Direct prompt injection
User input overrides system prompt, exfiltrates instructions, or coerces unsafe output.
Your customers are asking how you red-team the LLM feature — and prompt injection is the question nobody on your team has a clean answer to yet.
Prompt injection, RAG leakage, tool-use safety, and the service that wraps the model — tested against the OWASP Top 10 for LLM Applications, with NIST AI RMF or EU AI Act framing on request.
What is at stake
Four situations AI teams are navigating right now.
Customer red-team requests
Your customers are asking how you stress-test the model and the surrounding feature.
NIST AI RMF / EU AI Act
You need documented evidence of adversarial testing on the AI feature itself.
Tool-use risk
Your model calls internal APIs or modifies data — you need to know what an attacker can chain.
Pre-launch hardening
You are shipping a new LLM feature and want to find the worst case before customers do.
How we help
We test the six surfaces customers and framework reviewers ask about when you ship an AI feature.
Findings are mapped to the OWASP Top 10 for LLM Applications. NIST AI RMF or EU AI Act framing is added on request — the language enterprise customers and regulators expect.
Indirect prompt injection
A retrieved document, web page, or email instructs the model into unintended actions.
RAG and retrieval boundaries
Cross-tenant retrieval leakage, source-document poisoning, content isolation.
Tool-use safety
Unsafe tool selection, parameter tampering, unbounded chains, privilege escalation through tools.
Data exposure and tenancy
Conversation, training-data, and cross-user context leakage in chat and agent surfaces.
Surrounding service
API auth, rate limiting, abuse resistance, and conventional web-app issues wrapping the model.
How an engagement works
Four steps from scoping call to a report your customers and framework reviewers will accept.
- 01
Scoping call
A quick call. We learn your model stack, retrieval architecture, and tool integrations — and whether customers or regulators are driving the test. You leave with a fixed scope, price, and date.
- 02
Hands-on testing
A senior tester runs the engagement end-to-end — prompt injection, RAG boundaries, tool chains, and the surrounding service. Critical findings surfaced immediately on a live channel.
- 03
Report you will read
Every finding has a working proof and a remediation engineers can act on. Mapped to the OWASP Top 10 for LLM Applications. NIST AI RMF or EU AI Act framing included on request.
- 04
Retest included
We retest fixed items and update the report at no extra cost. The version you share with customers or framework reviewers reflects your actual fixed state.
Customer asking how you red-team your AI feature?
A quick scoping call turns that question into a fixed scope, price, and start date.
Get a straight answer Why AI teams trust the result
Senior testers, real certifications, and a report customers and reviewers accept.
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Certifications
OSCP · OSWE · GPEN · GXPN · CRTO · CCSP · CISSP · CREST CRT
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OWASP LLM alignment
Findings mapped to the OWASP Top 10 for LLM Applications and standard OWASP Web / API categories for the surrounding service
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Senior-led
Every engagement led end-to-end by a senior tester — no subcontractors, no junior handoffs
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Retest included
Retest of reported findings is included in scope at no extra cost
FAQ
AI / ML — common questions
Do you only test the LLM, or the whole product around it?
Both, depending on scope. Most engagements cover the model surface (prompt injection, RAG, tool use) and extend into the surrounding app and API — which is usually where real impact lives.
Are you aligned to the OWASP Top 10 for LLM Applications?
Yes. Findings are mapped to the OWASP Top 10 for LLM Applications and to standard OWASP Web and API Top 10 categories where the surrounding service is in scope.
Can the report support NIST AI RMF or EU AI Act readiness?
Yes. We can frame findings in the language of the NIST AI RMF or EU AI Act high-risk system requirements on request, in addition to standard SOC 2 / ISO control mappings.
How do you test indirect prompt injection?
We craft adversarial documents, web pages, or email content the feature retrieves, then verify whether embedded instructions can override the model — using both common patterns and ones tuned to your prompt structure.
Do you cover open-source or self-hosted models?
Yes. Hosted or self-hosted, the attacker-reachable surface is the prompt, retrieval, tools, and surrounding service. That is what we test regardless of model provider.
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.