Adversarial Prompt Library
Curated set of adversarial prompts across harm categories — jailbreaks, policy evasions, and capability probing. All human-authored, not template-generated.
Use Case
LLM Evaluation
& Red-Teaming
Safety gaps, policy violations, and capability blind spots don't reveal themselves through automated benchmarks alone. Human adversarial testing — structured, repeatable, multilingual — is the layer that finds what models try to hide.
The Problem
Benchmarks don't find the dangerous edges
Automated evals measure what you already knew to measure. Human red-teamers find the adversarial patterns, cultural edge cases, and multi-turn jailbreaks that static test suites miss — especially in non-English languages.
Non-English safety gaps
Models trained primarily on English data often have significantly weaker safety alignment in other languages. Human native-speaker red-teamers surface these gaps — automated evals rarely catch them.
Multi-turn jailbreaks
Single-turn evals miss attacks that build over multiple exchanges. Experienced human red-teamers construct extended conversation sequences that gradually shift model behaviour.
Cultural harm patterns
Harmful content in culturally specific contexts — political sensitivities, religious edge cases, regional stereotypes — requires in-context human judgment, not pattern matching.
How Fuzu Atlas delivers LLM red-teaming
01
Threat Model Design
Work with your safety team to define attack categories, harm taxonomy, and priority domains before testing begins.
02
Red-Teamer Briefing
Expert red-teamers briefed on your model's intended use, known weak points, and the specific harm categories in scope.
03
Structured Testing
Adversarial prompts generated and logged. Each prompt tagged by attack type, language, severity, and reproduction reliability.
04
Report & Handoff
Structured report: vulnerability inventory, severity ratings, reproduction steps, and suggested mitigation categories.
What's included in an Fuzu Atlas red-teaming engagement
Multilingual Coverage
Same red-teaming protocol run in priority languages by native-speaker red-teamers. Direct comparison of safety behaviour across language coverage.
Domain Expert Red-Teamers
Medical, legal, and technical red-teamers for domain-specific harm discovery — misrepresentation, dangerous advice, and professional impersonation.
Multi-Turn Attack Sequences
Constructed multi-turn conversations designed to shift model behaviour across exchanges. Documented with full turn-by-turn transcripts.
Severity Classification
Every finding rated by severity, reproducibility, and breadth. Risk-ranked report delivered with your team's harm taxonomy applied consistently.
Ongoing Testing Programs
Regression testing between model versions. Continuous monitoring as new capabilities are added and safety posture drifts with fine-tuning.
Related solutions & industries
Find what your benchmarks miss
A structured red-teaming sprint — threat model, native-speaker coverage, and a full vulnerability report — in weeks.