16 posts tagged with "red-teaming"

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.

Want to test your LangChain application for vulnerabilities? This guide shows you how to use Promptfoo to systematically probe for security issues through adversarial testing (red teaming) of your LangChain chains and agents.

You'll learn how to use adversarial LLM models to test your LangChain application's security mechanisms and identify potential vulnerabilities in your chains.

Let's face it - LLMs are gullible. With a few carefully chosen words, you can make even the most advanced AI models ignore their safety guardrails and do almost anything you ask.

As LLMs become increasingly integrated into apps, understanding these vulnerabilities is essential for developers and security professionals. This post examines common techniques that malicious actors use to compromise LLM systems, and more importantly, how to protect against them.