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AI and LLM Security Testing

AI security testing that examines prompts, tools, memory, and orchestration as a real attack surface

We validate prompt injection, unsafe tool execution, sensitive data exposure, model-to-system trust assumptions, and the controls that should limit harmful actions.

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What teams usually value in this service

Prompt injection and tool-abuse focus
System and workflow view, not only model behavior
Findings that map to engineering controls

What is covered

  • Prompt injection and instruction-hijacking paths
  • Tool invocation, plugin abuse, and unsafe agent actions
  • Sensitive data exposure through prompts, retrieval, and memory
  • Weak authorization around AI-triggered actions and outputs
  • System design controls that should constrain model influence

Who this service is for

  • Teams launching AI features into customer-facing products
  • Products that connect models to tools, retrieval, file handling, or internal systems
  • Organizations facing buyer questions about AI risk and control maturity

Common attack paths and issues tested

Prompt injection to unsafe tool use

We test whether untrusted content can manipulate the model into running actions or exposing data beyond intended boundaries.

Cross-user data leakage

Retrieval, memory, and response construction are reviewed for ways one user’s context could bleed into another user’s outputs.

Agentic workflow abuse

Where the model can call tools or trigger downstream actions, we validate how authorization and execution safety are actually enforced.

What clients receive

  • AI-specific findings tied to model, orchestration, and system controls
  • Remediation guidance focused on architecture and enforcement points
  • Business impact framing for customer trust and launch decisions
  • Retest support for key fixes or control changes

Engagement process

  1. 1Scope models, workflows, tools, data sources, and trust boundaries
  2. 2Test prompt behavior, execution paths, and control points
  3. 3Validate exploitability and likely business impact
  4. 4Deliver reporting, engineering guidance, and retest support

Related resources

Articles that help teams evaluate and prepare for this service

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Frequently asked questions

Is AI testing just prompt red teaming?

No. Prompt testing matters, but effective AI security testing also looks at tools, data access, orchestration logic, and how the rest of the system trusts model output.

Can you assess an AI feature before full launch?

Yes. Pre-launch review is often the right time because it lets teams fix unsafe design assumptions before customers depend on the workflow.

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