Displaying Application Study Tool (AST) Dashboards in Your Own Grafana Instance
The Application Study Tool (AST) has its own Prometheus and Grafana instances. These instances run as containers and are designed to coexist with other Prometheus and Grafana instances in your environment, even on the same host. However, during demos and discussions with customers, many have expressed the desire to use their existing Grafana instance to display AST dashboards. Although it may not be obvious to new Grafana users, this process is straightforward. This blog will walk you through launching a second generic Grafana container instance, connecting it to the AST instance of Prometheus (the data source), importing a dashboard from the AST instance of Grafana, and displaying it in the new Grafana instance. If you already have a non-AST instance of Grafana running in your environment, the steps to launch a second Grafana container are optional. However, you may want to run it in order to test the import functionality and make your own customizations before importing it again into your “production” Grafana instance. Here is an example of a dashboard folder in a non-AST Grafana instance after importing three dashboards from AST: Launch a Second (Generic) Grafana Container If you already have a Grafana instance, you may skip this step. However, if you don’t, or you would like to use a “sandbox” for testing customizations before importing the dashboard into your “production“ Grafana instance, you can use the following steps to launch a new Grafana container. The following assumptions are made for the steps that follow: You are using Docker as your container runtime. (If you are using Podman, simply substitute “podman” for “docker” in each of the following commands. Other container runtimes may also work for this exercise, but I have not tested them.) You have sufficient privileges to run containers. If you don’t, you may need to run these commands with “sudo”. If that fails due to permissions errors, you will need to request the necessary privileges from your Linux administrator. We want to run Grafana version 11.5.2. Any recent version should work. However, this is the latest version as of the writing of this blog. The IP address of the host where you are running these containers is 192.168.0.15. Yours will likely be different. Use your own host’s IP when you run “curl” inside the grafana2 container. In my testing, I used MacOS. This will also work on any current Linux distribution and should work on Windows. First, launch the Grafana container. I set this new instance of Grafana to listen on port 3002 (the default for Grafana is 3000) to avoid conflicts with the AST instance, if they are running on the same host. $ docker run -d --name=grafana2 -p 3002:3000 grafana/grafana:11.5.2 Next, exec into the container to ensure it can connect to the AST instance of Prometheus. You can instead check connectivity from the Grafana UI, but the below method is a good way to troubleshoot any connectivity errors you may encounter. $ docker exec -it grafana2 bash You are now running a Bash shell inside the new Grafana container. Run a curl command to confirm the new Grafana container can reach the Prometheus application, which listens on port 9090, by default. (The IP address, 192.168.0.15, is used as an example. Use your own host's IP address here.) 5d3e8256af3d:/usr/share/grafana$ curl 192.168.0.15:9090 <a href="/graph">Found</a>. Now, it is time to test the new Grafana instance. Open a web browser and navigate to the host where this new Grafana container is running, at port 3002. If you are running on your local machine, it will be http://localhost:3002/. The default credentials are admin/admin. When first logging in, Grafana will prompt you to change the password. You may choose to change it now or click “skip” to leave it as is. Now you can export one of the dashboards from AST and import it into this instance. Export a Dashboard from AST Now that you have launched a second instance of Grafana (or you are running your own non-AST instance), it is time to import a dashboard from AST. You can import just one dashboard of your choosing (i.e., BigIP - Device Device >> Virtual Servers), or several (or even all) dashboards from AST. For this example, we will only import one dashboard, BigIP - Device Device >> Virtual Servers. If you wish to import other dashboards, the steps are the same. Navigate to the dashboard you would like to import into your Grafana instance. For the example used here, navigate to Dashboards >> BigIP – Device >> Device Virtual Servers. Click the blue "Share" button near the upper-right corner. In the pop-up box, click the Export tab. Click the blue "Save to file" button to download the JSON file representing the dashboard. Two notes: If you wish to use your own non-AST instance of Prometheus, you will need to move the slider for “Export for sharing externally” (available in the Share pop-up box, under the Export tab) to the right to enable it. This will allow you to select your own Prometheus instance as the data source when importing the dashboard into the alternate Grafana instance. The default JSON for these dashboards is also available in “dashboards” folder of the repo: https://github.com/f5devcentral/application-study-tool/tree/main/services/grafana/provisioning/dashboards. This version has the “Export for sharing externally” option enabled, so you will need to select the desired Prometheus data source – either your own or the AST instance – when importing the dashboard into the alternate Grafana instance. Import the Dashboard into the New (or Existing) Grafana Instance If you have just launched a new, generic Grafana container using the instructions in the above section, Launch a Second (Generic) Grafana Container, you can now launch the UI from a web browser by navigating to http://localhost:3002/ (assuming you are running on your