Technical Capability Brief
AI Red Teaming & Safety Testing
AI red teaming is adversarial testing against models directly for responsible AI (RAI) violations. The client provides access to a model - through an interface, API, or playground - and we systematically attempt to break its safety alignment. This is distinct from application security testing: the target is the model's behavior itself, not the application built around it.
What we test
Five attack surfaces
Jailbreaking and Policy Bypass
Systematic adversarial prompting to get the model to produce outputs that violate its safety guidelines. We go well beyond known public jailbreaks, building custom attack strategies tailored to the specific model's behavior patterns and safety architecture.
Harmful Content Generation
Testing across the full RAI harm taxonomy: toxicity, hate speech, violence, self-harm, sexual content, child safety, dangerous or illegal activity guidance, and domain-specific harms relevant to the model's intended use case.
Bias and Fairness Probing
Testing whether the model produces systematically different outputs across demographic groups, particularly in high-stakes contexts like hiring, lending, healthcare, or legal applications.
Hallucination and Misinformation
Evaluating the model's tendency to generate false but confident-sounding claims, fabricate citations or sources, or present speculation as fact, especially in domains where accuracy has real consequences.
System Prompt and Configuration Extraction
Attempting to get the model to reveal its system prompt, safety instructions, tool definitions, or other configuration details that the deployer intended to keep hidden.
How we approach it
Custom attack strategies at scale
We build custom attack strategies tailored to each model's specific behavior patterns. For engagements requiring scale, we build custom AI-vs-AI adversarial automation - using models to generate, mutate, and evaluate adversarial prompts against the target. This covers far more attack surface than manual testing alone while maintaining the creativity and adaptability that purely automated approaches lack.
Our testing goes well beyond known public jailbreaks and standardized benchmark sets. We develop novel attack strategies based on the model's observed responses, safety architecture, and intended use case.
What we find
Typical finding patterns
Safety Alignment Gaps at the Boundaries
Models typically handle obvious harmful requests well, but edge cases - ambiguous requests, domain-specific harms, multi-turn manipulation, and context-dependent safety decisions - reveal where alignment breaks down.
Inconsistent Refusal Behavior
The same harmful request may be refused in one phrasing but complied with in another, indicating weak generalization in safety training rather than robust alignment.
System Prompt Leakage
Models frequently reveal portions of their system prompts, safety instructions, or tool configurations when probed with the right techniques.
Domain-Specific Harm Gaps
Generic safety training often doesn't cover harms specific to the model's deployment context.
Why it matters
A regulatory expectation and a business necessity
As AI models are deployed in increasingly high-stakes contexts, the consequences of safety failures grow. RAI testing at scale - not just spot-checking with known jailbreaks, but systematic adversarial evaluation - is becoming a regulatory expectation and a business necessity.
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