Investigating multi-vector attacks in Log Explorer

Cloudflare has integrated Moonshot AI's Kimi K2.5 model into its Workers AI platform, enabling large models to power developer agents. The model's 256k context window and support for multi-turn tool calling make it suitable for various agentic tasks. By serving large models directly within the Cloudflare Developer Platform, the company aims to facilitate efficient and cost-effective agent development. The integration of Kimi K2.5 has resulted in significant cost savings, with a 77% reduction in costs for one particular agent that processes over 7B tokens per day. Cloudflare has also optimized its inference stack to serve large models efficiently, using custom kernels and advanced techniques to improve performance and GPU utilization. To support agentic workloads, Cloudflare has released new features, including prefix caching and surfacing cached tokens, as well as a new session affinity header to improve cache hit rates and reduce inference costs. These improvements make it easier for developers to build and deploy efficient and cost-effective agents using

Cloudflare introduces Custom Regions for precision data control, allowing customers to define their own geographical boundaries for traffic processing. This feature provides customers with more control over data localization and compliance, particularly useful for multinational companies with complex regional requirements. Cloudflare's Custom Regions use a flexible expression-based membership system, enabling customers to specify region membership and routing decisions.