Companies building AI products
It uncovers misuse and security risks before release, and gives customers and investors evidence of due care in development.
/ AI RED TEAMING
AI Red Teaming
AI systems do not fail the way conventional software does. AI Red Teaming attacks your models and AI-powered applications the way an adversary would, before someone else does it for real.
[ 01 ] / WHAT IT IS
What is AI Red Teaming, and how is it different from conventional testing?
In short
AI Red Teaming is structured, adversarial testing of AI systems. It covers security attacks (such as prompt injection, data leakage and model extraction) as well as misuse, reliability and harmful-output risks, so that weaknesses are found before deployment rather than after.
In traditional software, bugs are mostly deterministic: the same input triggers the same fault. AI systems are probabilistic. They take instructions in natural language, and their behavior can change with tiny variations in input. Ordinary functional and security tests are therefore not enough. You need methods that examine the model, and the system around it, with an adversarial mindset.
AI Red Teaming looks at two questions together. The first is security: can an attacker subvert the system, reach data, connected systems or tools? The second is misuse and reliability: can the system be pushed into harmful, misleading, biased or policy-violating output, does it leak sensitive information, and does it hold up under extreme input? Modern AI systems also increasingly act as agents: they call tools, access data and take actions, which raises the stakes considerably.
It follows the logic of red teaming: creative, objective-driven, attacker-minded work, adapted to how AI behaves. If your priority is the technical security of the application itself, an LLM penetration test offers a more tightly structured, security-focused assessment.
Frameworks and standards
- OWASP Top 10 for LLM Applications
- MITRE ATLAS
- NIST AI RMF
- EU AI Act
- ISO/IEC 42001
- OWASP AI Exchange
[ 02 ] / WHO IT'S FOR
Who benefits from AI Red Teaming?
Anyone building or deploying an AI system that touches real users, sensitive data or business processes.
Organizations rolling out chatbots or assistants
Customer-service, internal and coding assistants can reach company data and systems, so their potential for abuse has to be tested.
Teams building AI agents and automation
Agents take actions, so manipulating them can cause real harm. The test shows what they can be talked into doing.
Regulated industries
Finance, healthcare and the public sector may be expected to document risk assessment and testing, particularly under the EU AI Act.
Risk and compliance leaders
AI risk frameworks such as NIST AI RMF and ISO 42001 call for practical testing evidence.
Executives who need certainty
The test shows plainly what harm your AI system could do if someone deliberately misuses it, and how well your brand is protected.
[ 03 ] / WHAT'S INCLUDED
What AI Red Teaming covers
We tailor the scope to the type of system, its risk profile and its use case.
01
Threat modeling and risk profile
We establish who might abuse the system, how much harm they could do, and what data, tools and integrations the AI can reach.
02
Jailbreaks and policy bypass
We test whether safety rules and system instructions can be defeated through creative, multi-step or hidden attacks.
03
Prompt injection (direct and indirect)
We test whether users or external content (documents, web pages, emails) can carry hidden instructions that steer the model.
04
Data leakage and privacy
We probe for leakage of the system prompt, internal documents, training data and other users' information.
05
Agents and tool use
We check whether an agent can be pushed into unauthorized actions, excessive use of its permissions or dangerous tool calls.
06
Harmful, biased and misleading output
Against your policies and risks, we test whether harmful, biased, false or legally problematic responses can be provoked.
07
Model and data manipulation
We examine attacks on the training and fine-tuning pipeline, data sources, model files and supply chain, including data poisoning.
08
Reporting and defensive recommendations
You receive reproducible examples, risk ratings and concrete mitigations (filters, permissions, monitoring, design changes).
[ 04 ] / HOW IT WORKS
How an AI Red Teaming engagement runs
The work combines creative attacker thinking with structured documentation so that results can be reproduced and tracked.
Understanding the system
We learn the model, data sources, integrations, tools and user base, as well as your organization's risk appetite.
Attack scenarios
We design and rank the relevant misuse and attack scenarios, drawing on the OWASP LLM Top 10 and MITRE ATLAS.
Testing
We run manual and automated adversarial tests: creative hand-crafted attacks plus large test sets to measure how well the model resists.
Analysis and rating
We rate findings by reproducibility, impact and exploitability, and compare them with your policies.
Report and recommendations
You receive an executive summary, technical detail and prioritized mitigations.
Retest and ongoing testing
After fixes we retest, and recommend regular, automated regression testing after each model update.
[ 05 ] / WHAT IT SOLVES
What AI Red Teaming solves, and why it pays off
AI systems bring new kinds of risk: a well-phrased request can push an AI into an action nobody intended, or make it reveal confidential data. AI Red Teaming exposes these problems before launch, when they are still cheap to fix and before customers or the press find them.
It also prepares you for regulation. The EU AI Act and risk frameworks expect documented, risk-appropriate testing, particularly for high-risk systems. The report serves as evidence.
- early discovery of AI-specific attacks and misuse
- protection of sensitive data and connected systems
- protection of your brand and customer trust from harmful output
- preparation for EU AI Act and risk framework expectations
- a reproducible test suite for retesting after model changes
What you receive
- executive summary and risk overview
- threat model and attack scenarios
- reproducible findings with evidence
- risk ratings by impact and exploitability
- prioritized mitigations
- retest report and regression test suite
[ 06 ] / WHY QYNTAR
Why have Qyntar run your AI Red Teaming?
Because testing AI systems takes creative attacker thinking and a solid grounding in conventional security.
Attacker mindset
We bring the experience of classic red teaming and penetration testing to AI systems, so risks come out in a form that can really be exploited.
The whole system, not just the model
We examine the application, APIs, data sources and integrations around the model too, because most real damage comes from flaws there.
Built for regulation
Results map to OWASP, MITRE ATLAS and NIST AI RMF, so they support your compliance work as well.
Practical recommendations
The report is not a list of generic warnings. It contains specific, achievable mitigations.
[ 07 ] / FAQ
Frequently asked questions: AI Red Teaming
What is AI Red Teaming?
Adversarial, attacker-minded testing of AI systems that examines security attacks as well as misuse and reliability risks, so that weaknesses surface before deployment.
How is AI Red Teaming different from an LLM penetration test?
AI Red Teaming is broader: besides security it covers misuse, harmful output and reliability, in a creative, objective-driven way. An LLM penetration test focuses more tightly on the technical security of the application, against standard categories (OWASP LLM Top 10).
What is prompt injection?
An attack in which instructions are placed in the model's input (directly, or hidden in external content) so that they override its original instructions. It is especially dangerous for AI agents, because a manipulated model can take real actions.
Do we need AI Red Teaming because of the EU AI Act?
The EU AI Act sets accuracy, robustness and cybersecurity requirements for high-risk systems and requires adversarial testing for general-purpose models with systemic risk. Which category your system falls into, and from when, is a question for legal counsel, because the application timeline can change.
Do you need access to the model?
Not necessarily. Most tests can be run black-box through the user interface or API. Deeper access (system prompt, data sources, configuration) gives more precise results.
How long does an engagement take?
It depends on complexity: a few days for a simple chatbot, several weeks for an agentic system with many integrations. We give a firm timeline once we understand the system.
Is one test enough?
No. Models, prompts and data sources change, and new attack techniques appear. We recommend retesting after significant changes, and regular automated regression testing with the test suite we hand over.
[ 08 ] / RELATED SERVICES
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[ + ] / CONTACT
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