4 posts tagged with "tool-comparison"

Why are we red teaming AI systems?​

If you're looking into red teaming AI systems for the first time and don't have context for red teaming, here's something I wrote for you.

The rush to integrate large language models (LLMs) into production applications has opened up a whole new world of security challenges. AI systems face unique vulnerabilities like prompt injections, data leakage, and model misconfigurations that traditional security tools just weren't built to handle.

Input manipulation techniques like prompt injections and base64-encoded attacks can dramatically influence how AI systems behave. While established security tooling gives us some baseline protection through decades of hardening, AI systems need specialized approaches to vulnerability management. The problem is, despite growing demand, relatively few organizations make comprehensive AI security tools available as open source.

If we want cybersecurity practices to take more of a foothold, particularly now that AI systems are becoming increasingly common, it's important to make them affordable and easy to use. Tools that sound intimidating and aren't intuitive will be less likely to change the culture surrounding cybersecurity-as-an-afterthought.

I spend a lot of time thinking about what makes AI red teaming software good at what it does. Feel free to skip ahead to the tool comparisons if you already know this stuff.

As organizations increasingly adopt machine learning models from various sources, ensuring their security has become critical. Two tools have emerged to address this need: Promptfoo's ModelAudit and Protect AI's ModelScan.

To help security teams understand their options, we conducted a comparison using 11 test files containing documented security vulnerabilities. The setup of this comparison is entirely open-source and can be accessed on Github.

As enterprises deploy AI applications at scale, red teaming has become essential for identifying vulnerabilities before they reach production. Two prominent open-source tools have emerged in this space: Promptfoo and Microsoft's PyRIT.

Quick Comparison​

Feature	Promptfoo	PyRIT
Setup Time	Minutes (Web/CLI wizard)	Hours (Python scripting)
Attack Generation	Automatic, context-aware	Manual configuration
RAG Testing	Pre-built tests	Manual configuration
Agent Security	RBAC, tool misuse tests included	Manual configuration
CI/CD Integration	Built-in	Requires custom code
Reporting	Visual dashboards, OWASP mapping	Raw outputs
Learning Curve	Low	High
Best For	Continuous security testing	Custom deep-dives

As LLM applications move into production, security teams face a critical challenge: how do you systematically identify vulnerabilities before attackers do?

Two open‑source tools have emerged as popular choices for LLM red teaming, each taking a fundamentally different approach to the problem.