local machine). The default login credentials are admin/admin. If this is just a temporary test instance, you may click “skip” when prompted to “Update your password”. (For a production instance or any instance that will be used more than just briefly, we recommend changing this to a stronger password.) If you are using an existing Grafana instance, navigate to it and log in. Connect the New Grafana Instance to the AST Prometheus Instance From this non-AST Grafana instance, verify the Prometheus data source is reachable from Grafana, and then connect to it by following these steps: In the menu bar on the left, click Connections >> Data sources. If this is a new instance of Grafana, the “Add data source” button will appear in the middle of the screen. If this is an existing instance with pre-existing data sources, the button will be in the upper-right corner of the screen and will say “Add new data source”. Click on it. Select Prometheus from the list of data sources. You may have to scroll down or enter “prometheus” in the search bar. Fill in a name (for example, “ast-prometheus”), and the URL to connect to the Prometheus instance. In my case, it was my host's private IP address, 192.168.0.15, and the port Prometheus is listening on (9090 by default): http://192.168.0.15:9090. Set the “Interval behaviour >> Scrape interval” to be the same as the value used for the collection_interval setting in your AST configuration. If you did not explicitly change it when configuring AST, it will be the default value of 60s. Click the blue "Save & test" button and ensure you get the message, “Successfully queried the Prometheus API” at the bottom of the screen. Import the Dashboard into the New Grafana Instance Click on “Dashboards” in the menu on the left. Click the blue “New” button in the upper-right and, from the drop-down, select "Import". Click on "Upload dashboard JSON file" and upload the JSON file you previously exported from the original AST dashboard. Give it a name (under Name). Under the Prometheus drop-down, select your Prometheus data source. (In the example above, it is called "ast-prometheus". If you accept the default name, it will just be “prometheus”.) Click Import. Voilà! You are now taken to the newly imported Grafana dashboard. Conclusion The Application Study Tool offers excellent observability for F5 BIG-IP systems and the traffic they handle. If you have your own Grafana instance with your own set of dashboards, there is no need to manage two separate instances. You can combine the two so you have all your dashboards in one place. The flexibility of Grafana also allows it to be highly customizable, so you can modify any of the out-of-the-box dashboards AST provides and even create your own. If you have gotten value from customizing some of the default AST dashboards, feel free to post what you did below, as many of our readers will find this valuable.How I Did It: Simplifying BIG-IP Telemetry for Third-Party Observability Platforms
In this article, I introduce two community-supported projects designed to help customers get more value from BIG-IP telemetry. The BIG-IP Telemetry Streaming Validator and Configurator simplifies the deployment of Telemetry Streaming, while the BIG-IP Telemetry Exporter helps integrate BIG-IP metrics and logs into existing observability platforms such Grafana, Splunk, Elastic, Datadog, and other modern analytics ecosystems utilizing OpenTelemetry.Protecting your MCP Server from AI Vulnerabilities with F5 BIG-IP Advanced WAF JSON Schema Validation
This demo shows how a JSON schema can inform the BIG-IP how requests should be expected to come in. The F5 BIG-IP Advanced WAF can mitigate MCP server vulnerabilities by sanitizing parameters in requests allowing them to pass through to the MCP server. This demo covers two vulnerabilities from OWASP MCP Top 10. For more info on the OWASP MCP Top 10 vulnerabilities, click here. MCP02: Privilege Escalation via Scope Creep Scope creep can occur intentionally for convenience or accidentally through configuration drift allowing an agent to gain broad or administrative privileges to our MCP Server. As MCP servers connect to multiple systems, scope increases can result in a high-impact attack surface. Due to the nature of AI agents, an over-privileged agent can make unlabeled changes, trigger deployments, or access sensitive data without human review. For more information on OWASP MCP02: Privilege Escalation via Scope Creep, click here. MCP03: Tool Poisoning Schema poisoning occurs when an adversary tampers with the contract or schema definitions that govern agent-to-tool interactions in an MCP ecosystem. Schemas define the shape, types, and semantics of requests and responses — effectively the “language” agents use to call tools. If an attacker can modify a schema (or its metadata) so that a benign-sounding operation maps to a destructive action, agents that trust and follow the schema may inadvertently execute dangerous commands. Schema attacks are a supply-chain style compromise: the attacker doesn’t exploit a code bug directly, they change the contract so legitimate agents behave incorrectly while passing superficial validation. For more information on OWASP MCP03: Tool Poisoning, click here. Mitigating MCP02 and MCP03 Enforcing JSON Schema validation at the BIG-IP can mitigate requests that may allow excessive privilege to resources on our MCP server. This method gives security admins more control over how their MCP server can be used. In this video, we will demonstrate how an attacker can exploit MCP02 and MCP03 to gain access to resources unintended for them. Subsequently, we use JSON Schema validation to inform the F5 BIG-IP Advanced WAF's security policy on how a security admin would intend those resources to be accessed. Check out the video below for a demonstration of how the F5 BIG-IP's JSON Schema validation can mitigate MCP 02 and MCP 03.
