AI Red Teaming
Every model, agent, and system you ship has a breaking point. We find it first, using the same instincts we’ve used to keep our clients’ games unbreakable for over two decades.
AI red teaming means deliberately attacking an AI model, agent, or system to find and fix the potential ways it can fail before being sent for real deployment. It matters more now than it did even a year ago: Stanford’s 2026 AI Index recorded 362 documented AI incidents in 2025, up 55% from the year before, and found that safety scores which look solid under normal conditions weaken considerably once a model faces a deliberate jailbreak attempt. The gap between how a system behaves under normal use and how it behaves under deliberate attack is exactly what red teaming exists to close.
For 28 years, Keywords Studios has been finding what breaks games before players ever get the chance. We bring that same adversarial instinct to AI, red teaming models, agents, and physical systems for frontier labs, foundation model developers, and embodied AI teams around the world.
Experts in Breaking Games, and Now AI
Testing a game properly means understanding how thousands of systems, assets, and player actions interact, then methodically finding every combination that breaks something before a player does. That discipline, built over 28 years of AAA QA, maps onto AI more directly than most people expect.
- Non-deterministic testing. Game AI, physics engines, and LLMs share the same core property: the same input can produce a different output each run. Game QA has spent decades built around finding failure in these types of systems, which is exactly the skillset non-deterministic AI models require.
- Edge case generation at scale. AAA launches require thousands of edge cases mapped against structured coverage matrices. A game played by tens of millions of people will surface every edge case eventually; the only question is whether QA finds it first or a player does. This same discipline is what finds the gaps other AI testing teams miss.
- Persona-based jailbreaking. Our writers and narrative designers build believable characters for a living, which makes them perfect for the roleplay attacks that slip past guardrails.
- Multi-agent dynamics. Testing chained agents uses the same skillset as testing multi-NPC systems in games, where the danger lies in the interaction between agents, rather than any single part.
- Sim2Real exploit thinking. Our simulation teams build VR training environments and tune physics and haptics for a living, so they know exactly how a virtual environment can misrepresent the real world, using this knowledge to design adversarial scenarios that expose it before a robot ships.
How an Engagement Runs
Every engagement follows the same six-stage model, whether it’s a new model shipping or an agent moving into production.
- Scoping and threat modeling. We define the attack surface and what "harm" means for your deployment.
Deliverable: a signed-off Threat Model and Risk Register. - Asset mapping and access. Structured access to APIs, agent configuration, and system prompts.
Deliverable: an Access Manifest and Environment Baseline. - Attack playbook design. A structured library of adversarial scenarios built on the OWASP LLM Top 10.
Deliverable: a Numbered Attack Playbook. - Adversarial execution. Scripted and freeform attacks run in parallel to find what the playbook didn’t predict.
Deliverable: a daily findings log. - Dataset curation and debrief. Every finding becomes a labeled, RLHF-ready adversarial example.
Deliverable: a Red Team Report and Adversarial Dataset. - Remediation support. We re-run the attacks that produced Critical findings to confirm they’re actually fixed.
Deliverable: a Validation Report.
Stage 3 builds its playbook from a specific taxonomy. We use the OWASP LLM Top 10 as the spine and add further categories for physical and agentic systems that don’t exist in standard frameworks yet. The table below shows ten of the categories that taxonomy covers and what’s actually at stake if each one goes untested.
| Category | What We’re Testing | Real-World Risk If Missed |
|---|---|---|
| Prompt Injection | Direct and indirect manipulation of model instructions via user input or retrieved content | An attacker can rewrite what the model does using content it was only supposed to read |
| Insecure Output Handling | Model outputs that trigger downstream harm: code execution, UI rendering, API calls | A model output can execute unintended code or triggers an unauthorized action |
| Training Data Poisoning | Whether the model exhibits biases or backdoors introduced at the data layer | A biased or backdoored model ships and won't be caught until it’s exploited |
| Excessive Agency | Agentic systems taking consequential real-world actions beyond intended scope | An agent can take a real action (such as a purchase, a message or a system change) that nobody approved |
| Sensitive Information Disclosure | Model leaking system prompts, training data, or PII through inference | Training data, prompts, or user PII can leak through the model’s own responses |
| Model Denial of Service | Inputs designed to consume disproportionate compute or degrade availability | A single crafted input can take the service down or makes it unusably slow |
| Overreliance / Hallucination | Confident false outputs in high-stakes contexts: medical, legal, physical | A confident, false answer can get used to make a safety-critical decision |
| RAG Poisoning | Injecting adversarial content into retrieval corpora to corrupt grounded responses | A model’s answers can be quietly corrupted by planted content it retrieves and trusts |
| Multi-Agent Trust Exploitation | One agent in a pipeline manipulating instructions passed to downstream agents | One compromised agent can pass bad instructions on, which is trusted by every agent after them |
| Physical AI Sim2Real Attacks | Simulation inputs that cause well-performing virtual models to fail in real environments | A robot that performed perfectly in simulation can fail or cause harm in the real world |
A finding moves from red teaming into evaluation once it clears three bars: a confirmed reproduction case, a severity rating that has passed inter-annotator agreement review, and a labeled adversarial example mapped to the client’s harm taxonomy. Once it clears those, evaluation takes over, tracking it as a permanent regression test rather than a one-time finding.
