Red-Teaming

The All-AI-Jailbreaks repository is a curated, actively maintained archive of 19 prompt-injection and jailbreak experiment files spanning at least nine major model families, including DeepSeek, Gemini, GLM, Grok, Kimi, Qwen, Sonnet, ChatGPT, and Antigravity. It is best understood not as a “how to jailbreak” list but as a structured red-teaming corpus that maps directly onto the OWASP Top 10 for LLM Applications, where prompt injection ranks as LLM01:2025 — the number-one vulnerability in the industry. This review explains what the archive contains, how its five research themes align with the OWASP taxonomy, and how it fits the broader 2026 AI-security ecosystem of automated red-teaming frameworks and defensive proxies. ...

The threat surface for large language models has expanded beyond what most security teams anticipated three years ago. What began as a concern about chatbot misuse has evolved into a full-spectrum attack discipline targeting autonomous AI agents that browse the web, execute code, manage files, and call external APIs on behalf of users. This guide consolidates the current state of LLM red teaming as of 2026, covering the attack categories, specialized tooling, and operational processes that security teams need to protect AI-powered systems in production. ...

OpenAI acquiring PromptFoo is not a talent grab — it is a strategic acknowledgment that AI security testing is no longer optional infrastructure. With 93% of organizations now shipping AI-generated code and only 12% applying equivalent security standards, the attack surface is enormous and growing. PromptFoo was the most mature open-source tool purpose-built for LLM red-teaming, and OpenAI buying it means the company is betting that security evaluation needs to be a first-class part of the developer workflow, not an afterthought bolted on by a third-party CLI. ...