22 posts tagged with "red-teaming"

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.

Want to test the safety and security of a model hosted on Ollama? This guide shows you how to use Promptfoo to systematically probe for vulnerabilities through adversarial testing (red teaming).

We'll use Llama 3.2 3B as an example, but this guide works with any Ollama model.

Here's an example of what the red team report looks like:

Want to break a HuggingFace model? This guide shows you how to use Promptfoo to systematically probe for vulnerabilities through adversarial testing (red teaming).

You'll learn how to craft prompts that bypass safety filters and manipulate model outputs for a wide range of potential harms.

We're excited to announce the deployment of Promptfoo's latest strategy, GOAT, to jailbreak multi-turn conversations.

Fuzzing has been a tried and true method in pentesting for years. In essence, it is a method of injecting malformed or unexpected inputs to identify application weaknesses. These payloads are typically static vectors that are automatically pushed into injection points within a system. Once injected, a pentester can glean insights into types of vulnerabilities based on the application's responses. While fuzzing may be tempting for testing LLM applications, it is rarely successful.