Enhancing AI Data Pipelines with BIG-IP v21: Discover S3 Integration
F5 BIG-IP v21 revolutionizes AI data pipelines with advanced support for S3-compatible object storage, enabling enterprises to optimize, secure, and scale AI and analytics workflows seamlessly. By introducing S3-tuned traffic profiles, intelligent load balancing, and robust health monitoring, BIG-IP ensures predictable performance, resiliency, and protection against protocol-specific threats. This transformative delivery layer empowers businesses to handle complex workloads efficiently, making AI-driven innovation faster, smoother, and more reliable than ever.
Mitigating OWASP Web Application Insecure Design using F5 BIG-IP Advanced WAF
This article provides OWASP Top 10 Insecure Design caused due to improper planning, logic in the application. These risks allows Web crawlers, automated bots etc. to cause web scraping attack. This article also provides mitigation steps by F5 BIG-IP using Advanced WAF protection.How I did it - “Delivering Kasm Workspaces three ways”
Securing modern, containerized platforms like Kasm Workspaces requires a robust and multi-faceted approach to ensure performance, reliability, and data protection. In this edition of "How I did it" we'll see how F5 technologies can enhance the security and scalability of Kasm Workspaces deployments.
PQC: a blindspot (logging) on BIG-IP - RFE
On BIG-IP it's currently not possible to log information such as proposed and negotiated Key Exchange Algorithm. No available iRule commands for that. On the other hand, NGINX do offer the possible to log it ($ssl_curve/$ssl_curves variables). I've got a RFE created for the following: Provide new iRule commands in events CLIENTSSL_CLIENTHELLO and CLIENTSSL_HANDSHAKE that outputs the Key Exchange Algorithm: - list of proposed (by the SSL client) Key Exchange Algorithms in CLIENTSSL_CLIENTHELLO - negotiated Key Exchange Algorithms in CLIENTSSL_HANDSHAKE => RFE ID2347153 — "iRule command equivalent to NGINX's $ssl_curve for logging the negotiated KEM/DH group" Don't hesitate to open a support case to bind it to that RFE, the more we are the higher priority will be assigned to implement it (hopefully). 😀 AlexandreProtecting Your MCP Server From Secret Exposure With F5 BIG-IP Advanced WAF's Data Guard
The Threat of Token Mismanagement in MCP Servers Tokens and credentials serve as the backbone for authentication and authorization in MCP servers, yet their mishandling presents a significant security risk. Developers sometimes store these secrets insecurely, embedding them in configuration files or leaving them easily accessible. The inherent features of MCP—such as long-lived sessions, stateful agents, and persistent context—add complexity to this risk. Tokens can inadvertently be stored, retrieved, or indexed through user prompts, system recalls, or log inspections. This introduces a new vulnerability: contextual secret leakage, where the model or protocol layer unknowingly becomes a repository for sensitive information. Attackers can exploit this vulnerability to extract and misuse these exposed credentials, gaining unauthorized access production systems. Mitigating OWASP MCP01 with F5 BIG-IP Advanced WAF Data Guard Recognizing the gravity of this issue, OWASP has officially categorized Token Mismanagement and Secret Exposure in MCP servers under the MCP01 vulnerability class. This classification highlights the widespread nature of the threat and underscores the urgent need for tools like F5 BIG-IP Advanced WAF’s Data Guard. Although the long term solution is to correct token mismanagement at the backend servers, the F5 BIG-IP Advanced WAF’s Data Guard offers a quick and easy way to mitigate this vulnerability. By sanitizing server responses, Data Guard ensures that sensitive data—such as tokens—is never inadvertently exposed to unprivileged users. In the following video, we will see how token mismanagement can result in system error logs containing sensitive data. Subsequently, we demonstrate how we can utilize BIG-IP Advanced WAF Data Guard to sanitize these responses, thus mitigating OWASP MCP 01: Token Mismanagement & Secret Exposure. For more information on F5 Data Guard, click here. For a list of OWASP MCP Top 10 vulnerabilities, click here
