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 you are budgeting the engagement first, AI security audit cost explains what pushes LLM red-team pricing up or down. 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.
Is LLM red teaming more than jailbreak testing?
Yes. Jailbreak testing asks whether a user can push a model past an instruction or refusal. LLM application red teaming also tests whether untrusted users, content, and integrations can cross a product trust boundary:
| Boundary | Test question | Evidence captured |
|---|---|---|
| User → model | Can direct input override policy or expose hidden context? | Exact input, assembled model request, and output |
| Document → retrieval → model | Can untrusted content become an instruction or cross tenant scope? | Source, retrieval result, assembled context, and authorization state |
| Model → tool | Can model output trigger an unauthorized read, write, message, or payment? | Tool schema, arguments, caller identity, policy decision, and result |
| Model → application | Can generated text break a parser, UI, citation, or business rule? | Raw output, consuming code path, and observed effect |
| Fix → retest | Does the same attack path fail after remediation? | Original reproduction replayed against the fixed version |
A prompt list alone does not establish application impact. A confirmed finding needs the affected boundary, observable behavior, reproduction evidence, and a result tied to the tested version.
Need the real workflow tested?
Scope the LLM application boundary.
Bring the prompts, retrieval, tools, roles, and launch or buyer deadline. The scope stays fixed and every confirmed finding must be replayable.
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 the engagement runs
- Scope and rules of engagement. We name the target, boundaries, allowed tests, escalation path, and evidence-handling rules.
- Environment and access validation. We confirm the agreed version, representative accounts and data, logging, and safe test path before adversarial work starts.
- Adversarial testing. We run the agreed injection, leakage, tool-abuse, retrieval, and output-integrity cases against the real workflow.
- Immediate critical-finding escalation. A confirmed critical issue is raised through the agreed contact path; it is not held for the final report.
- Evidence review and reporting. Each confirmed finding receives reproduction steps, traces, severity, business impact, and remediation guidance.
- Fix walkthrough. We review the evidence and fix order with the team that will make the changes.
- One agreed retest pass. We rerun the confirmed cases after fixes and record the result.
The written scope names the application, model, prompts, retrieval sources, tools, accounts, data classes, and environments tested. Unlisted systems and future model, prompt, retrieval, or tool changes remain outside the result. The written scope sets the schedule; any agreed clock begins only after target access and rules of engagement are in place.
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.