Defending the cloud: Azure neutralized a record-breaking 15 Tbps DDoS attack
On October 24, 2025, Azure DDOS Protection automatically detected and mitigated a multi-vector DDoS attack measuring 15.72 Tbps and nearly 3.64 billion packets per second (pps). This was the largest DDoS attack ever observed in the cloud and it targeted a single endpoint in Australia. By utilizing Azure’s globally distributed DDoS Protection infrastructure and continuous detection capabilities, mitigation measures were initiated. Malicious traffic was effectively filtered and redirected, maintaining uninterrupted service availability for customer workloads. The attack originated from Aisuru botnet. Aisuru is a Turbo Mirai-class IoT botnet that frequently causes record-breaking DDoS attacks by exploiting compromised home routers and cameras, mainly in residential ISPs in the United States and other countries. The attack involved extremely high-rate UDP floods targeting a specific public IP address, launched from over 500,000 source IPs across various regions. These sudden UDP bursts had minimal source spoofing and used random source ports, which helped simplify traceback and facilitated provider enforcement. Attackers are scaling with the internet itself. As fiber-to-the-home speeds rise and IoT devices get more powerful, the baseline for attack size keeps climbing. As we approach the upcoming holiday season, it is essential to confirm that all internet-facing applications and workloads are adequately protected against DDOS attacks. Additionally, do not wait for an actual attack to assess your defensive capabilities or operational readiness—conduct regular simulations to identify and address potential issues proactively. Learn more about Azure DDOS Protection at Azure DDoS Protection Overview | Microsoft LearnSecurity Review for Microsoft Edge version 143
We have reviewed the new settings in Microsoft Edge version 143 and determined that there are no additional security settings that require enforcement. The Microsoft Edge version 139 security baseline continues to be our recommended configuration which can be downloaded from the Microsoft Security Compliance Toolkit. Microsoft Edge version 143 introduced 3 new Computer and User settings; we have included a spreadsheet listing the new settings to make it easier for you to find. As a friendly reminder, all available settings for Microsoft Edge are documented here, and all available settings for Microsoft Edge Update are documented here. Please continue to give us feedback through the Security Baselines Discussion site or this post.Host remote MCP servers on Azure Functions
Model Context Protocol (MCP) servers allow AI agents to access external tools, data, and systems, greatly extending the capability and power of agents. When you’re ready to expose your MCP servers externally, within your organization or to the world, it’s important that the servers are run in a secure, scalable, and reliable environment. Azure Functions provides such a robust platform for hosting your remote MCP servers, offering high scalability with the Flex Consumption plan, built‑in authentication feature for Microsoft Entra and OAuth, and a serverless billing model. The platform also offers two hosting options for added flexibility and convenience. The options allow for hosting of MCP servers built with Azure Functions MCP extension or the official MCP SDKs. Azure Functions MCP Extension (GA) The MCP extension allows you to build and host servers using Azure Functions programming model, i.e. using triggers and bindings. The MCP tool trigger allows you to focus on implementing tools you want to expose, instead of worrying about handling protocol and server logistics. The MCP extension launched as public preview back in April and is now generally available, with support for .NET, Java, JavaScript, Python, and Typescript. New features in the extension Support for streamable-http transport Support for the newer streamable-http transport is added to the extension. Unless your client specifically requires the older Server-Sent Events (SSE) transport, you should use the streamable-http. The two transports have different endpoints in the extension: Transport Endpoint Streamable HTTP /runtime/webhooks/mcp Server-Sent Events (SSE) /runtime/webhooks/mcp/sse Defining server information You can use the extensions.mcp section in host.json to define MCP server information. { "version": "2.0", "extensions": { "mcp": { "instructions": "Some test instructions on how to use the server", "serverName": "TestServer", "serverVersion": "2.0.0", "encryptClientState": true, "messageOptions": { "useAbsoluteUriForEndpoint": false }, "system": { "webhookAuthorizationLevel": "System" } } } } Built-in server authentication and authorization The built-in feature implements the requirements of the MCP authorization protocol, such as issuing 401 challenge and hosting the Protected Resource Metadata document. You can configure it to use identity providers like Microsoft Entra for server authentication. In addition to server authenticating, you can also leverage this feature to implement on-behalf-of (OBO) auth flows where the client invokes a tool that accesses some downstream services on-behalf-of the user. Learn more about the built-in authentication and authorization feature. Mavin Build Plugin for Java For Java applications, the Maven Build Plugin (version 1.40.0) parses and verifies MCP tool annotations during build time. This process automatically generates the correct MCP extension configuration, ensuring that the MCP tool defined by the user is properly set up. The build-time analysis is especially beneficial for Java apps, as it allows developers to utilize the MCP extension without concerns about increased cold start times. We'll continuously enhance the plugin’s capabilities. Upcoming improvements, such as property type inference, will reduce manual configuration and make it even easier to use the McpToolTrigger. Get started