What Is an AI Red-Team Exercise?
An Armorstack AI Red-Team Exercise is a structured adversarial engagement that tests the real-world resilience of deployed AI systems against the attacks most security teams don’t yet have a playbook for. Most organizations that have deployed LLMs, agentic AI systems, or ML models have never subjected them to dedicated adversarial testing — they rely on the model provider’s built-in safety filters and assume that’s sufficient. It isn’t. Armorstack red-team practitioners run multi-phase adversarial campaigns against specific AI objectives: a customer-facing LLM chatbot, an AI-powered code assistant with access to proprietary repositories, a clinical decision-support system processing PHI, or an AI underwriting model handling customer financial data. Attack scenarios include direct and indirect prompt-injection chains, systematic jailbreak campaigns, sensitive-data extraction via output manipulation, training-data reconstruction probes, model-supply-chain compromise simulation, and multi-turn manipulation sequences. The engagement produces a full findings report with severity ratings, reproduction steps, and prioritized remediation guidance.
What You Get
- AI system threat model with attack-surface mapping
- Prompt-injection chain testing across direct and indirect vectors
- Systematic jailbreak campaign using a model-specific technique library
- Sensitive-data extraction attempts (PII, PHI, PCI, IP, system prompts)
- Training-data reconstruction / memorization probes
- Supply-chain compromise simulation (plugin, fine-tune, and RAG-pipeline vectors)
- Full findings report with severity ratings, reproduction steps, and remediation guidance
Who This Is For
CISO
Accountable for AI systems that process sensitive data and face adversarial users — customers, competitors, or hostile nation-state actors.
AI / ML Engineering Team
Building and deploying LLMs or agentic systems who want adversarial validation before production, or hardening guidance after deployment.
Cyber Insurance Underwriter / GC
Requiring documented adversarial testing evidence as part of a renewal application or a pre-litigation due-care record.
How It Works
Scoping & Rules of Engagement
Target AI systems, attack objectives, data classification, and rules of engagement defined in writing. Black-box (no internal access), gray-box (limited context), or white-box (full system-prompt and architecture access) scope agreed up front.
Threat Modeling
Attack-surface map built for each target system — input vectors, output channels, downstream integrations, data stores, and user population. Attack plan derived directly from the threat model.
Adversarial Campaign Execution
Multi-phase attack execution across agreed scenarios: prompt injection, jailbreak, data extraction, supply-chain, and multi-turn manipulation. Typical duration 2–4 weeks depending on scope.
Findings Report & Debrief
Full findings report with CVSS-equivalent severity ratings, reproduction steps, screenshots and logs, and prioritized remediation guidance. Executive debrief included.
Investment
Healthcare and financial-services multi-system engagements typically $35,000–$65,000.
Timeline: 2–4 week engagement depending on scope. Report delivered within 5 business days of campaign close.
Every engagement begins with a scoping call and a written proposal. Work begins only after the engagement agreement is executed.Request a Consulting Proposal
Why Armorstack
AI-Native Attack Techniques
Attack library maintained against current LLM deployments — not recycled web-app penetration-test methodology. Prompt injection, jailbreak, and AI supply-chain techniques updated continuously.
Regulatory Context
Findings written to satisfy auditor and examiner evidence standards (SOC 2, HIPAA, PCI-DSS, CMMC) and cyber-insurance underwriter requests — not just a technical report.
Remediation-Oriented
Every finding includes a practical remediation recommendation — guardrail configuration, inference-boundary filter, policy control, or architectural change. Not just a list of failures.
Maps to Ongoing Observability
Findings map directly to detection signatures deployed under Armorstack’s LLM Security Observability program — red-team attack patterns become monitored detection rules.
Frequently Asked Questions
What is the difference between AI red-teaming and traditional penetration testing?
Traditional penetration testing targets infrastructure, application, and network attack surfaces (CVEs, misconfigurations, authentication weaknesses). AI red-teaming targets the AI system itself — how the model behaves under adversarial prompt manipulation, how it can be made to violate its intended behavior, and how sensitive data can be extracted through the model rather than from the systems around it. Both are required; they test different surfaces.
Do you need access to the model’s system prompt and architecture?
No. Black-box testing (no internal access, only the user interface) is the default and most realistic attack scenario. Gray-box (system prompt provided) and white-box (full architecture access) options are available for specific objectives, like training-data reconstruction probes that benefit from internal knowledge.
How does this relate to the NIST AI RMF ‘Manage’ function?
The NIST AI RMF Manage function requires organizations to respond to and manage AI risks, including residual risks identified during measurement. Documented AI red-team results provide evidence of adversarial risk assessment and mitigation — directly satisfying Manage 1.3 (monitor for unanticipated negative impacts) and Manage 2.2 (treatment of residual risks).
How often should AI red-team exercises be conducted?
At minimum annually, or when: a major model version is deployed, a new high-risk use case goes live, a significant architecture change is made (new RAG pipeline, new plugin), or following a security incident involving the AI system. For high-risk deployments (clinical decision support, financial underwriting), semi-annual exercises are recommended.
Related Services
Ready to Engage AI Red-Team Exercises?
Every VERITY AI engagement begins with a scoping call and a written proposal. No commitments until scope, deliverables, and pricing are agreed upon.Request an Engagement Proposal