Protect against SharePoint CVE-2025-53770 with Azure Web Application Firewall (WAF)
Summary Microsoft recently disclosed CVE-2025-53770, a critical vulnerability affecting on-premises SharePoint Server versions 2016, 2019, 2010, 2013, and Subscription Edition (SE). The vulnerability allows unauthenticated remote code execution (RCE) by chaining two separate CVEs: CVE-2025-49706 – Authentication Bypass CVE-2025-49704 – Deserialization Vulnerability Microsoft has released security updates for SharePoint Server 2016, 2019, and SE. Versions 2010 and 2013 are out of support and will not receive patches, leaving them exposed. If exploited, this vulnerability could allow an attacker to bypass authentication, extract cryptographic keys, and execute arbitrary C# code on the server. Technical details On-premises SharePoint Servers are enterprise-grade collaboration platforms that organizations install and manage on their own infrastructure, typically in their data centers. The attack chain for CVE-2025-53770 involves the following steps: CVE-2025-49706 – Authentication Bypass The attacker sends a crafted POST request targeting the endpoint:/_layouts/15/ToolPane.aspx?DisplayMode=Edit&a=/ToolPane.aspx with a malicious Referer value:/_layouts/SignOut.aspxThis manipulates SharePoint into trusting the request and its payload. CVE-2025-49704 – Deserialization Vulnerability The attacker then sends a POST request with a serialized spinstall0.aspx payload, designed to extract MachineKey values from web.config. These keys are then used to craft a serialized C# code payload embedded in a valid __VIEWSTATE, which SharePoint trusts and executes. Microsoft guidance We strongly recommend following Microsoft's official mitigation steps outlined in the MSRC blog: Customer guidance for SharePoint vulnerability CVE-2025-53770 | Microsoft Security Response Center See the “How to protect your environment” section for patching guidance, configuration updates, and additional mitigation strategies. Protecting with Azure Web Application Firewall You can create a custom rule to help detect and block suspicious requests matching known indicators of this attack. Example WAF custom rule: Condition 1: URI contains / _layouts/15/ToolPane.aspx or / _layouts/15/spinstall0.aspx Condition 2: Referer header contains / _layouts/SignOut.aspx or / _layouts/15/SignOut.aspx JSON view "customRules": [ { "name": "CVE202553770", "priority": 100, "ruleType": "MatchRule", "action": "Block", "matchConditions": [ { "matchVariables": [ { "variableName": "RequestUri" } ], "operator": "Regex", "negationConditon": false, "matchValues": [ "(?i)/_layouts(?:/\\d+)?/(SignOut|spinstall0|ToolPane)\\.aspx" ], "transforms": [] }, { "matchVariables": [ { "variableName": "RequestHeaders", "selector": "Referer" } ], "operator": "Regex", "negationConditon": false, "matchValues": [ "(?i)/_layouts(?:/\\d+)?/(SignOut|spinstall0|ToolPane)\\.aspx" ], "transforms": [] } ], "skippedManagedRuleSets": [], "state": "Enabled" } ] Next steps Patch immediately: Apply Microsoft’s updates for SharePoint 2016, 2019, and SE. Isolate legacy systems: SharePoint 2010 and 2013 remain vulnerable—consider restricting network access or migrating to supported versions. Deploy WAF protections: Add the custom rule above to monitor and block suspicious traffic targeting vulnerable endpoints. You can find more information about Custom Rules on Azure WAF for Application Gateway here or for Azure Front Door here. For more on Azure WAF, see: Azure Web Application Firewall documentationOptimize Azure Firewall logs with selective logging
A common question from customers is whether Azure Firewall supports filtering or selecting which logs are sent to a Log Analytics workspace. This concern usually stems from the high cost of storing large volumes of data — especially in environments where the firewall inspects substantial amounts of network traffic. Azure Firewall now supports ingestion-time transformation of logs in Azure Log Analytics. This capability introduces selective and advanced filtering, giving customers more control over what data is collected and analyzed. In this blog post, we’ll explore a major new capability: Azure Firewall now supports ingestion-time transformations in Log Analytics — enabling flexible, cost-efficient logging through selective data collection. Why does it matter? For many enterprise customers, the cost of ingesting Azure Firewall logs into Log Analytics — especially at scale — can be significant. Depending on the logging mode (Basic or Analytics), ingestion costs can be substantial, potentially making it challenging to expand logging coverage across additional workloads. With ingestion-time transformations, users can filter logs by rows, columns, timestamps, and more — and apply transformations before