18 posts tagged with "red-teaming"

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.

Intro to AI red teaming​

Is this your first foray into AI red teaming? And probably red teaming in general? Great. This is for you.

Red teaming is the process of simulating real-world attacks to identify vulnerabilities.

AI red teaming is the process of simulating real-world attacks to identify vulnerabilities in artificial-intelligence systems. There are two scopes people often use to refer to AI red teaming:

• Prompt injection of LLMs
• A wider scope of testing pipelines, plugins, agents, and broader system dynamics

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.

Google's Gemini represents a significant advancement in multimodal AI, with models featuring reasoning, huge token contexts, and lightning-fast inference.

But with these powerful capabilities come unique security challenges. This guide shows you how to use Promptfoo to systematically test Gemini models for vulnerabilities through adversarial red teaming.

Gemini's multimodal processing, extended context windows, and thinking capabilities make it particularly important to test comprehensively before production deployment.

You can also jump directly to the Gemini 2.5 Pro security report and compare it to other models.

The Evolution of Red Teaming​

Early red teaming tools and research began with jailbreaks like "Ignore all previous instructions" and static lists of harmful prompts. At Promptfoo, we took those ideas a step further by dynamically generating attacks based on the context of the target application.

OpenAI's GPT-4.1 and GPT-4.5 represents a significant leap in AI capabilities, especially for coding and instruction following. But with great power comes great responsibility. This guide shows you how to use Promptfoo to systematically test these models for vulnerabilities through adversarial red teaming.

GPT's enhanced instruction following and long-context capabilities make it particularly interesting to red team, as these features can be both strengths and potential attack vectors.

You can also jump directly to the GPT 4.1 security report and compare it to other models.

Anthropic's Claude 4 represents a major leap in AI capabilities, especially with its extended thinking feature. But before deploying it in production, you need to test it for security vulnerabilities.

This guide shows you how to quickly red team Claude 4 Sonnet using Promptfoo, an open-source tool for adversarial AI testing.

While generative AI creates new opportunities for companies, it also introduces novel security risks that differ significantly from traditional cybersecurity concerns. This requires security leaders to rethink their approach to protecting AI systems.

Fortunately, OWASP (Open Web Application Security Project) provides guidance. Known for its influential OWASP Top 10 guides, this non-profit has published cybersecurity standards for over two decades, covering everything from web applications to cloud security.

Promptfoo's initial red teaming scans against DeepSeek-R1 revealed significant vulnerabilities, particularly in its handling of harmful and toxic content.

We found the model to be highly susceptible to jailbreaks, with the most common attack strategies being single-shot and multi-vector safety bypasses.

Deepseek also failed to mitigate disinformation campaigns, religious biases, and graphic content, with over 60% of prompts related to child exploitation and dangerous activities being accepted. The model also showed concerning compliance with requests involving biological and chemical weapons.