Red-Team Your LLM Before Your Users Do

Structured adversarial evaluation of your LLM or AI agent — hallucination rate, prompt injection, jailbreak surface, and safety scoring — delivered as an audit-grade report.

Duration: 5–7 days Team: 2 AI QA Engineers (LLM specialisation)

The Challenge

You might be experiencing...

Your LLM app is in production and users have found prompt injections or jailbreaks you didn't know existed.

An enterprise prospect has asked for a hallucination rate benchmark and safety evaluation before signing — and you don't have one.

You are shipping an AI agent that takes actions in the real world and you have no systematic evaluation of its failure modes.

You fine-tuned a base model and want independent verification that the fine-tuning didn't introduce new safety issues.

LLM red-teaming is the practice of systematically finding ways your language model can be made to behave badly — before your users, customers, or adversaries find them for you.

Why Standard Testing Misses LLM Vulnerabilities

Traditional software testing is deterministic: given input A, output B. LLMs are probabilistic: a prompt that fails today may succeed tomorrow with minor rephrasing. Standard QA teams test for bugs in code; they are not equipped to test for hallucination, prompt injection, jailbreak paths, or emergent safety failures in language models.

OWASP LLM Top 10 catalogues the most critical vulnerability categories for LLM applications — prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain vulnerabilities. Our red-team evaluation covers all 10 categories with test cases designed for your specific deployment context.

Hallucination: The Risk Your Customers Notice First

Hallucination — generating factually incorrect or ungrounded content with apparent confidence — is the most visible LLM failure mode. For AI products in high-stakes domains (legal research, medical information, financial advice, code generation), a hallucination rate above 5% is typically disqualifying for enterprise procurement.

We benchmark your LLM’s hallucination rate against a domain-specific evaluation set, compare it to baseline models, and identify the query categories where hallucination risk is highest. This benchmark becomes a specification: remediate until hallucination rate is below your acceptable threshold.

Our Approach

Engagement Phases

Day 1

Threat Modelling & Evaluation Design

Map your LLM's attack surface: input modalities, user roles, system prompt architecture, tool use surface, and deployment context. Design evaluation sets for hallucination, prompt injection, jailbreak, and safety scoring specific to your use case.

Days 2–4

Adversarial Evaluation

Execute structured red-team evaluation across 4 dimensions: hallucination rate on domain-specific queries, prompt injection resistance, jailbreak surface mapping, and safety policy compliance. Each test case is documented with reproduction steps.

Days 5–7

Scoring & Report

Score findings against OWASP LLM Top 10 and custom rubric. Produce prioritised vulnerability list with severity ratings, reproduction steps, and specific remediation guidance for each finding.

What You Get

Deliverables

Hallucination Rate Report — domain-specific benchmark with methodology documentation

Prompt Injection Vulnerability Assessment — all injection vectors tested, severity-rated

Jailbreak Surface Map — with specific prompts, bypass techniques found, and remediation

Safety Policy Compliance Score — against your stated safety requirements

Prioritised Fix List — all findings ranked Critical / High / Medium / Low with remediation steps

Executive Summary — 1-page suitable for enterprise customer or investor review

Expected Outcomes

Before & After

Metric	Before	After
Hallucination Rate	Unknown — no systematic benchmark	Measured hallucination rate with 95% confidence interval on domain-specific eval set
Prompt Injection Surface	Unknown — no structured adversarial testing	All injection vectors mapped, severity-rated, and remediation steps provided
Enterprise Procurement	Lost deal — no safety evaluation documentation	Passed enterprise security review with aiml.qa red-team report as evidence

Technology

Tools We Use

OWASP LLM Top 10 Garak / Promptfoo Custom adversarial prompt libraries RAGAS / DeepEval

Common Questions

Frequently Asked Questions

What LLMs and architectures do you evaluate?

We evaluate any LLM-based system — OpenAI API integrations, open-source models (Llama, Mistral, Falcon), fine-tuned models, RAG architectures, and multi-agent systems. The evaluation framework adapts to your architecture. For RAG systems, we additionally evaluate retrieval quality and context injection risks specific to RAG attack vectors.

Do we need to share our system prompt?

For a thorough red-team evaluation, yes — we treat your system prompt as confidential under NDA. Without seeing the system prompt, we can still run black-box adversarial testing, but white-box testing (which examines the system prompt for injection vulnerabilities and structural weaknesses) produces significantly higher-quality findings.

How do you measure hallucination rate?

We construct a domain-specific evaluation set of 100–200 queries with known ground-truth answers in your use case domain, then measure hallucination rate as the proportion of responses containing factually incorrect or ungrounded claims. We report overall hallucination rate, rate by query category, and compare against baseline (GPT-4o or equivalent) as a benchmark. Methodology is documented in the report so you can re-run the evaluation after remediation.

Can you evaluate AI agents with tool use?

Yes. Multi-agent and tool-use systems require additional evaluation dimensions: tool call injection (manipulating the model into calling tools with attacker-controlled parameters), indirect prompt injection via tool outputs, and goal-hijacking in multi-step agent flows. These are among the highest-severity findings in AI agent evaluations and are not covered by standard LLM red-teaming frameworks.

Ship AI You Can Trust.

Book a free 30-minute AI QA scope call with our experts. We review your model, data pipeline, or AI product — and show you exactly what to test before you ship.

Talk to an Expert