Red-teaming has long been a cornerstone of enterprise security—but when it comes to generative AI, most organizations are applying outdated tactics to a new kind of threat landscape. Static prompt tests, one-off audits, and CLI-based tools may surface obvious failures, but they rarely capture the real-world complexity of how GenAI systems behave once deployed.
This post explores the key blind spots in most AI red-teaming strategies and best practices security leaders can adopt to evolve.
Why Static Testing Falls Short in a Dynamic AI World
Traditional AI red-teaming tools often focus on prompt injection success rates: did the model respond or refuse? But this binary outcome misses the bigger picture.
Was the refusal robust across variations? Did the model redirect or subtly mislead? Was it vulnerable to indirect, multi-turn probing? Most red teams stop at surface-level indicators and can’t account for nuanced failures that unfold over time. And as enterprises increasingly test open-source or experimental models (often with limited insight into internal safety mechanisms), security leaders are demanding a deeper, more principled approach to validation before onboarding.
The Rise of Agentic Red Teaming
One of the most promising developments in AI security is the shift from static prompts to agentic red-teaming—a method where autonomous adversarial agents probe and adapt to model behavior over time.
Instead of running a fixed set of prompts, these agents are given intent-based objectives (e.g., extract confidential data, bypass moderation, impersonate internal roles) and are allowed to iterate. They learn from the model’s responses and attempt increasingly sophisticated, indirect methods to reach their goal—mirroring how real-world attackers behave.
This approach not only surfaces vulnerabilities missed by traditional tools, but also reflects the emerging risk landscape: one where models interact with complex systems and other agents, and where threats play out over time, not in isolation.
Testing AI in Context: Why MCP Changes the Game
This shift toward dynamic, agentic systems is now being formalized in technical standards, most notably, the Model Context Protocol (MCP). MCP specifies how context is passed and preserved across tools, agents, and model interactions. That context could include system instructions, task history, or shared memory between AI components.
From a security standpoint, MCP presents a new kind of red-teaming challenge: evaluating not just isolated model responses, but how models behave within a sequence, with evolving instructions, dependencies, and objectives.
A model that appears secure in a clean, one-off test may fail when it inherits a compromised context or is manipulated by upstream agents. Red teams that ignore context flow are likely to miss entire classes of real-world vulnerabilities.
Why Contextual Testing Beats Binary Results
All of this points to a broader truth: binary results like pass/fail on a single prompt are no longer sufficient. To make meaningful security decisions, teams need to understand how systems fail, why they fail, and under what conditions.
Contextual testing surfaces the patterns, dependencies, and subtleties that can expose latent weaknesses in GenAI systems. This includes how models respond to state changes, prompt re-entry, or adversarial chaining. None of which is captured by static red-teaming.
Beyond model-level probing, deep, application-aware red teaming is essential if you're leveraging AI at runtime. Especially in RAG pipelines or agentic workflows where internal data is introduced at inference, it’s the application’s behavior—not just the underlying model—that determines your exposure.
These deeper insights also demand more from reporting. It’s not enough to flag a vulnerability, you need to show what triggered it, why it matters, and how to fix it. That means logging full prompt/response chains, mapping issues to categories of intent or system behavior, and providing clear prioritization for remediation.
AI Red-Teaming Best Practices
Security teams can take immediate steps to close the gaps in their red-teaming strategies:
- Expand beyond one-shot prompts: Incorporate multi-turn and context-sensitive scenarios.
- Test against specific threat intents: Move from generic jailbreaks to targeted adversarial goals.
- Make results explainable and actionable: Prioritize outputs that translate into security improvements.
- Ensure repeatability and scale: Red-team testing should be automated and integrated with your broader DevSecOps pipeline.
Red-teaming for AI isn’t just about penetration testing and vulnerability assessment, it’s about gaining the insights you need to secure what’s being built. As AI becomes more embedded across enterprise systems, security practices must evolve in parallel.
