AI Red Teaming & Safety Evaluation
Human-led adversarial testing for large language models and AI systems. Our red-teamers deliberately probe a model for safety failures, policy violations and harmful outputs — before real users do.
What AI red teaming means at Digitive
Red teaming is the practice of deliberately probing an AI model to find safety failures, policy violations, harmful outputs and adversarial vulnerabilities before they reach real users. Our specialists act as adversarial testers — thinking like bad actors to surface weaknesses a model's own developers cannot easily anticipate from the inside.
It is some of the most sensitive work in AI safety, and we treat it that way: structured and delivered under the same governance and security approach as our evaluation work.
Why human adversarial evaluation matters
Automated classifiers and benchmark suites catch the obvious cases. The failures that reach production are usually the ones that require human creativity to find — coded language, multi-turn manipulation, cross-lingual jailbreaks and domain-specific harms in specialist fields.
Human red-teamers who understand these domains find those gaps, explain why they happen, and give your team something it can act on.
What we red-team
Coverage across the risks that matter for enterprise and safety-critical AI deployments.
Jailbreaks & prompt injection
Roleplay exploits, hypothetical framings, encoded text, multi-turn manipulation and instruction-override attempts against your safety guardrails.
Harmful-content elicitation
Structured probing for unsafe instructions and disallowed outputs, with severity-ranked findings for every successful elicitation.
Bias & fairness probing
Systematic testing for demographic bias, stereotyping and discriminatory outputs across protected attributes.
Factuality stress-testing
Adversarial prompts designed to trigger confident-but-false answers, reviewed with domain-focused fact-checking.
Toxicity & hate-speech evaluation
Detection of subtle toxic output — coded language, dog-whistles and culturally specific terms automated classifiers miss.
Political & ideological bias
Assessing whether a model amplifies, suppresses or distorts viewpoints — important for media, civic and election-adjacent use.
Agentic safety
For AI that takes actions: testing whether an agent can be manipulated into harmful, unauthorised or unintended behaviour.
Multilingual safety evaluation
Running adversarial tests across many languages, where safety guardrails built in English routinely break down.
How we work
Severity-ranked, reproducible findings
Findings are delivered with a reproducible test case, transcript, severity rating and remediation guidance — not just a vulnerability list.
Calibrated to your policy taxonomy
Reviewers calibrate against a structured rubric mapped to your own harm categories, so results plug into your existing eval and review process.
Human adversaries, not just automated tools
Adversarial testing is performed by people who can apply subject-matter knowledge where a domain lens matters, rather than relying on automated classifiers alone.
We take contributor wellbeing seriously
Adversarial testing means deliberately exposing people to harmful, disturbing and policy-violating content. We treat that as a serious responsibility. Responsible red-team operations should protect the people doing the work — through practices such as managing exposure, rotating people off sensitive material, letting them step away from a task, and providing access to wellbeing support.
We design our red-team engagements with these principles in mind, so the focus is on protecting the people involved, not only the model being tested.
2,300+ adversarial prompts · 40+ languages · 118 critical findings
Frontier-model red team for a policy-critical AI deployment
Structured adversarial testing across jailbreaks, prompt injection, and policy stress — with severity-ranked findings the safety team could act on.
Part of a full evaluation stack
Red teaming works best alongside broader model evaluation. Many teams pair adversarial safety testing with LLM output evaluation, human feedback and RLHF, factuality checks and multilingual review — all available through the same managed team model.
Find your model's weaknesses first
Start with a scoped red-team pilot, then scale into a managed red-team pod with continuous adversarial testing and regular reporting.