AI Application Penetration Testing

AI Application Penetration Testing is a security assessment of applications that use AI models, prompts, RAG pipelines, APIs, tools, MCP servers or autonomous workflows. It identifies risks that may allow attackers to manipulate outputs, access sensitive data or trigger unauthorised actions.

Yes. LLM testing is included where large language models are part of the AI application. Blacklock tests prompt injection, jailbreaks, system prompt leakage, sensitive data disclosure, unsafe output handling, RAG weaknesses and excessive resource use.

Yes. MCP testing is included where Model Context Protocol servers, tools or integrations are in scope. Blacklock tests MCP risks such as token exposure, tool poisoning, command injection, weak authorisation, context over-sharing and privilege escalation.

Web application testing focuses on traditional vulnerabilities such as injection, access control and session flaws. AI testing also examines prompt behaviour, model responses, retrieval pipelines, tool usage, MCP permissions, memory and unsafe autonomous actions.

Blacklock can test AI chatbots, copilots, workflow automation tools, AI-enabled SaaS platforms, internal assistants, RAG applications, LLM applications and agentic AI systems.

Yes. APIs used by the AI application can be included in scope. Blacklock tests API authentication, authorisation, business logic, input handling and whether the AI layer can trigger unauthorised API actions.

Testing covers prompt injection, jailbreaks, system prompt leakage, sensitive data disclosure, insecure output handling, RAG poisoning, excessive agency, insecure plugins, MCP tool abuse, token exposure, weak authentication and business logic abuse.

Access may include test accounts, user roles, API documentation, system architecture, sample prompts, RAG data sources, MCP server details, tool schemas, plugin details, approved tokens and controlled test environments.

Yes. Blacklock can assess internal AI applications where appropriate access is provided, including private environments connected through agreed access methods.

The duration depends on application complexity, number of workflows, integrations, user roles, tools, models and data sources. A standard assessment typically takes several days to two weeks.