Products / Red Team Lab
DNLA Red Team Lab
An adversarial testing lab that deliberately tries to make your AI system fail, before a user, a customer, or an attacker does it for you. It runs as a core part of every QAi Health Check, and is also available as a standalone engagement.
Why it matters
Passing the happy path proves almost nothing
Most AI systems are tested the way they're demoed: a handful of clean, well-behaved questions, asked once, in the expected language, with no adversarial intent. Production doesn't work that way. Real users ask contradictory questions, paste in malicious instructions, push the system past its intended scope, and switch languages mid-conversation, and a system that has never been tested against that pressure has an unknown failure surface, not a safe one. Red Team Lab exists to find that surface on purpose, under controlled conditions, before it gets found in production.
What we test for
Failure modes, matched to the system type
Generative and classical systems fail differently, so the attack surface is scoped to match.
Generative AI systems
LLM-based chatbots, RAG systems, and autonomous agents.
- Hallucination
- Information leakage
- Instruction bypass
- Prompt injection
- Unauthorized tool use
- Irreversible actions
- Reliance on incorrect sources
- Bias
- Inconsistency
- Failure across languages
- Behavior under conflicting information
Classical ML systems
Predictive, scoring, and classification models.
- Drift
- Leakage
- Robustness
- Imbalance
- Calibration
- Sensitivity
- Failure across population groups or scenarios
How it runs
From scoping to a fix-priority report
- Scope the target: which systems, which environments, which actions are in and out of bounds
- Design attack scenarios specific to the system's actual failure surface, not a generic checklist
- Run adversarial sessions against the live system, manually and with tooling
- Log every attempt, every response, and every point where a boundary held or gave way
- Rank findings by severity and business impact, not by raw count
- Deliver a fix-priority report the engineering team can act on directly
Why it's different
The deliverable isn't a bug count
Running a system against a checklist of attacks is easy. The hard part (and the actual value) is turning "we found 73 issues" into something a leadership team can act on: a severity ranking, the business impact of each finding, and exactly what it will take to fix it.
Who it's for
- Teams shipping a chatbot, RAG system, or agent to real customers for the first time
- Teams that changed model, prompt, or tool permissions and need to know what broke
- Regulated or high-trust environments where a single bad output has outsized consequences
- Anyone who has only ever tested the happy path
Want to know how your system actually breaks?
Standalone, or bundled into a QAi Health Check.