Prompt injection is a boundary problem
Prompt injection matters when untrusted content can influence privileged instructions, tool calls, data access, or user decisions. The fix is rarely only a better prompt.
AI Security Testing
Adversarial security testing for AI-enabled applications.
AI security testing covers the application, model-facing workflow, retrieval layer, tool integrations, permissions, and the traditional controls around the AI system.
What is tested
The complete AI application boundary, not just prompts in isolation.
LLM-enabled systems combine model behavior, application logic, retrieval, tools, permissions, and data handling. AI security testing validates how those layers behave under adversarial input and realistic misuse scenarios.
- Prompt and instruction abuse scenarios
- Tool and agent boundary testing
- Retrieval and sensitive context exposure review
- AI workflow threat modeling
service: ai-security-testing
status: scoped
[input] business objectives
[input] technical boundaries
[output] evidence + recommendationsRisk coverage
Find the places where AI behavior can cross security boundaries.
The work focuses on practical exposure: data leakage, prompt injection, unsafe tool use, confused authorization, retrieval poisoning, excessive context access, and brittle human approval workflows.
- Prompt and instruction attacksDirect and indirect prompt injection, instruction hierarchy failures, unsafe content handling, and bypass attempts.
- Tool invocation boundariesTesting whether model-driven tool calls can exceed intended permissions, arguments, workflow states, or approval gates.
- Retrieval layer exposureReview of sensitive context access, document isolation, tenant boundaries, source trust, and retrieval abuse cases.
- Evaluation-ready findingsReusable scenarios and evidence that can inform regression tests, guardrail evaluation, and operational monitoring.
AI security education
AI risk usually appears at integration boundaries, not inside the model alone.
LLM-enabled applications mix probabilistic model output with deterministic software controls. Security testing needs to examine how prompts, tools, retrieval, authorization, logging, and human approval interact.
Retrieval needs authorization
Retrieval systems should enforce source trust, tenant isolation, document permissions, and sensitive context limits before content reaches the model.
Tools are security-sensitive APIs
When a model can call tools, the tool boundary needs clear permissions, argument validation, approval rules, auditing, and safe failure behavior.
FAQ
AI security testing questions.
AI security testing is scoped around the deployed application and workflow, because most meaningful risk appears at integration boundaries.
Is this only prompt injection testing?
No. Prompt injection is one category. Testing also covers retrieval, tools, agents, authorization, data handling, logging, evaluation logic, and the surrounding application controls.
Can you test systems that use private data?
Yes, with agreed test data handling, boundaries, and evidence rules. The focus is whether sensitive context, tenant data, or internal knowledge can be exposed or misused.
Do you test AI agents and tools?
Yes. Tool and agent testing looks at invocation boundaries, permissions, approval flows, argument manipulation, state confusion, and abuse of connected systems.
Can findings become automated tests?
Where appropriate, high-value scenarios can be converted into repeatable test cases so teams can validate fixes and catch regressions as the AI workflow changes.
Start with a focused review
Need assurance before launch, audit, or scale?
Share the system, product, or AI workflow you want tested. The first step is a short scoping discussion to define objectives, constraints, and the right engagement model.