Activate AI Red Teaming in the Enterprise: Pressure-Test Your Modern Stack 

Following last month's release of AI Governance and Model Evaluation exercises, Immersive One’s Secure AI capability now extends beyond defining boundaries. Security teams can safely practice the tactics required to identify and abuse AI vulnerabilities across the enterprise stack—from RAG pipelines to autonomous agents. Aligned to eight MITRE ATLAS skills, four new AI Red Teaming collections enable teams to benchmark their readiness: pressure-testing how guardrails can be broken, not just how they are defined.

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As enterprise AI moves from experimental prototypes to production-grade agents, the security vacuum widens. These architectures are being deployed dramatically faster than the capacity analysts have to audit and secure them. Over a series of recent releases, Immersive One now enables defenders to expand AI proficiency from theoretical risk assessment to functional exploitation.

Four new MITRE ATLAS-aligned AI Red Team collections provide the technical foundation to identify and abuse vulnerabilities across the modern stack—including RAG, MCP, and AI Agents. By simulating real-world attacks in Immersive One’s sandboxed environment, red teams lead the organizational transition from risky experimentation to a governed, defensible AI operation.

Establish New Rules of Engagement

While traditional red teaming targets deterministic logic, AI exploits probabilistic behaviors, requiring a fundamental shift in mindset. Web and infrastructure security experts must translate years of intuition into the world of adversarial machine learning. Immersive One’s AI Red Teaming—Foundations collection delivers that entry point. By establishing a conceptual framework aligned to MITRE ATLAS, this collection helps researchers:

• Contrast traditional vs. adversarial ML: Understand why standard vulnerability scanners miss semantic failures and probabilistic bypasses.
• Master vocabulary: Align on the specific language of AI risk—from "inversion" to "membership inference."
• Navigate the MITRE ATLAS framework: Exercise methodology aligned to the latest ATLAS framework updates. 

Security teams need to understand the rules before they can effectively break them. Exercising on Immersive One ensures researchers possess the mental model required to move confidently into functional exploitation.

Map the Shadow AI Attack Surface

Adversaries cannot exploit what they cannot find. The explosion of AI adoption has created a surge in shadow AI, where untracked chatbots, CI/CD code scanners, and backend summarization tools exist outside official security perimeters. Researchers can deep dive into this technical phase via AI Red Teaming—Reconnaissance, enabling them to:

• Identify hidden AI deployments: Locate customer-facing and internal-only AI tools, covering Active Scanning and Search Victim-Owned Websites.
• Gather victim identity information: Understand the permissions and identities associated with AI services, aligning to Search Application Repositories and Gather Victim Identity Information.
• Plan subsequent attack phases: Use gathered artifacts to tailor later exploitation stages against the discovered surface.

Practice Precision Targeting via Model Fingerprinting

Effective red teaming requires human intelligence over blind testing. To execute a successful breach, researchers must understand the specific model and infrastructure they are interacting with. In AI Red Teaming—AI Model Access, teams focus on model fingerprinting and inference API discovery, building muscle to:

• Discover exposed endpoints: Locate hidden API access points that allow direct, unshielded interaction with the model, per AI Model Inference API Access.
• Fingerprint specific models: Use targeted prompts to Discover AI Model Family, revealing the underlying architecture and versioning.
• Increase exploit success: Move from generic jailbreaks to highly targeted attacks tailored to known weaknesses of a specific model version.

Exploit Excessive Agency and Poisoned Data

The highest-value concern for enterprises today is the vulnerability of RAG pipelines and AI Agents that have direct access to sensitive data and internal APIs. When an agent is given too much power without sufficient guardrails, it creates "Excessive Agency." AI Red Teaming—Initial Access empowers researchers to:

• Prove tangible risk: Demonstrate how Prompt Infiltration via Public-Facing Application can lead to unauthorized API execution. 
• Test RAG integrity: Validate whether data retrieval processes are susceptible to manipulation via AI Supply Chain Compromise, like poisoning the external data sources an agent ingests. 
• Enforce boundaries: Use findings to build more robust controls that ensure agent autonomy doesn’t outpace security oversight.

Validate Secure AI Transformation with Framework-Aligned Capabilities

The shift to agentic AI requires a new standard for validating security. It is no longer enough to know that a risk exists; security leaders must prove that their teams can find, exploit, and eventually detect these specific threats. By building offensive skills, organizations find and close gaps in the AI stack before they are exploited in the wild. These collections provide the hands-on proficiency needed to ensure your AI adoption strategy is not just fast, but secure, tested, and proven.

Immersive One’s Secure AI capability is ever-expanding, ensuring organizations can prove and improve AI security skills as the landscape evolves. Within a stable, sandboxed environment, teams can exercise functional exploitation in addition to defensive governance—safely pressure-testing people, processes, and tools in tandem. This comprehensive approach ensures leaders can truly benchmark their readiness across the full spectrum of AI risk. 

Get Started

• Immersive One customer? Start pressure testing your team’s ability to identify shadow AI and exploit excessive agency by assigning an AI Red Team collection. Look for “AI for Offensive Operations,” available via the Upskill tab.
• Exploring Immersive One? Book a demo to learn how to benchmark your team’s readiness against real-world AI threats.

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