Promptfoo is an AI security platform positioned as a tool for discovering vulnerabilities in AI applications during the development stage. According to its official introduction, it is trusted by 156 Fortune 500 companies and more than 300,000 developers worldwide.
Purpose Promptfoo's core purpose is AI security testing and red teaming, helping developers discover and fix vulnerabilities before AI applications, agents, and RAG systems go into production.
Main capabilities
- Automated red teaming for agents and RAG: Promptfoo simulates real users to uncover vulnerabilities specific to an application.
- Generates customized attacks for the target, covering: direct and indirect prompt injection, jailbreaks against existing guardrails, data and PII leakage, business rule violations, unsafe tool use in agents, and harmful content generation.
- AI security testing built into the development workflow: from integration to remediation, it supports connecting AI applications, agents, and workflows, integrating into CI/CD pipelines (GitHub, GitLab, Jenkins, etc.), supports MCP and agent frameworks, and can be deployed locally or used in the cloud.
- Large-scale context-aware attack generation, officially stated to create thousands of attacks customized for an application, with the help of real-time threat intelligence from a community of more than 300,000 users.
- Highly automated; it is officially stated that its deep automation can surpass manually orchestrated testing and allows attack processes to be customized per business.
- Remediation guidance goes directly into Pull Requests and developer workflows: displaying security findings in PRs, providing actionable remediation steps, and supporting cross-team remediation tracking.
- Continuous monitoring.
- Built on the AI security community: it is officially stated to provide access to real-time threat intelligence and innovation, with contributors from companies such as OpenAI, Google, Microsoft, and Amazon.
How to use It is officially described as embeddable in the development process: connect AI applications, agents, and workflows; continuously test in CI/CD; and receive remediation guidance in Pull Requests and developer workflows. More details on configuration and testing are not yet provided in the materials.
Use cases
- AI application and agent development teams conducting security testing before launch.
- Organizations that need continuous AI security testing in CI/CD.
- Enterprises concerned about risks such as prompt injection, jailbreaks, data leakage, PII leakage, business rule violations, tool misuse, and harmful content.
- Security leaders who need both depth and automation, integration with existing tools, and enterprisescale deployment.
- Industries such as healthcare, telecommunications, retail, and enterprise software.
Limitations The official page does not provide specific pricing, charging model, free quota, platform support scope, deployment details, or performance metrics; the above information is not yet provided.