ingestion. This ensures that only relevant and critical data is stored, helping reduce costs while retaining the necessary telemetry for analysis, threat detection, and compliance. Customer benefits Security monitoring: Log only suspicious traffic for more targeted threat detection. Cost optimization: Avoid ingesting and storing unnecessary data. Compliance: Use DCR (data collection rules) to filter and route logs to meet audit/reporting needs. Incident response: Focus on logs that matter, accelerating investigation time. Custom alerts: Build insights on top of curated, high-value logs. What are transformations in Azure Monitor? Ingestion-time transformations in Azure Monitor allow you to filter or modify incoming telemetry before it reaches your Log Analytics workspace. This happens in the cloud pipeline — after the data source (such as Azure Firewall) sends its logs, but before those logs are ingested and stored. Transformations are defined using DCR and written in Kusto Query Language (KQL). Each transformation runs against incoming records individually, letting you precisely control what gets stored – and what doesn’t. For example, you might collect only entries where the action column contains the word “deny”. That filter can be applied at ingestion time, so only those critical logs are stored. The diagram below shows how this works end-to-end, from data source to filtered ingestion. To learn more and estimate potential processing charges, refer to the official documentation. Transforming Azure Firewall logging In this section, we’ll walk through a few real-world use cases shared by customers — including how to create a DCR based on specific filtering criteria. Important: Ingestion-time transformations for Azure Firewall logs are supported only when using resource-specific logs. If you’re using legacy diagnostic settings, this capability is not available. To enable transformations, ensure your firewall is configured to send logs using the Azure Firewall resource-specific log schema. First, navigate to your Log Analytics workspace and locate the table where your Azure Firewall logs are stored (e.g., AZFWApplicationRule). Click the three-dot menu (…) on the right and select “Create transformation”. Creating a transformation is a 3 steps-process. Step 1 – Basics: Create a DCR to define how incoming data is processed and specify where it should be stored. Step 2 – Schema and transformation: Use the Transformation Editor to write a KQL query that filters the incoming data based on your criteria. Step 3 – Review: Review the table name, DCR name, and KQL query before clicking “Create”. This summary ensures everything is configured correctly. For more information on how to create a DCR, refer to the official documentation. Use case 1: Excluding alerts from low priority IDPS signatures This DCR transformation filters and reshapes incoming Azure Firewall IDPS logs before they're ingested into a Log Analytics workspace. source | where Action !contains "alert" and Severity != 3 | project TimeGenerated, Protocol, SourceIp, SourcePort, DestinationIp, DestinationPort, Action, SignatureId, Description, Severity Here's a breakdown of what it does: source: This refers to the incoming data stream — in this case, the AZFWIdpsSignature table (intrusion detection/prevention logs from Azure Firewall). | where Action !contains "alert" and Severity != 3: This line filters out any log entries where the Action contains "alert" (non-blocking detection events). Any entries where Severity equals 3 (which represents low severity events). The result: We’re keeping only more actionable or higher-severity entries that don’t just raise alerts but may involve blocks or higher-severity behaviors (e.g., deny actions, critical or warning severities). | project ...: The project statement selects and forwards only the specified columns to the Log Analytics workspace. When you run a query in your Log Analytics workspace, you’ll notice that only the specific columns defined in your transformation’s project statement are available — and they appear in the exact order specified in the query. Use case 2: Filtering out unnecessary logs (trusted or testing networks) This DCR transformation filters out log entries from specific source IP address ranges before they're ingested into Azure Monitor. In this scenario, the 10.0.200.x and 10.0.300.x ranges might represent trusted or test network segments that generate high volumes of traffic — traffic that don’t need to be logged. By excluding these IPs at ingestion time, you can significantly reduce unnecessary log volume and associated costs. source | where not( SourceIp startswith "10.0.200." or SourceIp startswith "10.0.300." ) | project TimeGenerated, Protocol, SourceIp, SourcePort, DestinationIp, DestinationPort, Action, ActionReason, Policy, RuleCollection, Rule