NewCan a decision model be your guardrail?
We ran 97 real prompt-injection emails through 18 decision models and Bedrock. From release v1.0.0 of our benchmark: 19 systems on 9,975 checks, 7,706 of them public.
Open-source research for AI governance and security, and the threat intelligence our products are built on.
NewWe ran 97 real prompt-injection emails through 18 decision models and Bedrock. From release v1.0.0 of our benchmark: 19 systems on 9,975 checks, 7,706 of them public.
The foundational AI security knowledge base behind raxIT. Eight public feeds, hundreds of evidence-backed threat patterns that grow with every incident, and a 58-rule open-source scanner. This is why our AI security harness gets better every week instead of aging.
An open-source safety benchmark for the prompts where the right answer is neither a refusal nor full compliance. The gray zone, where real deployment decisions happen.
An incident-response simulation engine that clones your organisation as AI agents, drops a threat actor into the room, and runs the attack dozens of times so you get a probability distribution instead of a single story.
The first credible LLM explainability primitive we have seen in three years, wired into a prompt-engineering loop. Reads what an open-weight model is computing at each token, so you can stop when output and thought disagree.