How to Red-Team Your LLM Application

What does red-teaming an LLM app involve?

Red-teaming an LLM app means deliberately attacking your own system to find where it breaks before a real adversary does — probing for jailbreaks, data leaks, prompt injection, and harmful outputs in a structured, repeatable way. It is the offensive counterpart to building defences: you only know your guardrails hold once someone has genuinely tried to get past them. For LLM systems this is essential, because their failure modes are unintuitive and standard QA rarely surfaces them.

The difference between red-teaming and ordinary testing is intent. Functional tests check that the app does what it should for cooperative users. Red-teaming checks what a hostile, creative user can make it do. Both matter, but only one finds the jailbreak before your users post it online.

Map what you are trying to protect

Effective red-teaming starts with goals, not random prompts. Decide what would actually constitute a failure for your app, because that defines your attack objectives.

• Can the model be made to ignore its safety rules and produce content it should refuse?
• Can it be tricked into revealing its system prompt, secrets, or another user's data?
• Can injected instructions in retrieved content hijack a tool or action?
• Can it be steered into harmful, biased, or off-brand responses that damage you?

Writing these objectives down first keeps the exercise focused. Without them you generate a pile of clever prompts and no clear sense of whether any of them represent a real risk to your business.

Cover the main attack categories

Work through the known failure classes systematically rather than improvising. Jailbreaks use role-play, hypotheticals, or encoding tricks to bypass refusals. Prompt injection — direct and indirect — attempts to override instructions, and indirect injection through documents or web content is the higher-stakes variant for agentic apps. Data extraction probes whether the system prompt, training data, or other users' information can be coaxed out. Tool and action abuse tests whether the model can be made to call functions in unintended ways. The OWASP Top 10 for LLM Applications is a solid checklist to make sure you are not missing a category that an attacker will happily find for you.

If you have never tried to jailbreak your own app, assume it is jailbreakable. The only question is whether you find out first or your users do.

Combine manual and automated attacks

You need both. Manual red-teaming brings human creativity — the unexpected phrasings and multi-turn manipulations that automated tools miss — and is where the most interesting failures usually surface. Automated red-teaming scales: run a large library of known attack patterns on every release so regressions get caught immediately. Use both, and treat your growing collection of successful attacks as a regression suite that runs forever, so a vulnerability you fixed last month cannot quietly return in next month's release.

Turn findings into fixes and repeat

A red-team finding is only useful if it changes the system. For each successful attack, record what happened, judge its severity, and decide the response — tightening a guardrail, scoping a tool more narrowly, adding an output check, or accepting a documented low risk. Re-test after every fix to confirm it holds and did not break something else. Red-teaming is not a one-off audit; it is an ongoing practice that runs with each meaningful change, because new prompts and new models open new gaps that yesterday's testing never covered.

Prefer it handled for you?

Running a thorough red-team — mapping objectives, covering every attack class, and building a lasting regression suite — is specialist work. talk to BSH Technologies and let our cybersecurity services stress-test your LLM app and harden it against the attacks that matter.