Promptfoo + Humanbound

If you're building AI agents, it's likely that you're already utilizing Promptfoo within your development stack. Promptfoo is a widely adopted tool, with over 300,000 developers and 156 companies in the Fortune 500 utilizing it to identify issues such as prompt injection, jailbreaks, data leaks, and business rule violations before deployment.

A common question that arises is, if developers have already integrated Promptfoo, why would they need to incorporate Humanbound as well? The straightforward answer is that they don't have to choose between the two. On the contrary, they were designed to work together, not compete for the same role. Each serves a distinct purpose towards a common objective.

Promptfoo's strength lies in its extensive coverage and community support. Its red teaming engine utilizes real-time threat intelligence drawn from a large user base of open-source contributors, covering not just security, but also prompts, models, and RAG pipelines. It's a tool that many teams initially adopt, typically within their CI/CD processes, to identify obvious issues early on.

It's worth noting that continuous monitoring using this approach can also be achieved with Promptfoo or similar tools, but it's not a unique feature.

What sets Humanbound apart is its out-of-the-box monitoring capability. Once activated, it is immediately live on the company's infrastructure, unlike Promptfoo or other similar tools, which require the setup and maintenance of scheduling and CI/CD pipelines. Humanbound's findings are also directly mapped to compliance frameworks including EU AI Act, NIST AI RMF, and OWASP LLM and Agentic AI Top 10, with severity levels calibrated according to the domain.

When used in tandem, Promptfoo provides the community-driven breadth, while Humanbound offers continuous, compliance-aware depth.

The two tools can be used together seamlessly. Humanbound's firewall training pipeline has explicit support for importing Promptfoo's scan results. By executing the command `# Auto-detected from Promptfoo's JSON eval export hb firewall train --import results.json:promptfoo`, Promptfoo's evaluation IDs and results are merged with Humanbound's own adversarial and QA test logs.

The combined dataset is then used to train the Tier 2 agent-specific classifier within the Humanbound Firewall. This means that every prompt injection detected by Promptfoo in the CI pipeline becomes training data for the firewall's runtime defense, which safeguards the agent in production, without the need to re-run these attacks.

To illustrate this in practice, a team already using Promptfoo could incorporate Humanbound in three steps. They could continue running Promptfoo's red teaming within their CI/CD pipeline on every pull request. The results from Promptfoo could then be fed into Humanbound's firewall training alongside Humanbound's own adversarial test logs.

Turning on Humanbound's continuous monitoring post-deployment would ensure the agent is continually tested, and the firewall is regularly retrained. This results in a security posture that leverages Promptfoo's rapid, broad coverage during development, and extends into Humanbound's continuous, evidence-backed defense once the agent is live.

The system is also compliant with frameworks such as EU AI Act, HIPAA, and FCA, providing a layer of protection and proof when questioned.