This llm red teaming guide walks through the attack categories worth probing, how a red team engagement is scoped, and what a useful findings report looks like. The goal is simple: find the failures before your users do.
We grade outputs. We do not train models, run fine-tuning, or sell experts we cannot verify.
Red teaming is structured adversarial probing of an AI system: deliberately trying to make it fail, leak, or misbehave, then documenting what worked. It borrows the name from security practice, but the target here is model behavior, not network perimeters.
A good red team thinks like an attacker and a confused user at the same time. Some probes are malicious, like prompt injection. Others are ordinary, like a user pasting a document that happens to contain instructions. Both break systems in production.
The output is a findings report: each successful attack documented with reproduction steps, a severity, and a recommended fix. Our red teaming service follows this exact shape.
These four cover most of what breaks LLM apps in practice. A red team engagement should touch all of them, not just the famous one.
Attacker text smuggled inside pasted content, uploaded documents, or tool outputs overrides your system instructions. The model follows the injected instruction because it cannot reliably tell data apart from commands. This is the most common real-world attack on LLM apps.
Roleplay scenarios, hypothetical framing, encoded or translated requests, and multi-turn setups that talk the model out of its refusals. Guardrails that hold against direct requests often fold against a patient second or third turn.
Probing for system prompts, configuration details, or fragments the model should not reveal. In multi-tenant setups, testers also check whether one user can reach another user's context through cleverly phrased requests.
Disallowed advice, biased or discriminatory decisions, and harmful instructions delivered in a confident, helpful tone. The danger is not just that the model complies, but that it complies fluently enough that nobody notices.
You tell us what the app does and what is off limits. Scope is written down before any probing starts.
Structured attack attempts across the categories above, each one documented as it happens.
Every successful attack with reproduction steps, a severity, and a recommended fix, plus a walkthrough call.
Red teaming starts with boundaries, written down. Which endpoints we can hit, what data we can use, which attack categories are in scope, and what is explicitly off limits. Scoping protects both sides: you know exactly what will be tested, and we know exactly where to stop.
We scope against a staging or test deployment whenever one exists. If only production is available, we agree on rate limits and data handling up front so probing never disrupts real users.
We probe and report. We attempt the attacks, document what works, and hand you reproduction steps and fixes. What we do not do: exploit live systems beyond the agreed scope, exfiltrate real user data, or leave anything running that you did not ask for.
Every finding stays in the report. There is no public disclosure and no drama, just a clear list of what broke and how to fix it.
A useful finding has four parts: the attack category, the exact input that triggered it, the severity with a reason, and a concrete fix. Vague findings like "the model can be jailbroken" help nobody. Reproducible findings get fixed.
Findings are ranked so your team knows where to start. A P0 prompt injection that leaks system instructions outranks a P2 tone issue every time. Our severity scale keeps the ranking consistent across engagements.
A pentest targets your infrastructure: servers, APIs, authentication. Red teaming an LLM app targets model behavior: what the model can be talked into saying or doing. You need both, but one does not cover the other. A perfectly secured API can still serve a model that leaks its system prompt.
No. A staging or test deployment is better, because we can probe freely without worrying about real users. If only production exists, we agree on strict rate limits and data handling rules before anything starts.
No. Probing is read-only from your side: we send inputs and read outputs. We do not modify data, escalate privileges, or touch infrastructure. The scope document lists exactly what we will and will not do, and we stay inside it.
It depends on scope: the number of endpoints, the attack categories in scope, and how deep you want us to go. A focused engagement on one app is measured in days of probing, not months. We agree on the shape of the work before we start.
A findings report with every successful attack documented: reproduction steps, severity, and a recommended fix for each one. Plus a walkthrough call where we go through the findings with your team and answer questions. The report is written for engineers, not for marketing.
Before, and then again after any big change. Pre-launch probing catches the embarrassing failures while they are still cheap to fix. Post-change probing catches regressions in your guardrails. Most teams red team at launch and repeat when the model, prompts, or tools change.
Tell us what your app does and we will scope a probing engagement around it.
Not sure where to start? Talk to us and we will point you at the right evaluation.