Misinformation in LLMs—Causes and Prevention Strategies
Misinformation in LLMs occurs when a model produces false or misleading information that is treated as credible. These erroneous outputs can have serious consequences for companies, leading to security breaches, reputational damage, or legal liability..
Latest Posts
Sensitive Information Disclosure in LLMs: Privacy and Compliance in Generative AI
Imagine deploying an LLM application only to discover it's inadvertently revealing your company's internal documents, customer data, and API keys through seemingly innocent conversations.
Understanding AI Agent Security
In an earlier blog post, we discussed the use-cases for RAG architecture and its secure design principles.
What are the Security Risks of Deploying DeepSeek-R1?
Promptfoo's initial red teaming scans against DeepSeek-R1 revealed significant vulnerabilities, particularly in its handling of harmful and toxic content..
1,156 Questions Censored by DeepSeek
DeepSeek-R1 is a blockbuster open-source model that is now at the top of the U.S.
How to Red Team a LangChain Application
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..
Defending Against Data Poisoning Attacks on LLMs: A Comprehensive Guide
Data poisoning remains a top concern on the OWASP Top 10 for 2025.
Jailbreaking LLMs: A Comprehensive Guide (With Examples)
Let's face it - LLMs are gullible.
Beyond DoS: How Unbounded Consumption is Reshaping LLM Security
The recent release of the 2025 OWASP Top 10 for LLMs brought a number of changes in the top risks for LLM applications.
Red Team Your LLM with BeaverTails
Ensuring your LLM can safely handle harmful content is critical for production deployments.