Here's a breakdown of what it does: source: This refers to the incoming data stream — in this case, the AZFWNetworkRule table. | where not (…): Applies a filter to exclude logs that match the criteria inside. SourceIp startswith "10.0.200." and SourceIp startswith "10.0.300.": These conditions match any log where the SourceIp address falls within the 10.0.200.0/24 or 10.0.300.0/24 subnets (i.e., IPs like 10.0.200.1, 10.0.200.45, etc.). | project ...: The project statement selects and forwards only the specified columns to the Log Analytics workspace. Conclusion By leveraging ingestion-time transformations through DCR, organizations gain full control over which Azure Firewall logs are ingested in Log Analytics. This selective logging capability helps reduce noise, cut costs, and retain only high-value data for security, compliance, and operational insights. As Azure Firewall evolves these enhancements offer greater flexibility and efficiency for managing cloud-native network telemetry. Resources Azure updates | Microsoft Azure Monitoring data reference for Azure Firewall | Microsoft Learn Transformations Azure Monitor - Azure Monitor | Microsoft Learn Create a transformation in Azure Monitor - Azure Monitor | Microsoft Learn
Automating Enriched DDoS Alerts Using Logic Apps
In today’s digital world, Distributed Denial of Service (DDoS) attacks have become one of the most common and disruptive threats facing online applications and services. These attacks aim to overwhelm a target, typically a website, API, or server, by flooding it with massive volumes of traffic, rendering it slow or completely inaccessible. Azure DDoS Protection is Microsoft's cloud-native defense that helps safeguard public-facing endpoints hosted in Azure. It works by continuously monitoring traffic patterns at the network layer (L3 and L4) and applying mitigation techniques in real time when suspicious or anomalous activity is detected. Azure DDoS Protection is tightly integrated with the Azure platform and provides always-on traffic scrubbing without requiring any manual intervention. While Azure mitigates these attacks in the background, understanding who is attacking, which resources are targeted, and how often these events occur is helpful. This is where Azure Logic Apps shines. Azure Logic Apps is a powerful platform to simplify the integration and automation of multiple services that help you run your business workflows. You can run your custom code or use no code at all to get your workflows running. When combined with Log Analytics & KQL queries, Logic Apps can help you extract critical insights from DDoS logs, including: Attack starts and end times Affected public IPs Top attacking IPs, countries, and ASNs Volume of traffic and packets dropped Attack patterns and frequency Application availability The result of the process is an email alert with details about the resource associated with the Public IP as detailed above. The owner of the resource is added as a recipient of the email, along with the security team who get alerted when the Attack occurs. Whether you're a security engineer, a product owner, or part of a cloud operations team, this solution can help you improve visibility and enhance coordination during DDoS incidents. Let’s dive into how this automation works. Here is the link to this template. Note: This template is an updated version of the same template discussed in this Blog- Enriching DDoS Protection Alerts with Logic Apps What this template contains: Log Search Alert rule Action Group Logic App Office 365 API Connector Azure Monitor Logs API Connector Parameters to Input when deploying: Security team's Email Address Company Domain (In the form of abc@domain.com) Workspace name (Name of the Log Analytics workspace being used) Prerequisites: A Public IP Address with DDoS Protection enabled either via IP Protection or Network Protection A Log Analytics Workspace to which the above Public IP Address should be sending Diagnostic logs, specifically all of the below categories: DDoS protection notifications Flow logs of DDoS mitigation decisions Reports of DDoS mitigations Note: The Log Analytics Workspace must reside in the same Resource Group as the one where this template is being deployed. 