insomnia – testing APIs, plugins, integrations with the model, and company systems.
- Manual LLM scenarios – testing prompts, abuse cases, and AI behavior in unusual situations.
- Configuration and data flow analysis – verification of prompt processing, responses, logs, documents, and user data.
Frequently Asked Questions
What is AI solution security testing?
AI solution security testing is a controlled evaluation of systems utilizing artificial intelligence, LLM models, company data, APIs, and integrations. Its purpose is to verify that the AI solution does not disclose confidential information, cannot be manipulated, and operates in accordance with the organization’s assumptions.
Which AI solutions can be tested?
Chatbots, AI assistants, RAG systems, copilots, AI agents, applications integrated with LLM models, and tools for analyzing documents, data, or reports can be subjected to testing.
Does AI security testing only cover the model?
No. We test the entire solution: the application, model, API, backend, integrations, RAG system, data access, user permissions, and actions performed by AI agents.
What is prompt injection?
Prompt injection is a technique for manipulating an AI solution using specially crafted instructions. An attacker may attempt to bypass system rules, alter model responses, disclose data, or force actions inconsistent with the application’s intended purpose.
Does AI testing include data security?
Yes. We verify that the AI solution does not disclose personal data, confidential information, company documents, conversation history, prompts, logs, or data belonging to other users or teams.
When should AI security tests be performed?
Testing should be performed before production deployment, after adding new integrations, after changing the model, or when the AI solution begins processing company data, documents, customer information, or data from internal systems.
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