AI Red Teaming: test your agents before attackers do
Context-aware red teaming stress-tests your agents against thousands of attack scenarios, simulating real-world adversarial techniques to uncover weaknesses early.
AI threats evolve fast; your security has to evolve faster
AI systems have moved from generating responses to taking actions. Traditional vulnerability assessment cannot keep pace with the evolving capabilities of agentic AI, and one-off tests or static jailbreak lists miss risks specific to your system’s architecture and use case.
Prisma AIRS AI Red Teaming uses a dynamic red teaming agent and a comprehensive attack library to identify security and safety weak points, continuously simulating real-world attacks and delivering prioritised remediation insights. Its three principles: comprehensive, contextual and continuous.
Red teaming engineered for the modern AI stack
Easy endpoint onboarding
Templates for major public AI platforms and secure integration with private endpoints.
Agentic profiling
A profiler agent gathers the target’s tools, banned words and configuration to build a meaningful risk profile.
Contextual attack generation
An attacker agent uses that profile to go beyond generic jailbreaks with scenarios specific to your system.
Actionable insights
Granular risk findings for every simulation that feed runtime policies and other remediation.
Broad technique coverage
50+ techniques mapped to OWASP Top 10 and NIST AI RMF, backed by Huntr’s 19,000+ members and Unit 42 research.
Multi-agent systems
Multi-turn attacks and testing of autonomous, multi-agent systems are supported.
AI Red Teaming with Albisec
As a Palo Alto Networks partner, we run AI security projects end to end, from discovery to production.
- An initial assessment that inventories your AI applications, agents and models
- Selection and licensing of the Prisma AIRS components that fit your use cases
- Deployment, policy design and integration with your existing NGFW, Prisma Access and Cortex environment
- 24/7 support, periodic policy reviews and reporting after go-live
Official product page:paloaltonetworks.com/ai-security/ai-red-teaming
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Learn moreFrequently asked questions
What is AI red teaming?
AI red teaming approaches AI apps and agents from an attacker’s perspective, using controlled attack simulations to expose weaknesses such as prompt injection, data leakage, privilege abuse and unsafe actions.
How often should AI red teaming be done?
Risk changes as models, prompts and agent tools change. We recommend testing before every significant release and on a regular schedule, ideally integrated into CI/CD.
Can we add our own attack scenarios?
Yes. In addition to the built-in library, AppSec teams can bring their own attacks.
Talk to our experts about your Palo Alto Networks project
A new deployment, a firewall migration or a support contract: tell us what you need and we will map out the right path with a free initial assessment.