Finding What Doesn’t Trigger an Alert
Automated monitoring catches what it's built to catch, but can't find a failure mode nobody's thought to test for, which is exactly the gap red teaming is designed to close.
In one agentic AI engagement our red team found a single indirect prompt injection that silently altered an agent’s decisions for the rest of that session, with no visible sign anything had changed and no alerts being triggered. Left undetected, hundreds of moderation decisions could have been made under that compromised state before anyone noticed.
We flagged this finding as Critical before the formal report was issued to allow the client to react in real-time. The configuration was frozen the same day and we ran retrospective analysis to confirm that no attacks had yet happened in the wild.
The most dangerous red team findings are those which are quiet, persistent and slip quietly under the radar, resisting standard auditing.
Why AI Red Teaming Needs Humans In The Loop
Automated scanning tools help scale coverage and catch regressions fast, but they can’t yet find what they haven’t been told to look for. The attacks that matter most: persona-based jailbreaks, narrative manipulation, the kind of layered social engineering a good writer builds, these all still need a human driving them. An AI system can’t fully red-team itself for the same reason a story writer can’t catch every plot hole in their own script: it’s testing against its own blind spots.
Red teaming exists to make AI perform safely and predictably, not just cheaply, which is why we don’t sell AI red teaming as a way to cut headcount. A model that ships faster but hallucinates, leaks data, or behaves inconsistently under pressure isn’t more productive, it’s just moving the cost somewhere less visible. Human oversight is what keeps a model anchored to how it’s actually meant to behave in production, under real conditions, not just in a benchmark.
Testing That Adapts With You
Most of our red teaming clients are researchers running an iterative process, not a fixed spec. A data set gets swapped, an annotation approach changes, a new model version ships, and the scope of what needs testing shifts with it. That means the value of an engagement depends on how easily it can flex, not just how thorough the first pass is. We restructure attack playbooks and reallocate specialists as your model changes, week to week if needed, rather than locking a scope on day one and testing against it regardless of what’s actually shipped by the time the report lands. Our follow-the-sun model leverages specialists in different time zones across the globe, handing off to each other as the day ends, which means that our flexibility runs 24/7 rather than stopping when one team logs off.
Modern games aren’t static. A live-service title, a cross-platform launch, or a new piece of DLC each change the attack surface of any AI system built into the game, whether that’s an NPC dialogue system, a moderation agent, or a matchmaking model. AI models and agents share this same evolution over time, opening up new vulnerabilities with each update.
Red teaming that only happens once, before launch, misses everything that changes after. We run red teaming as an ongoing service for exactly that reason, re-testing as the game, and the model inside it, keeps evolving.
Multilingual and Voice-Based AI Red Teaming
Most AI red teaming happens only in English, which is a real gap for global businesses. Stanford’s 2026 AI Index found leading models lost close to half their accuracy on the same reasoning test once it was run in a regional dialect instead of the standard language, with the gap getting wider the further a language sits from the model’s main training data.
- Multilingual and dialect-aware testing. With native audio delivery in 50+ languages and dialects, we test the same attacks in the languages and cultures your users actually use. This runs on the same production-scale localization infrastructure we already operate for live game titles: native-language pipelines at industrial scale, not a translated add-on.
- Voice and audio red teaming. Our voice actor network and audio pipeline let us test speech-to-text mishearing and voice-based social engineering directly, rather than relying on simulating it from text.
- Physical and embodied AI. Our simulation teams design adversarial scenarios in Unreal and Nvidia Omniverse/Isaac Sim to expose Sim2Real failures before a robot ships.
