Direct prompt injection
User input overrides system prompt, exfiltrates instructions, or coerces unsafe output.
Know what a determined user can do to your LLM feature before it ships to customers.
AI security testing for LLM-backed features — prompt injection, data leakage, tool-use safety, and RAG pipeline risks, aligned to the OWASP LLM Top 10.
What's at stake
LLM features break in ways traditional security testing does not cover.
A crafted prompt can extract another tenant's data, bypass policy filters, or coerce your agent into calling an API it should not. These show up in production features that shipped without adversarial testing.
Standard web app and API pentests will not test prompt injection, RAG retrieval boundaries, or tool-use manipulation. This engagement does.
What we cover
The parts of an LLM feature an outsider can actually reach.
Indirect prompt injection
A retrieved document, web page, or email instructs the model into unintended actions.
Data leakage and tenancy
Cross-user, cross-tenant, or cross-conversation context leakage in chat and RAG.
Tool-use safety
Unsafe tool selection, parameter tampering, unbounded tool chains, privilege boundaries.
Abuse and misuse
Generation of harmful content, evasion of policy filters, and rate-limit abuse.
RAG pipeline review
Retrieval boundaries, source-poisoning resilience, content-isolation guarantees.
When this fits
Three patterns we see most often.
Customer-facing chat
A chat or copilot on your product data. Risk: a curious user extracts another tenant's data or the system prompt.
Document or email RAG
A summarization or Q&A feature that reads uploaded or ingested documents. Risk: indirect prompt injection through retrieved content.
Agent with tools
A feature where the model can call APIs, search, or modify data. Risk: unsafe tool selection or parameter manipulation.
What you get
A report your product team can act on.
Adversarial corpus
The exact prompts, documents, and tool inputs we used. Reusable in your CI as regression tests.
OWASP LLM mapping
Findings mapped to the OWASP Top 10 for LLM Applications and to surrounding API / web categories.
Mitigation playbook
For each class of finding, a paste-ready set of mitigations: prompt structure, tool gating, retrieval limits.
Shipping an LLM feature and not sure what to test?
A quick scoping call maps the risk surface and gives you a fixed scope and price.
Get a straight answer Adjacent engagements
Pair AI testing with the service that wraps the model.
API testing →
The API layer that exposes the LLM feature to the world.
Web application testing →
The web app or chat UI the user actually sees.
Authenticated testing →
Role boundaries inside an LLM feature with multiple user types.
Compliance pentest →
Frame findings for SOC 2, ISO, NIST AI RMF, EU AI Act.
Further reading
Background from our team.
FAQ
AI security testing — common questions
When should we test an LLM-backed feature?
Before it reaches customers, and again whenever the prompt structure, tool-use surface, or data the model can read changes. Most regressions come from prompt-template edits or new tool integrations, not the model itself.
What does prompt injection testing cover?
Direct injection (user instruction overrides the system prompt) and indirect injection (a document, page, or email instructs the model). We test both against the actual feature surface — chat, RAG, summarization, and agent tool use.
Do you test for data leakage from the model?
Yes — context data the user should not see, cross-tenant and cross-user content returns, and training-data extraction patterns.
Do you cover RAG and tool-use surfaces?
Yes. RAG: source-document poisoning, retrieval boundary violations, instruction-injection through retrieved content. Tool-use: unsafe tool selection, parameter tampering, and chain-of-thought leakage.
Is this aligned to OWASP LLM Top 10?
Yes. Findings are mapped to the OWASP LLM Top 10 and to the OWASP API and Web Top 10 where the surrounding service is in scope.
Want a credible answer when a customer, auditor, or your board asks how secure you are?
A quick scoping call with the senior tester who would run your engagement. No slides, no pitch — we look at what you have, tell you what we would test first, and give you a fixed scope, price, and date.