Nuvexia’s AI security advisory service includes red teaming and model testing for AI systems, adversarial testing designed to find vulnerabilities in your models, agents, and AI infrastructure before attackers, regulators, or customers do. Testing is aligned to OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, and the EU AI Act’s binding adversarial testing requirements for systemic-risk GPAI models.
What AI red teaming is
We deliberately try to make an AI system fail, including leaking data, following a malicious instruction, producing unsafe output, or being manipulated into unintended behavior, so those failure modes get fixed before they are exploited in production.
Why it matters now
For GPAI models with systemic risk, the EU AI Act makes adversarial testing a legal requirement, not a best practice, and enterprise customers increasingly expect it as diligence.
Test the model and the system around it
Not just chatbot replies, but agents that use tools, infrastructure that hosts them, and data that trains them.
Frameworks we test against
Findings are mapped to these frameworks so they plug directly into governance, risk, and compliance reporting.
2026 edition, published August 2026. The most referenced catalogue of LLM app risks, including prompt injection, sensitive disclosure, and excessive agency.
Knowledge base of real-world adversary tactics and techniques against AI systems.
Broader risk-management context that testing results feed into for governance and assurance.
Binding adversarial testing for systemic-risk GPAI models above 10^25 FLOPs, required for new providers since 2 August 2025 and existing providers by 2 August 2027.
For organizations that also need systems tested against AI Verify safety and robustness checks.
What’s included
A full-scope engagement that covers model behavior, agent safety, and the infrastructure around them.
Attempts to make the system follow malicious instructions or bypass safety controls.
Tests for data leakage, including training-data memorization risks.
Checks for risks where poisoned data or models influence behavior.
Across demographic and use-case dimensions, not just generic toxicity.
Excessive agency, unsafe tool invocation, and goal hijacking for autonomous agents.
Review of the systems hosting your models and their access controls.
Documented findings mapped to frameworks above, with prioritized fixes.
Not a one-time engagement, but testing aligned to future releases.
Who this is for
You may fall under EU AI Act systemic-risk classification and need to meet Article 55 obligations.
Chatbots, copilots, or autonomous agents where adversarial failure would be customer-facing.
You need to demonstrate model safety to regulators, enterprise customers, or investors as part of diligence.
This testing feeds directly into governance and assurance, see ISO/IEC 42001 Services and for the platform these models run on, see AI Infrastructure Build & Support.
Frequently asked questions
Traditional penetration testing looks for exploitable weaknesses in conventional software and infrastructure. AI red teaming specifically targets model behavior, such as how a model can be manipulated through inputs like prompt injection and jailbreaks, what it might leak from training data, and how autonomous agents might be misused, risks that do not exist in traditional software in the same form.
For general-purpose AI models with systemic risk under the EU AI Act, yes. Article 55 makes adversarial testing a binding legal obligation, not a best practice. Outside that specific category, it is not yet universally mandated, but it is increasingly expected by regulators, enterprise customers, and insurers as evidence of responsible deployment.
A community-developed, regularly updated catalogue of the most critical security risks in LLM-based applications. The 2026 edition was published in August 2026 and is used widely as a baseline for what AI security testing should cover.
Yes. Agentic systems introduce their own risk category, excessive agency, including unsafe tool use and unintended autonomous actions. Nuvexia's testing scope covers this specifically rather than treating agents like a standard chatbot.
We recommend testing before every major model or agent release, after significant system changes, and on a recurring cadence at least annually, or more frequently for systemic-risk GPAI models to meet EU AI Act Article 55 expectations for testing before and after release. We set a specific cadence during scoping based on your release cycle and risk profile.
Find failures before they find you
Talk to Nuvexia about an AI red teaming or model testing engagement, from prompt injection to agentic safety, with findings mapped to OWASP, ATLAS, NIST RMF, and EU AI Act.