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We are expanding the Clef decision model family with Clef-omni, natively processing audio, video, images, and text in a single pipeline. We’ve also lowered Clef-flash pricing and boosted Clef inference speeds by up to 2.0x.
We are introducing Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows. Also launching: a new reinforcement learning platform that allows developers to fine-tune decision models using their own data.
We built a WAF tester that adapted each request based on what the WAF blocked or passed. This helped us explore variations that a fixed test might miss. We ran it across six attack categories on an authorized staging environment and discovered detection gaps worth fixing. Here’s how the loop worked, what got through, and what we did about it.
A modern storefront can look healthy while malicious JavaScript quietly siphons revenue, hijacks clicks, or rewrites analytics. See how Cloudflare's machine learning models surface evasive client-side attacks for analyst investigation.
Bot operators have historically had the economic advantage, bypassing static, deterministic detection rules with cheap proxies and retooling. Cloudflare's new Adaptive Intelligence engine flips this dynamic by autonomously learning from the meta-signals of live traffic and deploying disposable rules, making automated attacks too expensive to sustain.
We are opening our advanced Client-Side Security tools to all users, featuring a new cascading AI detection system. By combining graph neural networks and LLMs, we've reduced false positives by up to 200x while catching sophisticated zero-day exploits.
By building and integrating a new heuristics framework into the Cloudflare Ruleset Engine, we now have a more flexible system to write rules and deploy new releases rapidly.
Learn more about how Cloudflare developed an AI model to uncover malicious JavaScript intent using a Graph Neural Network, from pre-processing data to inferencing at scale.
How Cloudflare uses generative AI to slow down, confuse, and waste the resources of AI Crawlers and other bots that don’t respect “no crawl” directives.
In this post we will describe how we use anomaly detection to watch for novel DDoS attacks. We’ll provide an overview of how we build models which flag unusual traffic and keep our customers safe.
This year for Cloudflare’s birthday, we’ve extended our AI Assistant capabilities to help you build new WAF rules, added new AI bot & crawler traffic insights to Radar, and given customers new AI bot
In this post, we discuss performance optimizations we've implemented for our WAF ML product. We'll guide you through code examples, benchmarks, and we'll share the impressive latency reduction numbers
To help preserve a safe Internet for content creators, we’ve just launched a brand new “easy button” to block all AI bots. It’s available for all customers, including those on our free tier
Cloudflare's Bot Management team has released a new Machine Learning model for bot detection (v8), focusing on bots and abuse from residential proxies
From identifying phishing attempts to protect applications and APIs, Cloudflare uses AI to improve the effectiveness of its security solutions to fight against new and more sophisticated attacks
E-commerce websites were targeted by a sophisticated Magecart attack, involving a hidden JavaScript code designed to secretly steal Personally Identifiable Information (PII) and credit card details from users
We recently shared an introduction to Cloudflare’s approach to MLOps, which provides a holistic overview of model training and deployment processes at Cloudflare. In this post, we will dig deeper into monitoring, and how we continuously evaluate the models that power Bot Management
Brand Protection's Logo Matching feature enables users to upload an image of the user’s logo or other brand image. The system scans URLs to discover matching logos and then presents the results for users to review
