AI Red Teaming Tools
Automated Adversarial Testing for LLM Agents
FortifAI is the AI red teaming platform built for autonomous AI agents. Run 150+ adversarial payloads across all OWASP Agentic Top 10 categories in under 90 seconds — with CI/CD integration, evidence capture, and developer-friendly reporting.
What Is AI Red Teaming?
AI red teaming is the practice of simulating adversarial attacks against AI systems to find vulnerabilities before real attackers do. It adapts classical red team methodology — think like an attacker, attempt everything they would — to the unique threat model of large language models and autonomous AI agents.
For agentic AI systems, red teaming covers prompt injection attacks, goal hijacking, tool abuse, memory poisoning, data exfiltration, and all other categories in the OWASP Agentic Top 10.
Traditionally, AI red teaming required weeks of manual work by specialized security researchers. FortifAI automates the adversarial payload execution layer — enabling continuous, reproducible red team testing inside CI/CD pipelines — while human experts focus on novel attack research.
FortifAI vs. Manual Red Teaming vs. Generic Scanners
| Feature | FortifAI | Manual Red Team | Generic Scanners |
|---|---|---|---|
| Automated adversarial payloads | 150+ payloads | Manual crafting | Limited |
| OWASP Agentic Top 10 coverage | 100% (10/10) | Partial | None |
| Time to first finding | < 90 seconds | Days/weeks | Hours |
| CI/CD integration | Native (npx) | None | Limited |
| Framework support | LangChain, AutoGen, CrewAI, custom | Varies | Generic HTTP |
| Evidence capture | Full (payload + response + reasoning) | Manual notes | Basic |
| Black-box testing | Yes | Yes | Yes |
| Reproducible results | Yes | No | Partial |
AI Red Teaming for Every Team
AI Red Teaming Tools — FAQs
What is AI red teaming?
AI red teaming is the practice of simulating adversarial attacks against AI systems — particularly LLM agents — to identify security vulnerabilities, safety failures, and unexpected behaviors before they can be exploited in production. It adapts traditional cybersecurity red teaming methodology to the unique threat model of AI systems.
What features should I look for in an AI red teaming tool?
Key features include: automated adversarial payload libraries (150+ payloads), OWASP Agentic Top 10 coverage, CI/CD integration, black-box testing (no model access required), structured JSON/PDF reporting, evidence capture (payload + response), behavioral analysis, and support for frameworks like LangChain, AutoGen, and CrewAI.
How is FortifAI different from manual AI red teaming?
Manual AI red teaming requires specialized expertise, takes days or weeks, and produces results that are hard to reproduce or integrate into development pipelines. FortifAI automates the adversarial payload execution phase — running 150+ attacks in under 90 seconds — making red teaming continuous, reproducible, and developer-accessible without replacing human expert analysis for advanced threat scenarios.
Can AI red teaming tools work with any LLM agent?
FortifAI works with any AI agent that exposes an HTTP endpoint, regardless of the underlying model or framework. It natively supports LangChain, AutoGen, CrewAI, and OpenAI Agents SDK, and works with custom agent implementations through standard HTTP configuration.
How often should AI red teaming be performed?
AI red teaming should be performed continuously — integrated into CI/CD pipelines to catch regressions on every deployment — and as a deeper manual exercise before major releases. FortifAI enables the continuous automated layer, while human red team exercises can focus on novel attack research and complex multi-step scenarios.
Automate Your AI Red Teaming
Replace weeks of manual red team effort with continuous, automated adversarial testing. 150+ payloads. OWASP-aligned. CI/CD-ready.