promptfoo.dev

Promptfoo is an AI security platform that red teams your application rather than just the underlying model. It generates thousands of context-aware attacks tailored to your specific AI app, agents, and workflows, drawing on real-time threat intelligence from a 300k+ user community, and then closes the loop with remediation guidance in your pull requests. This approach fits teams that build AI features into products and want security testing embedded in their existing development workflow, from CI/CD pipelines to MCP and agent frameworks, whether on-premise or in the cloud.

What "Application-Focused" Testing Means

Most AI security tools test the model in isolation. Promptfoo's stated approach is application-focused: it connects to your AI apps, agents, and workflows as they actually run, then attacks them in context. That distinction matters because a model that behaves safely in a generic benchmark can still leak data or be manipulated through your specific prompt structure, tool access, or retrieval setup.

The workflow follows three stages described on the site:

1. Connect — Integrate with your AI apps, agents, and workflows across CI/CD pipelines (GitHub, GitLab, Jenkins, and more), MCP and agent frameworks, and on-premise or cloud deployments.
2. Attack — Create thousands of context-aware attacks tailored to your application.
3. Fix — Get remediation guidance directly in pull requests and developer workflows.

How the Attack Generation Works

Rather than relying only on a fixed library of human-curated test cases, Promptfoo emphasizes three capabilities:

• Context-aware attacks — Thousands of attacks generated to fit your specific application, not a generic model.
• Real-time threat intel — Drawn from a community of 300,000+ open source users, with contributors from companies like OpenAI, Google, Microsoft, and Amazon.
• Deep automation — Scales beyond what human-curated tests can cover.
• Customizable attack flows — Attack flows can be tailored to your business requirements.

The practical effect: as new attack patterns emerge in the community, they can feed into your testing without you manually writing every case.

Closing the Loop: Remediation in Developer Workflows

Finding vulnerabilities is only half the job. Promptfoo surfaces security findings in pull requests, provides actionable remediation steps, tracks fixes across teams, and supports continuous monitoring. This means a developer sees the issue where they're already working, rather than in a separate security dashboard.

Who It's Built For

The platform is positioned for different roles with different needs:

The site notes adoption by 156 of the Fortune 500, with leading healthcare, telecommunications, retail, and enterprise software companies using it for AI security.

Getting Started

If you want to evaluate whether Promptfoo fits your workflow, the practical first step is to connect it to one AI application or agent you already run, then review the attacks it generates against that specific context. Because it integrates with CI/CD pipelines and agent frameworks, you can test it in a single pipeline before rolling it out more broadly. Pricing details are available on the site's pricing page; check there for current terms rather than assuming a free tier.

Promptfoo is an AI security testing platform that catches vulnerabilities in AI applications and agents during development rather than after deployment. It connects to your AI apps and CI/CD pipelines, generates context-aware attacks to red-team them, and surfaces remediation guidance directly in pull requests. It's aimed at teams building AI features — developers, security directors, and CISOs — and is used by 156 of the Fortune 500 and more than 300,000 developers, according to the company.

The core workflow: Connect, Attack, Fix

Promptfoo organizes its approach around three stages that map to how software teams already work.

1. Connect — integrate where you build

You wire Promptfoo into the systems you already use:

• CI/CD pipelines — GitHub, GitLab, Jenkins, and others
• MCP and agent frameworks
• On-premise or cloud deployments

The point is that testing runs inside your existing development workflow instead of as a separate, manual step.

2. Attack — test everything

Promptfoo generates thousands of context-aware attacks tailored to your specific application. Three things distinguish this from static, hand-written test suites:

• Real-time threat intel drawn from a 300k+ user community
• Deep automation that scales beyond human-curated tests
• Customizable attack flows matched to your business logic

3. Fix — close the loop

Findings don't stop at a report. Promptfoo delivers:

• Security findings surfaced in pull requests
• Actionable remediation steps
• Fix tracking across teams
• Continuous monitoring

This is the part that separates it from a one-off audit: remediation lands in the developer workflow, where the fix actually gets made.

What it's built on

Promptfoo leans on its open-source community for threat intelligence. Contributors come from companies including OpenAI, Google, Microsoft, and Amazon, and the platform reports 300,000+ open-source users and contributors from major foundation labs and tech companies. Weekly download figures are cited on the site as active deployments in production workflows worldwide.

