🛡 TL;DR: an open-source red teaming framework that calls itself "penetration testing, but for LLMs." Title: confident-ai/deepteam URL: 📌 Highlights 🎯 50+ vulnerabilities across 6 categories: data privacy, bias, authorization bypass, agent-specific risks and more ⚔️ 20+ attack methods, from prompt injection to multi-turn crescendo jailbreaks 📋 Aligned with OWASP Top 10 for LLMs/Agents, NIST AI RMF, MITRE ATLAS and other industry standards 🔒 Evaluation runs entirely locally, no data sent externally 🚦 Ships 7 real-time guardrails like ToxicityGuard for production use ⭐ 2.8k GitHub stars, 1,187+ commits and active ongoing development Just `pip install` it and pass your model callback, that low barrier to entry makes it easy to actually adopt. #LLMSecurity# #RedTeaming#

Automated Red Teaming with GOAT: the Generative Offensive Agent Tester "we introduce the Generative Offensive Agent Tester (GOAT), an automated agentic red teaming system that simulates plain language adversarial conversations while leveraging multiple adversarial prompting techniques to identify vulnerabilities in LLMs. We instantiate GOAT with 7 red teaming attacks, with an ASR@10 of 97% against Llama 3.1 and 88% against GPT-4" paper: (not peer reviewed)

Someone open-sourced an entire AI red-teaming lab G0DM0D3 lets researchers stress-test 60+ AI models with adversarial attacks, jailbreak testing, prompt transformations, and cross-model security comparisons.

New Anthropic research: Automating subtle emergent misalignment research. Red-teaming is a vital part of model safety testing, but it’s also limited by researchers’ time. To see if we could automate it, we set thousands of Claude agents the task of evading a training data monitor. These agents developed training data that looked harmless to monitors (like a veteran wrestler mentoring a newcomer, or a Q&A about professional beekeeping), but that in fact taught misaligned behavior. Examples like these can be used to directly improve future training data monitors. This research is only a first step, but it suggests that future red-teaming efforts could improve much further, using better models and more compute. Read the full post on the Alignment Science Blog:

I’ve been delaying so much to start studying and getting deep into AI Red Teaming and AI Agents security that I’m 95% convinced of start streaming on YouTube while I learn and practice. The idea would be that people keep me accountable and to go to a whole new level of learning in public. I prepared half a year ago a whole repo with resources in form of a path and journey through different existing platforms. I dare you to challenge me to an amount of streamed hours! Who wants to see me to fail at streaming for the first time ever!?!??

it's weird that openai will release the model behind recent math breakthroughs this early how much safety testing and red-teaming could they do on a model this powerful in such a short time? i mean they definitely want to steal some thunder from the fable 5.5 ahead of anthropic's IPO or this isn't the BIG Bell model we anticipated

anthropic is the smartest AI lab when it comes to acceleration right now all those months spent red-teaming and trying to understand model behavior seem to be paying off. they've gotten better at shipping frontier models safely while still moving faster than openai and that's interesting especially if both labs have similar automated research capacity

ANTHROPIC SAYS THE DEFENSE ACCESS TIER IS FOR DEFENSIVE WORK, THE RED TEAM ACCESS TIER ADDS AUTHORIZED PENETRATION TESTING AND RED-TEAMING TO CERTAIN DEFENSIVE USES, AND THE SPECIALIZED ACCESS TIER IS RESERVED FOR A LIMITED SET OF VERIFIED ORGANIZATIONS, FOR WHICH IT CURRENTLY REVIEWS EVERY ORGANIZATION IN DEPTH IN COLLABORATION WITH THE U.S. GOVERNMENT.