Checkout the quickstarts to get an MCP extension server deployed in minutes: C# (.NET) remote-mcp-functions-dotnet Python remote-mcp-functions-python TypeScript (Node.js) remote-mcp-functions-typescript Java remote-mcp-functions-java References Learn more about the MCP extension and tool trigger in official documentations. Self‑hosted MCP server (public preview) In addition to the MCP extension, Azure Functions also supports hosting MCP servers implemented with the official SDKs. This is a suitable option for teams that have existing SDK‑based servers or who favor the SDK experience over the Functions programming model. There is no need to modify your server code; you can lift and shift these MCP servers to Azure Functions— which is why they are termed self‑hosted. The hosting capability supports the following features: Stateless servers that use the streamable-http transport. If you need your server to be stateful, consider using the Functions MCP extension for now. Servers implemented with Python, TypeScript, C#, or Java MCP SDK. Built-in server authentication and authorization like the MCP extension Hosting requirement Self-hosted MCP servers are deployed to the Azure Functions platform as custom handlers. You can think of custom handlers as lightweight web servers that receive events from the Functions host. The only requirement for hosting the MCP server is a file called host.json. Add this file to your project root to tell Functions how to run the server. An example host.json for a Python server looks like: { "version": "2.0", "configurationProfile": "mcp-custom-handler", "customHandler": { "description": { "defaultExecutablePath": "python", "arguments": ["path to main python script, e.g. hello.py"] }, "port": "8000" } } Get started Check out quickstarts to get your self-hosted MCP server deployed in minutes: C# (.NET) mcp-sdk-functions-hosting-dotnet Python mcp-sdk-functions-hosting-python TypeScript (Node.js) mcp-sdk-functions-hosting-node Java Coming soon! References Read the official documentation of self-hosted MCP servers and learn about integrations with Azure services like Foundry and API Center. For .NET developers - check out the overview of self-hosted MCP servers from the recent .NET Conference! We’d love to hear from you! Let us know your thoughts about hosting remote MCP server on Azure Functions. Does either of the options meet your needs? What other MCP features are you looking for? Let us know what you’d like us to prioritize next!
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Observability for the Age of Generative AI
Every generation of computing brings new challenges in how we monitor and trust our systems. With the rise of Generative AI, applications are no longer static code—they’re living systems that plan, reason, call tools, and make choices dynamically. Traditional observability, built for servers and microservices, simply can’t tell you when an AI agent is correct, safe, or cost-efficient. We’re reimagining observability for this new world. At Ignite, we introduced the next wave of Azure Monitor and AI Foundry integration—purpose-built for GenAI apps and agents. End-to-End GenAI Observability Across the AI Stack Customers can see not just whether their systems are up or fast, but also whether their agent responses are accurate. Azure Monitor, in partnership with Foundry, unifies agent telemetry with infrastructure, application, network, and hardware signals—creating a true end-to-end view that spans AI agents, the services they call, and the compute they run on. New capabilities include: Agent Overview Dashboard in Grafana and Azure – Gain a unified view of one or more GenAI agents, including success rate, grounding quality, safety violations, latency, and cost per outcome. Customize dashboards in Grafana or Azure Monitor Workbooks to detect regressions instantly after a model or prompt change—and understand how those changes affect user experience and spend. AI-Tailored Trace View – Follow every AI decision as a readable story: plan → reasoning → tool calls → guardrail checks. Identify slow or unsafe steps in seconds, without sifting through thousands of spans. AI-Aware Trace Search by Attributes – Search, sort, and filter across millions of runs using GenAI-specific attributes like model ID, grounding score, or cost. Find the “needle” in your GenAI haystack in a single query. Foundry Low-Code Agent Monitoring – Agents created through Foundry’s visual, low-code interface are now automatically observable. Without writing a single line of code, you can track reliability, safety, and cost metrics from day one. Full-Stack Visibility Across the AI Stack – All evaluations, traces, and red-teaming results are now published to Azure Monitor, where agent signals correlate seamlessly with infrastructure KPIs and application telemetry to deliver a unified operational view. Check out our get started documentation. Powered by OpenTelemetry Innovation This work builds directly on the new OpenTelemetry extensions announced in our recent Azure AI Foundry blog post. Microsoft is helping define the OpenTelemetry agent specification, extending it to capture multi-agent orchestration traces, LLM reasoning context, and evaluation signals—enabling interoperability across Azure Monitor, AI Foundry, and partner tools such as Datadog, Arize, and Weights & Biases. By building on open standards, customers gain consistent visibility across multi-cloud and hybrid AI environments—without vendor lock-in. Built for Enterprise Scale and Trust With open standards and deep integration between Azure Monitor and AI Foundry, organizations can now apply the same discipline they use for traditional applications to their GenAI workloads, complete with compliance, cost governance, and quality assurance. GenAI is redefining what it means to operate software. With these innovations, Microsoft is giving customers the visibility, control, and confidence to operate AI responsibly, at enterprise scale.