Who it's for

The platform is positioned for different roles with different needs:

Security directors, in particular, are described as needing a solution that works at enterprise scale, integrates with existing tools, and gets adopted by their team — the emphasis being on depth without complexity.

When Promptfoo is a fit — and when it isn't

Consider it if you're shipping AI applications or agents and want security testing embedded in CI/CD rather than bolted on later, or if you need red-teaming that adapts to your specific application instead of generic model benchmarks.

Look elsewhere if you only need one-time model evaluation with no application context, or if you have no CI/CD or agent framework to integrate with — the value here comes from continuous, in-workflow testing.

Getting started

The natural entry point is the integration path: connect Promptfoo to your AI app and pipeline first, then run an initial red-team pass to see what it surfaces for your application, and finally wire remediation into your PR flow. The site's integrations and red-teaming sections cover the specifics for each supported pipeline and framework.

Promptfoo's community is the engine behind its threat intelligence: the platform describes itself as built on "the world's largest AI security community," with contributors from companies including OpenAI, Google, Microsoft, and Amazon, and real-time threat intel drawn from a user base of 300,000+ open source users. That matters if you want attack coverage that reflects what real attackers are actually trying, rather than a static test library that ages quickly.

Where the threat intelligence comes from

Promptfoo's approach ties three things together:

• A large open source user base. The site cites 300,000+ open source users securing AI applications with Promptfoo, plus a contributor count from major foundation labs and tech companies.
• Named contributors from foundation labs. Contributors come from companies like OpenAI, Google, Microsoft, and Amazon — the same organizations building the models many teams are testing.
• Real-time threat intel from the community. Promptfoo states that its attack generation draws on real-time threat intelligence from its 300k+ user community, which feeds context-aware attacks tailored to your application.

The practical effect: as new attack patterns surface in the wild, they can flow into the test suite rather than waiting for a vendor to hand-curate them. Promptfoo explicitly frames this as "deep automation that scales beyond human-curated tests."

What the community produces for you

The community isn't just a headcount — it shows up in the product's testing capability:

Promptfoo also notes that leading healthcare, telecommunications, retail, and enterprise software companies trust it with their AI security — useful context if you need to justify the choice internally.

Who else relies on it

Promptfoo highlights that teams at OpenAI and Anthropic use it to build better AI applications. One quoted user (from the "Build Hours" section) describes iterating on prompts, configuring tests in YAML, and viewing everything locally as faster and more straightforward than alternatives. A course reference notes Promptfoo offers a "streamlined, out-of-the-box solution" that can reduce the time and effort for comprehensive prompt testing.

If you're evaluating whether the community is active enough to trust, these are the concrete signals to weigh: named contributors from foundation labs, a six-figure open source user base, and adoption by both model providers and large enterprises.

How to decide if this matters for your team

The community and threat intelligence angle is most relevant when:

• You want attack coverage that updates as new threats emerge, not a fixed checklist.
• You need buy-in from security leadership and want third-party validation (Fortune 500 adoption, named contributors).
• You're already in or near the open source ecosystem and value transparency in how tests are built.

It's less of a deciding factor if your main need is a single point-in-time audit with no ongoing threat monitoring. For everyone else, the community is effectively the supply chain for Promptfoo's red teaming and remediation loop — connect, attack, fix — described on the site.

Promptfoo is designed for three distinct audiences inside an organization building AI applications: CISOs who need strategy and risk visibility, security directors who need depth and automation at enterprise scale, and developers who need speed and enablement inside their existing workflow. If your team ships AI apps, agents, or workflows and wants security testing embedded in development rather than bolted on afterward, Promptfoo is aimed at you. If you only need one-off model benchmarking with no integration into CI/CD or developer tooling, it is likely more than you need.

The three roles Promptfoo targets

Promptfoo's own positioning splits its audience by responsibility rather than company size:

The through-line is that each role gets a different entry point into the same platform, rather than a separate product.

Who this fits in practice

Promptfoo describes itself as an AI security platform that catches vulnerabilities in development, with testing built into the development workflow. That framing tells you the intended adopter:

• Organizations that ship AI features continuously. The platform connects to AI apps, agents, and workflows, and to CI/CD pipelines including GitHub, GitLab, and Jenkins, plus MCP and agent frameworks, on-premise or cloud.
• Teams that want red teaming without building it themselves. It generates context-aware attacks tailored to the application, drawing on real-time threat intelligence from a large user community, and scales beyond human-curated test sets.
• Teams that need remediation to land where work happens. Findings and remediation guidance surface directly in pull requests and developer workflows, with tracking across teams and continuous monitoring.