🔐Authentication Prerequisites: Azure Resource Graph The Logic App uses a Managed Identity to authenticate with Azure Resource Graph and query metadata about Azure resources Required Role: Logic App's Managed Identity will need Reader or higher access on the subscription (or resource group) that contains the Public IP address under DDoS protection Log Analytics Workspace To run Kusto queries and retrieve DDoS mitigation logs, the Logic App connects to Azure Log Analytics Workspace using the same Managed Identity Required Role: Logic App's Managed Identity will need Log Analytics Reader on the target workspace Office 365 (Email Notifications) API Connection For sending enriched alert emails, the Logic App uses an API connection to Office 365. This connection must be authorized to send emails on behalf of the configured account, specifically Mail.Send & User.Read permissions You must sign in and authorize this connection once during setup using the outlook credentials that you need it to use to send the emails If your tenant has admin consent policies, a Global Admin might need to approve use of the connectors (especially Office 365) for the Logic App Azure Monitor Logs API Connection This script queries Flow logs of DDoS mitigation decisions & Reports of DDoS mitigations To do this it needs AzureMonitorLogs API Connection and therefore, authorizing this is necessary for it to work as expected You must sign in and authorize this connection once during setup Firewall & Network Rules Ensure that: No IP restrictions block access from Logic App to the target services or public test URL in the HTTP step. You can find the outgoing IP Addresses here: Go to your Logic App Select Properties Look for the "Runtime outgoing IP addresses" section—these are your runtime IPs Now, let’s look at what each of the items in the Template does and their workings below in detail: Log Search Alert rule Monitors log data: It continuously scans the Azure Diagnostics logs, specifically targeting entries where the Category is DDoSProtectionNotifications and the type_s field indicates a Mitigation started event Runs on a schedule: The rule runs every 5 minutes and looks back at the last 30 minutes of logs. This ensures near-real-time detection of mitigation activity. (This can be modified as needed to increase the look back time if needed) Triggers on first sign of mitigation: If even one matching log entry is found (i.e., one mitigation event has started), the alert fires. This makes it extremely responsive Alerts through an Action Group: Once triggered, the rule calls a pre-defined Action Group, which will Invoke a webhook to notify a Logic App Why It’s Useful: While Azure DDoS Protection automatically mitigates volumetric and protocol attacks at the network edge, getting alerted when an event occurs requires user configuration. This is done by: Notifying your team the moment mitigation begins Adding observability, so you can correlate mitigation with service behavior or performance dips Action Group: Enrich-DDoSAlert — Connecting detection to automation When a DDoS attack is detected through an Azure Monitor alert, the response needs to be fast and efficient. That’s where Action Groups come in. In this case, the Enrich-DDoSAlert action group acts as the automation trigger for our DDoS response pipeline This action group is configured to call a webhook tied to an Azure Logic App using a secure HTTP POST request instantly when the alert fires. Then the Logic App carries out a series of enrichment and response steps based on the DDoS alert Why This Matters: The action group acts as a real-time bridge between detection and automation, triggering the Logic App instantly when an alert fires. The Action Group ensures that: The alert is captured Automation is triggered The investigation process starts without delay Logic App: Enrich-DDoSAlert Step-by-Step Breakdown Triggered via HTTP request Accepts a payload containing alert metadata such as: o Target resource ID o DDoS alert details o Search links and interval data Extracts impacted public IP and performs enrichment Using Azure Resource Graph, it queries the target IP to determine: o Associated Azure resource (VM, App Gateway, etc.) o DNS name, tags, region, resource group, and owner (from tags) Connectivity Check (Optional Validation) It performs an HTTP GET request to the DNS/IP of the attacked resource — checking if it’s still up or responding Generates an HTML-formatted email Using all this context, it builds a clean, readable email body that includes: o Top source IPs o IP under attack o Resource name/type o DNS name o Region o Tag info (owner, environment, etc.) o Link to Log Analytics search results o Status of connectivity test (code, headers, body) Queries Azure Monitor logs again (This time allows it to build a thorough DDoS Post Mitigation Report) After a 50-minute delay, it runs a query on the DDoS mitigation logs to extract: o Top source IPs o Top countries, ASNs, and continents o Time of mitigation o Traffic overview Note: This Delay is required but can be changed subtly. During this time, the post mitigation reports will be accumulated so it can be sent as an email in the next steps. Without this delay the reports will not populate correctly. Send a second email, titled "Post Mitigation