Red-Teaming LLMs
A structured methodology for probing language model safety boundaries before adversaries do.
Taxonomy of Attack Surfaces
Every LLM deployment exposes distinct failure modes. Categorize your probes across five axes: prompt injection, jailbreaking, data extraction, agent tool misuse, and multimodal bypass.
Adversarial Prompt Design
Craft prompts that exploit role confusion, encoding tricks, and multi-turn state corruption. Test base64 obfuscation, token smuggling via Unicode homoglyphs, and recursive self-reference patterns that degrade alignment guardrails.
Automated Fuzzing Pipeline
Build a harness that mutates seed prompts through synonym substitution, persona injection, and context-window flooding. Log every response for later triage — flag outputs containing PII leakage, policy violations, or tool-call side effects.
Evaluation Rubric
Score each finding on severity (informational → critical), reproducibility, and attack surface category. Maintain a living threat model that maps discovered weaknesses to concrete mitigations: input sanitization, output classifiers, and least-privilege tool scoping.
Continuous Red-Teaming
Model updates and fine-tuning runs introduce regression risk. Schedule weekly automated red-team sweeps and gate production deploys on a clean evaluation report. Treat your prompt corpus as a security asset — version it alongside your model weights.
Meridian note: This recipe pairs with our LLM security monitoring module. See LLM Guard for runtime enforcement.