Evidence of scale and adoption

If you are judging whether Promptfoo is built for organizations like yours, the adoption signals matter:

• Trusted by 156 of the Fortune 500, including healthcare, telecommunications, retail, and enterprise software companies.
• 300,000+ open source users securing AI applications.
• Contributors from companies including OpenAI, Google, Microsoft, and Amazon.
• Named users include teams at OpenAI and Anthropic.

One practitioner quote on the site highlights the developer angle: Promptfoo is "really powerful because you can iterate on prompts, configure tests in YAML, and view everything locally... it's faster and more straightforward."

When Promptfoo is probably not the right fit

• You are an individual doing ad-hoc prompt experiments with no intention of wiring tests into a pipeline or shared workflow.
• You need only model-level evaluation and have no application, agent, or workflow surface to attack.
• You cannot adopt tooling that requires integration into CI/CD or developer workflows, since that integration is the core of how the platform delivers value.

How to decide for your team

1. Identify which of the three roles owns the decision: risk posture (CISO), automation depth (security director), or day-to-day enablement (developer).
2. Check whether your delivery path matches the supported integrations — CI/CD pipelines, MCP and agent frameworks, on-premise or cloud.
3. Confirm you want remediation surfaced in pull requests rather than a separate reporting tool.
4. If all three line up, Promptfoo is built for your team's shape. If only the first applies and the rest do not, evaluate whether the integration model fits before committing.

Promptfoo is an AI security testing platform that plugs into your existing development workflow rather than replacing it. According to the site, it connects to your AI apps, agents, and workflows, then runs context-aware attacks against them, and finally surfaces findings and remediation guidance where developers already work—pull requests and CI/CD pipelines. It fits teams that want security testing embedded in the build process, and it supports both on-premise and cloud deployment.

The three-stage integration model

Promptfoo describes its approach as a loop: Connect → Attack → Fix. Each stage maps to a specific part of a normal engineering workflow.

1. Connect: integrate anywhere

You point Promptfoo at the AI applications, agents, and workflows you already have. The site lists these connection surfaces:

• CI/CD pipelines — GitHub, GitLab, Jenkins, and more
• MCP and agent frameworks
• On-premise or cloud deployment

The practical implication: if your AI feature is built and shipped through a pipeline, Promptfoo is designed to attach at that same layer instead of requiring a separate testing environment.

2. Attack: test everything

Once connected, Promptfoo generates thousands of context-aware attacks tailored to your application. The site highlights three properties of this stage:

• Real-time threat intel drawn from a 300k+ user community
• Deep automation that scales beyond human-curated tests
• Customizable attack flows matched to your business

This is the part that distinguishes it from static prompt testing—the test set adapts to the application rather than being a fixed checklist.

3. Fix: close the loop

Findings are not left in a separate dashboard. According to the site, Promptfoo delivers:

• Security findings in pull requests
• Actionable remediation steps
• Tracking of fixes across teams
• Continuous monitoring

Putting findings in PRs means the fix happens in the same review cycle as the code change, which is the main workflow benefit.

What this looks like in practice

Who this integration is built for

The site frames the platform for several roles, and the integration story differs by role:

• Developers — speed and enablement; findings arrive in the tools they already use
• Security directors — depth and automation at enterprise scale, integrated with existing tools
• CISOs — strategy and risk visibility across the organization

The site also states Promptfoo is trusted by 156 of the Fortune 500 and cites OpenAI and Anthropic as teams using it, with contributors from companies including OpenAI, Google, Microsoft, and Amazon.

Conditions and caveats

• Deployment flexibility (on-premise or cloud) is stated on the site, but specific infrastructure requirements are not detailed in the available material—check the integrations documentation before assuming a fit.
• The site references a pricing page, but no pricing figures or plan limits are provided in the source material, so cost and access terms should be verified directly.
• The "300k+ user community" and "weekly downloads" figures are the site's own claims; treat them as vendor-reported.

Bottom line

Promptfoo integrates by attaching to the pipeline and PR layer you already use, running adaptive attacks against your AI apps and agents, and returning fixes to developers in their normal review flow. If your team ships AI features through GitHub, GitLab, or Jenkins and wants security testing inside that process rather than beside it, this is the integration model to evaluate.