The Question
Red teaming — adversarial testing by a team simulating an attacker — is a well-established practice in enterprise security. You hire a team to attack your systems before a real attacker does, surface the gaps, and fix them.
AI red teaming applies the same principle to LLM applications, AI agents, and model infrastructure. But it is meaningfully different from traditional penetration testing, and most enterprises evaluating it do not fully understand what they are buying. This Advisory Brief explains what AI red teaming actually involves, what good looks like, and how to assess whether your organization needs it now or later.
AI red teaming exercises coverage of every risk in the OWASP Top 10 for Large Language Model Applications — it is the adversarial validation that confirms whether your controls address the threats they claim to address, or whether they fail against novel attack patterns.
Why This Matters Now
In 2023, Microsoft and OpenAI jointly published findings from extensive red team exercises against GPT-4 before its public release. The exercises — conducted by a dedicated red team — discovered a range of vulnerabilities including prompt injection vectors, system prompt extraction techniques, and jailbreak patterns that the model's safety training had not eliminated. The findings shaped the safety mitigations deployed before launch.
This is now standard practice at frontier AI labs. What is not yet standard practice at enterprises deploying those models is equivalent adversarial testing of the applications built on top of them. An enterprise that deploys GPT-4 via API and builds a customer-facing application on top of it is inheriting the safety work Microsoft and OpenAI did — but the application layer, the system prompt, the tool integrations, and the data flows are entirely the enterprise's own construction. That application layer has never been red teamed.
NIST AI Risk Management Framework 1.1, published in 2026, includes adversarial testing as a recommended practice for high-risk AI systems. The EU AI Act requires conformity assessments for high-risk AI that effectively mandate structured adversarial testing. The regulatory signal is consistent: red teaming for AI is moving from best practice to expected standard.
What the CURVE™ Data Shows
The 2026 Stackcurve AI Security CURVE™ Report covers the AI Red Team & Adversarial Testing category, with representative vendors including Horizon3.ai, SplxAI, Repello AI, Adversa AI, HackerOne, Trail of Bits, Synack, and Mindgard.
The category breaks into two delivery models. Automated continuous red teaming — platforms that run adversarial probes against your AI systems continuously, surface new vulnerabilities as they emerge, and provide ongoing coverage without human engagement for every test cycle. Horizon3.ai's NodeZero and SplxAI's automated AI red teaming platform represent this approach. Human expert engagements — structured assessments conducted by a team with documented AI security expertise, producing a findings report and remediation guidance. Trail of Bits, HackerOne's AI security practice, and Synack offer this model.
The full vendor rankings are in the 2026 AI Security CURVE™ Report — free to download.
The right model depends on your maturity and your objectives. Automated continuous testing is better for ongoing coverage and early detection of regressions. Human expert engagements are better for comprehensive initial assessments, compliance documentation, and surfacing subtle vulnerabilities that automated probes miss. Most mature programs use both.
The Gap Most Buyers Miss
Traditional penetration testing and AI red teaming share a name but require different expertise. A penetration tester who is expert at network exploitation, privilege escalation, and lateral movement does not automatically have the skills to effectively red team an LLM application. The threats are different. The attack techniques are different. The tooling is different.
Specifically, AI red teaming requires expertise in:
Prompt injection technique — crafting injection payloads that bypass specific guardrail architectures, including indirect injection via document and RAG manipulation, not just direct user-input attacks.
Goal misgeneralization exploitation — designing scenarios that expose divergence between a model's stated objectives and its actual behavior in edge cases, novel contexts, and adversarially constructed situations.
Agentic attack chains — constructing multi-step attack sequences that exploit tool integrations, memory systems, and inter-agent communication rather than targeting the model in isolation.
Model-specific knowledge — understanding the architecture, training approach, and known weaknesses of the specific model being tested. A red team exercise against a GPT-4o deployment should differ from one against Claude 3 or Gemini 1.5 — the attack surface is not identical.
When evaluating AI red team providers, ask specifically about the team's background. Security researchers who have moved into AI red teaming from traditional AppSec or network security bring important foundations but may lack the ML and LLM-specific depth that high-quality AI red teaming requires. The best AI red team practitioners today have both security and ML expertise.
Questions Your Buying Team Should Be Asking
1. What is the methodology, and how is it mapped to the OWASP Top 10 for LLMs and the five agentic threat classes? A structured methodology mapped to established frameworks is the baseline of a credible engagement. Ask for the methodology documentation before you engage. If the vendor cannot provide it, the engagement quality is likely to be ad hoc.
2. Does your team test agentic systems, or only conversational LLM applications? Many AI red team practices are still focused on conversational AI — chatbots, summarization tools, Q&A systems. If you have agentic deployments — AI with tool use, external access, and autonomous task execution — confirm that the engagement scope and team expertise explicitly covers them.
3. What does the deliverable look like, and how actionable is it? Ask for a sample report from a comparable engagement. The deliverable should include specific vulnerability descriptions, reproduction steps, impact assessment, and remediation guidance — not a high-level narrative without technical specificity.
4. Do you offer continuous automated testing, or only point-in-time engagements? A one-time engagement provides a snapshot. AI applications change — system prompts are updated, tools are added, models are upgraded. Continuous coverage catches regressions that point-in-time assessments miss. Understand what the vendor offers and whether your risk profile requires ongoing coverage.
5. Can you test in our environment, or only in a controlled test environment? The most valuable testing happens against your actual deployment — your real system prompt, your real tool integrations, your real data flows. Testing in a sanitized vendor environment reduces the relevance of the findings. Confirm whether production or production-equivalent testing is part of the engagement.
The Stackcurve Take
AI red teaming is not a luxury for organizations with mature security programs. For any enterprise running AI applications that handle sensitive data, interact with external parties, or have tool-use capabilities, adversarial testing is a necessary validation of controls that are otherwise unverified.
The sequence matters: red teaming is most valuable after you have deployed baseline controls — guardrails, least privilege, monitoring — because the exercise can then tell you whether those controls hold under adversarial pressure. Red teaming before you have controls surfaces findings you cannot yet remediate. Red teaming after you have controls tells you which ones are working and which have gaps.
For enterprises new to AI red teaming, a focused human expert engagement against your highest-risk AI deployments is the right starting point. Define the scope tightly — two or three Tier-1 agentic applications, tested against the full threat taxonomy — and use the findings to prioritize your remediation roadmap. Add continuous automated coverage once you have a baseline posture established.
The 2026 Stackcurve AI Security CURVE™ Report covers the AI Red Team & Adversarial Testing category in full, including the vendors Stackcurve considers most credible for enterprise engagements. Download it free →
Stackcurve Advisory Briefs are independent research. No vendor pays for placement, tier assignment, or editorial influence. The CURVE™ methodology is disclosed in full at stackcurve.net/research/methodology.