LLM red teaming is where a working AI feature meets adversarial use. The model may pass a demo, unit tests, and provider safety checks, but the product still has trust boundaries: user input, retrieved content, tool calls, system prompts, business rules, output parsers, auth, logs, and tenant data. We test that whole system, not just the model endpoint.
This service is for teams that need proof before a launch, procurement review, or enterprise security questionnaire. If the wider question is “is the whole AI product production-ready?”, start with the AI Product Readiness Assessment. If the immediate question is “can this LLM feature be manipulated?”, this is the narrower service.
Who it is for
You have an LLM feature in a product that real users or buyers will touch: a support assistant, analyst copilot, workflow agent, internal knowledge assistant, code generator, document reviewer, sales assistant, or any UI that routes user input through a model. You need to know whether a malicious user, tenant, document, or downstream integration can make it ignore instructions, leak data, call a tool, or produce output your product should not trust.
Typical triggers:
- A buyer asks for AI red-team evidence during procurement.
- You are about to give an LLM access to tools, files, messages, CRM records, payments, tickets, or internal data.
- You have guardrails but no independent evidence that they hold.
- A model, prompt, or orchestration change made old assumptions unreliable.
- Your team needs a remediation plan, not a scanner-style score.
What we test
Direct prompt injection and jailbreaks
We test whether a user can override the system prompt, bypass refusal rules, force hidden instruction disclosure, weaken policy boundaries, or steer the model into a response path your product treats as trusted. These tests map to the OWASP LLM Top 10, especially LLM01 Prompt Injection and LLM02 Sensitive Information Disclosure.
Indirect prompt injection
Most serious LLM failures do not come from a user typing “ignore previous instructions” into a chat box. They come from untrusted content that the system retrieves or imports: documents, web pages, support tickets, emails, issue comments, database rows, Slack messages, or knowledge-base articles. We test whether that content crosses into the instruction channel and changes model behavior.
Tool and function abuse
If the model can call tools, we test whether an attacker can trigger unauthorized actions, over-broad queries, destructive operations, or business-rule bypasses. We look at tool descriptions, argument schemas, permission checks, confirmation gates, rate limits, and whether the model can satisfy its own approval requirements.
Data leakage and tenant isolation
We test whether prompts, retrieved context, memory, logs, cache state, vector metadata, or tool responses can leak another user or tenant’s information. In multi-tenant products this is usually the finding that matters most to a buyer.
Output integrity
Many LLM products pass model output into parsers, workflows, or UI. We test structured-output validity, injection into markdown/HTML/links, unsafe citations, hallucinated references, and whether downstream code trusts text that should be treated as untrusted.
What you get
- A scope note that names the tested product surfaces, accounts, tools, and data classes.
- A threat model for the LLM feature and its trust boundaries.
- Reproducible findings with request traces, not screenshots.
- Framework mapping to OWASP LLM Top 10, MITRE ATLAS, and the agentic controls that apply.
- A prioritized fix plan your engineers can run.
- One retest pass for the confirmed fixes.
The report follows the same structure shown in our sample AI assessment report: severity, reproduction, evidence, business impact, remediation, and retest status.
How this differs from a normal appsec test
A traditional web app pentest is still valuable, but it usually treats the model as an opaque endpoint. LLM red teaming treats the model as part of an application architecture: prompts, retrieval, tools, permissions, memory, evals, and business workflow. The failure mode is often not “SQL injection in a route.” It is “customer-authored content became an instruction,” or “the model called a refund tool because retrieved context told it to.”
That distinction changes the test plan. We still care about auth, tenancy, logging, and rate limits, but we test them through the model’s behavior and the product paths that depend on it.
When to choose the broader assessment
Choose the AI Product Readiness Assessment when you need LLM red teaming plus evals, production-readiness review, auth and tenant audit, fix planning, and a buyer-facing report in one engagement. Choose this service when the LLM attack surface is the urgent question and the environment is already otherwise well understood.
Start by reading the LLM red-teaming guide and the prompt injection testing guide, then scope the service against your actual workflow. The operating rules for access, evidence handling, confidentiality, and retest are on trust and data handling; the public verification profile collects the proof links a buyer can inspect before signing.