DDoS Report", containing the above data. Post Mitigation Report plays a vital role in strengthening your defense strategy. By reviewing patterns in traffic origin, volume, and behavior, teams can: o Identify recurring attack sources or suspicious geographies o Correlate DDoS activity with other system anomalies o Fine-tune firewall and WAF rules based on attacker fingerprints In short, this enriched reporting not only enhances visibility but also enables teams to proactively adapt their security posture and reduce the impact of future attacks. Who gets notified? Office 365 API connector Both emails are sent using an authenticated Office 365 connector, delivered to the security team and tagged owner (which will be inputted during deployment). The high-priority email ensures visibility, while the second report gives retrospective clarity. Why this is useful: Reduces manual effort: No more pivoting across multiple tools to gather context Speeds up response: Teams get instant details Bridges Alert to Action: Combines signal (alert) with enrichment (resource graph + logs) and delivery (email) Customizable: You can adjust queries, recipients, or even trigger conditions Azure Monitor Logs API Connector The Azure Monitor Logs API Connector allows Logic Apps to query data from Log Analytics using Kusto Query Language (KQL). In this solution, it's essential for extracting DDoS-specific insights—such as top attacking IPs, countries, ASNs, and traffic volume—from diagnostic logs. What It Does in This Template: Executes KQL queries against your Log Analytics Workspace Retrieves: Flow logs from DDoSMitigationFlowLogs Mitigation reports from DDoSMitigationReports Delivers summarized data such as: Top attacker IPs Source ASNs and countries Mitigation start/end time Traffic patterns Here are some examples of the Automated & Enriched DDoS E-Mails: Potential Attack, First Email, as soon as an attack event is identified: Post Mitigation Summary Email: Conclusion: This Logic App doesn’t just automate alerting—it empowers your team with actionable context. By stitching together signals from Azure Monitor and Resource Graph, and packaging them into enriched, structured emails, it transforms raw alerts into informed decisions. Whether you're triaging incidents or conducting post-attack analysis, this setup ensures you're not starting from scratch each time. As attacks grow more complex, automation like this isn’t just nice to have—it’s essential. Start simple, adapt to your needs, and let your defenses work smarter.
Azure WAF Integration in Security Copilot is Now Generally Available
We’re excited to announce the general availability (GA) of Azure Web Application Firewall (WAF) integration with Microsoft Security Copilot. This marks a significant advancement in web application protection, bringing together Azure WAF’s industry-leading defense with the AI-powered capabilities of Security Copilot to transform how security teams detect, investigate, and respond to threats. Why This Integration Is a Game-Changer Modern web applications face relentless threats - from SQL injections and cross-site scripting (XSS) to bot attacks and sophisticated Layer 7 DDoS attempts. Defending against these threats requires more than just reactive measures; it demands intelligent, scalable solutions. With Azure WAF now integrated into Security Copilot, security teams can gain: Proactive threat analysis: Quickly uncover attack patterns and identify emerging threats. Optimized WAF configurations: Use AI insights to fine-tune rules and policies. Accelerated investigations: Leverage Copilot’s generative AI to streamline incident triage and response. This integration enables teams to work smarter and faster - turning raw data into actionable intelligence with the help of natural language prompts and AI-guided workflows. Seamless Protection Across Azure Platforms Azure WAF protects applications behind Azure Front Door and Azure Application Gateway, offering centralized, cloud-native security at scale. Now, with Security Copilot, analyzing WAF diagnostic logs no longer requires manual parsing or deep scripting expertise. Instead, AI delivers contextual insights directly to your SOC teams, cloud admins, and DevSecOps engineers. Whether you're investigating blocked requests or tuning security policies, this integration helps reduce operational overhead while strengthening your overall security posture. What Can You Do with Azure WAF in Security Copilot Let’s explore some of the core capabilities now available: SQL Injection (SQLi) Attack Analysis Understand why Azure WAF blocked specific SQLi attempts through detailed summaries of diagnostic logs and correlation of related events over time. Cross-Site Scripting (XSS) Attack Insights Get clear explanations for WAF’s enforcement