LLM Red-Teaming at Scale
A methodology for finding the failure modes in a production LLM pipeline before someone else does.
Most LLM red-teaming is a few hours of someone running jailbreak prompts, writing down what broke, and calling it done. That produces a list. It doesn't produce a system for catching regressions.
A production red-teaming program has four parts that actually work together.
The threat model first
LLM systems have an attack surface that doesn't exist in traditional software: the attack is against the output distribution, not the memory layout. This changes everything about what you're defending.
Prompt injection — adversarial instructions embedded in the input. The model follows them even when they contradict the system prompt.
Jailbreaking — structured inputs designed to bypass safety training. Effective jailbreaks exploit how safety training generalizes, not how it's implemented.
Hallucination amplification — inputs tuned to maximize the model's confidence in wrong outputs.
Context poisoning — adversarial content in the retrieval corpus. The model retrieves false information and treats it as ground truth.
Each requires different defenses. You can't have one mitigiation strategy that covers all four.
The evaluation harness
The harness runs 200+ prompt variants against the full pipeline per evaluation run. It's triggered by any model update or significant prompt change — not run ad hoc.
Prompt variants come from templates with adversarial perturbations: character-level typos, encoding tricks, language mixing, context window manipulation. The goal is to test generalization rather than recall of known attack patterns.
Outputs are scored across six dimensions: correctness, safety, instruction following, coherence, hallucination, and latency.
Statistical significance
One run is not a finding. Each variant runs 20 times with temperature variation. Failure rates are reported with 95% confidence intervals.
This matters because LLM outputs are non-deterministic. A single run tells you almost nothing. A 20-run sample with a confidence interval tells you whether the failure rate is 0.1% or 5% — which is the difference between "acceptable" and "a problem."
The regression pipeline
The harness runs in CI on every pull request that changes the model or the prompt. A new model version that increases any failure rate by more than 2 percentage points blocks the merge.
This is the part most teams skip. It's also the most important. Without it, you catch failures in production instead of in CI. With it, a safety regression in the model you shipped last Tuesday gets caught before anyone ships the model you shipped today.