Built on 28 Years of Protecting Unreleased IP
We don’t build or own AI models, so client data is never used to train anything outside your engagement, and nothing is ever shared across clients. This dedication to IP security, proven across 28 years of working with the world's biggest AAA game studios, removes the danger of cross-tenant data leakage, one of the most commonly cited risks in enterprise AI testing.
Our studios participate in industry and client assurance programmes such as ISO 27001. We are a European-owned company operating under GDPR, at a time when Stanford’s 2026 AI Index found that GDPR is still the most cited regulatory influence on AI governance globally, ahead of frameworks like the NIST AI Risk Management Framework and ISO/IEC 42001.
For 28 years, the world’s largest game studios have trusted us with highly sensitive, unreleased IP ahead of major launches. That experience has shaped the controls, processes and culture we rely on to minimise the risk of unauthorised disclosure.
How We Compare to an Internal Team
Coming in without institutional knowledge of how a system was built is an advantage in red teaming: it means we test areas an internal team wouldn’t think to check, because they understand the system too well to suspect them. The strongest setups usually combine both an internal team’s domain knowledge alongside an external team’s distance from it.
Here’s how an external AI red testing team can add value to your internal team:
| Dimension | Internal Team | Keywords Studios Red Team |
|---|---|---|
| Severity calibration | Intuitive, inconsistent across testers | IAA-validated, taxonomy-anchored |
| Coverage accountability | Findings-only reporting | Full coverage matrix |
| Attack surface breadth | Shaped by system knowledge | Unconstrained by design intent |
| Adversarial dataset output | Rarely formalized | RLHF-ready, labeled, reproducible |
| Re-test / validation | Often informal | Structured validation report |
| Gaming-specific vectors | Absent | Embedded in taxonomy |
What You Need to Bring
A red teaming engagement is only as good as what it’s allowed to test. Here are five things you need to provide to make the difference.
- Real access, not a demo. Sanitized environments produce sanitized findings.
- A defined risk taxonomy. Even a one-page version changes everything.
- Model context. Share what you already know so we can focus on what you don’t.
- A named owner. Someone who can act on Critical findings.
- Commitment to the loop. Findings should feed your RLHF pipeline, not sit in a PDF.
End-to-End AI Services, From Data to Deployment
Red teaming rarely happens in isolation. The same project usually needs annotated training data, synthetic environments to generate edge cases, evaluation to track fixes over time, and governance to keep it all defensible. Often these services are sourced from different providers, with different contracts to manage for each.
We cover the full AI pipeline in-house: data annotation, synthetic data and simulation, training and model tuning, red teaming, evaluation, localization, and governance, all under a single contract. That's a large part of why teams that come to us for red teaming tend to stay for the rest of the pipeline.
Service Overview
Trusted by 24 out of the top 25 gaming companies in the world and behind 76% of last year’s Game Awards winners, we bring decades of experience finding failure in unpredictable systems to some of the most demanding AI deployments in the world.
Talk to Our AI Solutions Team
Get in touch to scope an engagement, or see what we’re already shipping for frontier AI labs and embodied AI teams worldwide.
Frequently Asked Questions
What is AI red teaming?
AI red teaming is the process of deliberately attacking an AI model or agent to find failure modes before real attackers or a live deployment do. Keywords Studios runs this as a structured six-stage engagement, from threat modeling through to remediation validation, rather than a single testing pass.
How is AI red teaming different from AI testing?
AI testing checks a model against known criteria, whilst AI red teaming looks for the failure modes nobody's thought to check yet. A finding only moves from red teaming into evaluation once it has a confirmed reproduction case and a severity rating validated through inter-annotator agreement.
How is AI red teaming different from traditional cybersecurity red teaming?
Traditional cybersecurity red teaming attacks infrastructure like networks and servers, while AI red teaming attacks model behavior itself (which is probabilistic, so findings need statistical proof rather than a single failed test). Keywords Studios approaches this using the same discipline of gaming QA teams built testing non-deterministic systems like AI opponents and physics engines, rather than conventional penetration-testing methodology.
Does red teaming need to cover multiple languages?
Yes. AI red teaming that only tests English misses real vulnerabilities, since the same attack can behave completely differently once it’s translated into another language or dialect.
Does AI red teaming apply to physical AI?
Yes. Physical and embodied AI systems trained in simulation can fail against real-world conditions the simulation never captured, which is what Sim2Real-focused red teaming is designed to catch.
What happens to the data we provide for use in red teaming?
Keywords Studios doesn't build or own its own AI models, so there's no separate system for client data to ever feed into and nothing is shared across client engagements. Client data provided to us for use in an AI red teaming engagement is never used to train external models.
