AI & LLM Penetration Testing
Authorized, scoped testing of deployed AI applications. We start with your product: its model interface, retrieval and documents, agents, tool or MCP integrations where present, and the web and API authorization and data flows around them. Security review supports the testing where architecture and trust boundaries need context.
- Product teams preparing an LLM-powered chat, copilot, search, or agent feature for release
- SaaS and fintech companies integrating hosted or self-managed model endpoints
- Teams that need independent testing of RAG, tenant boundaries, tools, or MCP integrations
- Security and engineering leaders who need reproducible evidence for remediation or customer assurance
- Architecture and trust-boundary review for user input, model output, retrieval, documents, tools, and application data
- Targeted adversarial test cases selected for the deployed design and authorized interfaces
- Manual validation of prompt injection paths, sensitive-context exposure, unsafe output handling, and abuse controls where applicable
- RAG authorization, tenant separation, retrieval filtering, and document-ingestion testing where present
- Agent, tool, function-calling, or MCP authorization and action-boundary testing where present
- Surrounding web and API authentication, authorization, data-flow, and business-logic testing within scope
Coverage follows the deployed product
Model interface
Test exposed behavior, instruction boundaries, input handling, output handling, and sensitive-context risks through authorized product interfaces.
RAG, retrieval & documents
Assess retrieval authorization, tenant separation, filtering, ingestion trust, and context exposure where these components exist.
Agents, tools & MCP
Test tool selection, arguments, approvals, authorization, and action boundaries for enabled integrations.
Web, API & data flows
Validate the authentication, authorization, business logic, and data paths surrounding the AI feature.
Clear limits
Testing a third-party model’s exposed behavior and integration is not an audit of inaccessible weights, proprietary training data, or the provider’s internal model pipeline. Those require separate scoping, access, and potentially different specialists.
Governance context
The NIST AI RMF can inform separate governance advisory, but a penetration test does not establish AI RMF compliance or formally certify an AI system.
- Understand which weaknesses are reproducible in the deployed product
- Connect technical findings to realistic product, data, tenant, and business consequences
- Give engineering teams practical remediation guidance and evidence
- Support customer assurance without overstating what the assessment proves
- Confirmed scope, architecture, trust boundaries, assumptions, and exclusions
- Reproducible findings with evidence, severity, and business impact
- Prioritized remediation guidance for product and engineering teams
- Optional retest of agreed fixes, documented against the original findings
Four steps from scope to action
- 01
Define the scope
Confirm the deployed AI components, authorized surfaces, roles, and testing boundaries.
- 02
Assess the environment
Test the model, retrieval, tools, APIs, and surrounding controls for reproducible weaknesses.
- 03
Prioritize the findings
Rank confirmed findings by product, data, tenant, and business impact.
- 04
Deliver the action plan
Provide clear evidence and practical remediation guidance for engineering.