actions against XSS attacks, with trend analysis across your environment. Top Offending IPs Analysis Identify the most malicious IPs triggering WAF rules, along with insights into the behaviors and rule patterns that led to their blocking. Most Triggered Rules and Actions Gain visibility into your most active WAF rules - helping prioritize tuning efforts and enhance threat detection effectiveness. These capabilities are designed to turn WAF data into actionable knowledge - without the need for custom queries or extensive log review. Built for the Future of Intelligent Security As threats continue to evolve, so must our defenses. The Azure WAF and Security Copilot integration represents the next generation of web application protection - combining automation, AI reasoning, and expert knowledge to deliver adaptive security at cloud scale. By augmenting your team with AI, you can stay ahead of attackers, protect critical apps, and respond faster than ever before. Learn More and Get Started The GA of Azure WAF integration in Microsoft Security Copilot is more than just a feature release - it’s a new paradigm for web application security. Explore the capabilities today by visiting the Azure WAF documentation. Want to talk to us? Reach out to the Azure WAF product team to share feedback or request a demo. Let’s build a more secure web, together.Maximizing savings with Azure Firewall and Azure Monitor basic table plan
The Azure Firewall Product Team has recently announced support for the new Log Analytics Basic table plan for all resource-specific logging tables, offering a potential reduction in logging costs by up to 80%. This new mode complements the existing 80% cost reduction achieved through structured/resource-specific logging, providing even greater savings. To learn more about the cost optimization introduced by resource-specific logs, check out the blog post Optimizing Azure Firewall logging costs | Microsoft Community Hub. While the new Basic table plan is beneficial for cost-conscious customers, it's important to note that Policy Analytics and Security Copilot integrations are not compatible with the Basic table plan. For more information on Basic tables, refer to the Azure Monitor Logs documentation. Customers have long expressed concerns about high logging costs, so we listened and have developed a new Basic table plan to meet those needs. The Basic table plan provides a more economical solution without sacrificing essential functionalities. This initiative highlights Azure Firewall's commitment to delivering value and efficiency, making it easier for customers to manage their logging needs affordably. When querying Basic tables, the cost is determined by the volume of data scanned, which depends on both the size of the table and the query's specified time range. Essentially, the data scanned refers to the amount of data ingested within the time frame set by the query for the targeted table. For example, if a query scans data over a three-day period in a table that ingests 100 GB daily, the charge would be based on 300 GB of data. Enabling the basic table plan The basic table plan is enhanced by resource-specific tables. To learn more about using structured/resource-specific logs, review the following documentation: Monitor Azure Firewall | Microsoft Learn. To enable the basic table plan, locate the tables under your Log Analytics Workspace, click on “Manage table,” and adjust the configuration as shown below. Note: The table plan can be updated only once every 7 days. Security Copilot and Policy Analytics To use the Security Copilot integration with Azure Firewall, ensure your IDPS log table (AZFWIdpsSignature) is configured in Analytics mode. The same applies to Policy Analytics on the following tables: AZFWApplicationRuleAggregation AZFWIdpsSignature AZFWNatRuleAggregation AZFWNetworkRuleAggregation AZFWThreatIntel If you are using both features, your configuration will look like this: In summary, the new Log Analytics Basic table plan offers significant cost savings for Azure Firewall users, while maintaining essential functionality. By configuring your tables correctly, you can take full advantage of these savings and optimize your logging strategy. Explore the documentation and start saving today!
Best Practices for Securing Access to VMs
Azure Bastion and Microsoft Entra PIM work together to secure VM access by eliminating the need for public IPs, enabling identity-based authentication, and enforcing Just-In-Time (JIT) access. Bastion provides secure RDP/SSH connections through Entra ID without local credentials, while Entra PIM ensures that users only receive time-limited, approved access. This combination supports a Zero Trust model by minimizing persistent privileges and reducing the overall attack surface.
