What if your AI-powered application leaked sensitive data, generated harmful content, or revealed internal instructions – and none of your security tools caught it? This isn’t hypothetical. It’s happening now and exposing critical gaps in how we secure modern AI systems.

When AI systems like LLMs, agents, or AI-driven applications reach production, many security teams initially turn to familiar tools: AST solutions like SAST, SCA, and compliance checklists. But those tools weren’t designed for AI’s unique behavior. Unlike traditional applications, AI models don’t follow predictable code logic. Their risks emerge dynamically – influenced by user input, context, and adversarial probing. That’s why testing how they behave under real-world pressure is essential.

Why Traditional AppSec Tools Aren’t Enough

Early AppSec focused on static analysis – until attackers showed that real risk often lives in dynamic behavior. With AI, the distinction is even more pronounced. These are probabilistic, trained systems, and their vulnerabilities don’t live in the code – they emerge during interactions.

That doesn’t mean static analysis is irrelevant. In fact, the next generation of AI-native security combines dynamic and static techniques, each tailored to expose specific AI threats. What isn’t effective is relying on legacy tools not built for AI-specific risks like prompt injection, data leakage, and hallucination.

Red teaming is one of the most effective ways to uncover these behavioral vulnerabilities. It stress-tests AI systems in adversarial scenarios, revealing issues that only appear at runtime. But it’s not the only piece of the puzzle – it complements broader efforts to adapt static and dynamic analysis for AI’s unique threat landscape.

How Teams Are Tackling AI Red Teaming Today

Today, teams are operationalizing AI red teaming in three main ways:

1. Automated SaaS platforms

Solutions like Mend AI Red Teaming provide scalable, continuous testing that integrates into CI/CD pipelines. These platforms simulate a wide range of attack techniques, from prompt injection and data leakage to jailbreaks and hallucinations, across LLMs, AI agents, and broader AI-driven applications. They typically offer:

• Predefined and customizable attack libraries
• Runtime simulation of emerging threats
• Actionable reports for dev and security teams
• Optional remediation suggestions

By enabling repeatable, automated testing as part of development workflows, these platforms help teams detect vulnerabilities before they reach users.

2. Open source frameworks

Projects like Microsoft’s PyRIT offer modular toolkits for teams building their own red teaming infrastructure. They provide full control over test logic, threat simulation, and customization – making them appealing to larger organizations with the resources to invest in long-term, internal frameworks.

However, these solutions require significant upkeep: ongoing threat model updates, input tuning, and pipeline integration. They offer power and flexibility, but come with higher operational overhead.

3. Consultancy services

Firms like CrowdStrike and Trail of Bits offer bespoke red teaming engagements, often focused on high-risk deployments. These services include adversarial scenario design, expert-led probing, and deep security assessments.

While highly effective, these engagements are typically time-boxed and resource-intensive. Without ongoing integration into development workflows, their impact may diminish over time unless paired with internal follow-through.

Why It’s Still Hard

Red teaming AI systems is still a developing practice, and there’s no industry-wide standard for what “good” coverage looks like. Teams are building that playbook in real time.

• Model behavior is dynamic: Outputs shift with input phrasing, user history, and system context, making traditional pass/fail metrics less effective.
• Tooling is immature: Many teams still lack reliable solutions for adversarial data generation, automated variation of prompts, and behavioral coverage tracking.
• Patchwork approaches dominate: Without a unified framework, many teams combine partial solutions that miss important risks.
• Attack variation is constant: New attack types, or slight variations of known ones, emerge daily. A payload encoded in base64 or embedded in a PDF might bypass filters that would catch a plain-text version. These variations make consistent detection extremely difficult.
• Multimodal complexity: Testing AI systems that combine vision, language, and other inputs (like voice or images) is especially challenging. These categories are still immature from a security perspective, but attacks are already being observed, including within Mend AI’s red teaming indexing.

Meanwhile, attackers aren’t waiting. Every AI-driven system shipped without adversarial testing increases exposure.

What Smart Teams Are Doing

Forward-thinking organizations are building AI security programs that reflect these new realities. They:

• Develop threat models tailored to their AI applications
• Prioritize testing for high-risk behaviors like sensitive data leakage, prompt injection, and policy evasion
• Define behavioral boundaries, not just technical ones
• Integrate automated red teaming into CI/CD pipelines
• Use open frameworks for specialized attack simulation
  
• Implement system-specific sanitizers and mitigations on agents and LLMs. These defenses should be tailored to the unique behaviors of each AI system.
• Bring in consultants for deep, high-stakes analysis

Most importantly, they recognize that securing AI means testing how it behaves under pressure – not just how it was built.