5 Technical Sessions That Should Be Great: F5 AppWorld 2025
These F5 Academy sessions explore modern app delivery, security, and operations. The full list of sessions is on the F5 AppWorld 2025 Academy page - if you haven't yet registered you can do so here: Register for F5 AppWorld 2025 LAB - F5 Distributed Cloud: Discovering & Securing APIs API security has never been more critical, and this lab dives straight into the tough stuff. Learn how to find hidden endpoints, detect sensitive data and authentication states, and apply integrated API security measures to keep your environment locked tight. TECHNICAL BRIEFING - LLM Security and Delivery with F5’s Distributed Cloud Security Ecosystem AI is fueling the next wave of applications—but it’s also introducing new security blind spots. This briefing explores how to secure LLMs and integrate the right solutions to ensure your AI-driven workloads remain fast, cost-effective, and protected. LAB - F5 NGINX Plus Ingress as an API Gateway for Kubernetes Containerized environments and microservices are here to stay, and this lab helps you navigate the complexity. Configure NGINX Plus Ingress as a powerful API gateway for your Kubernetes workloads, enabling schema enforcement, authorization, and rate-limiting all in one streamlined solution. LAB - Zero Trust at Scale With F5 NGINX Zero trust principles become a whole lot more meaningful when you can scale them. Get hands-on with NGINX Plus and BIG-IP GTM to build a robust, scalable zero trust architecture, ensuring secure and seamless app access across enterprises and multi-cluster Kubernetes environments. LAB - F5 Distributed Cloud: Security Automation & Zero Day Mitigation In this lab, you’ll learn how to leverage advanced matching criteria and custom rules to quickly respond to emerging threats. Shore up your defenses with automated policies that deliver frictionless security and agile zero-day mitigation. Session Updates Coming in January 🚨 AppWorld's Breakout Sessions officially drop in January 2025 but here is a sneak preview! Check back in January to add these to your agenda. Global App Delivery With a Global Network How Generative AI Breaks Traditional Application Security and What You Can Do About It The New Wave of Bots: A Deep Dive into Residential IP Proxy Networks From ZTNA to Universal ZTNA: Expanding Your App Security Strategy --- See you at F5 AppWorld 2025! #AppWorld25Protecting APIs with Access Policy Manager and custom iRules
The problem: Unprotected API - Vulnerable to Overload Without Rate-Limiting Enforcement Our customer in the B2B sector is encountering a challenge with their public API. Despite having implemented a custom method for generating long-lived API keys, they find themselves unable to enforce rate-limiting effectively. This absence of rate-limiting mechanisms poses significant challenges, potentially resulting in the overloading of their system due to excessive requests or the exploitation of their API by unauthorized users. Without proper rate-limiting controls in place, the customer faces risks to both the performance and security of their API infrastructure, necessitating a solution to mitigate these concerns and ensure the smooth operation of their services for their clients. Our customers wants to offer two tiers of service level agreements (SLAs) - gold and standard. Complicating matters further, the API key, integral to authentication, is transmitted via a custom HTTP header. The solution: BIG-IP APM and Custom iRules for Effective Rate-Limiting My solution involves leveraging the API Protection feature of BIG-IP APM in conjunction with a custom iRule. By utilizing this combination, our customer can effectively extract the API Keys from HTTP requests and enforce rate limiting on specific API endpoints. As for now they only want to enforce rate limiting on the POST endpoints. This approach empowers the customer to secure their API while efficiently managing and controlling access to critical endpoints, ensuring optimal performance and safeguarding against abuse or overload. With this iRule we can to extract the API key from the HTTP Requests and store it in a variable, that can later be used by the API Protection feature of the APM. API Keys and the associated SLA level are stored in a Data Groupof the typestring. # Enable (1) or disable (0) logging globally when RULE_INIT { set static::debug 1 } # Access and analyze the HTTP header data for SLA value when HTTP_REQUEST { set sla [class lookup [HTTP::header value apikey] dg_apikeys] if { $static::debug } {log local0. "Made it to HTTP_REQUEST event with SLA value $sla."} } # Evaluate SLA value during per-request access policy execution when ACCESS_PER_REQUEST_AGENT_EVENT { set id [ACCESS::perflow get perflow.irule_agent_id] if { $id eq "read-sla" } { if { $static::debug } {log local0. "Made it to iRule agent in perrequest policy with SLA value $sla."} ACCESS::perflow set perflow.custom "$sla" } } And this is how the Per Request Policy in the API Protection profile looks. It uses the value of the API Key (extracted with the help of the the iRule) and the Source IP of the client to enforce Rate Limiting on the POST endpoints, using two different SLAs. In the APM log you should see the following message, once the client exceeds his quota defined in the SLA. Apr 28 20:12:42 ltm-apm-16.mylab.local notice tmm[11094]: 01870075:5: (null):/Common: API request with weight (1) violated the quota in rate limiting config(/Common/demo_api_ratelimiting_auto_rate_limiting_standard). Apr 28 20:12:42 ltm-apm-16.mylab.local notice tmm[11094]: 0187008d:5: /Common/demo_api_ratelimiting_ap:Common:6600283561834577940: Execution of per request access policy (/Common/demo_api_ratelimiting_prp) done with ending type (Reject) Further reading: You can find a more detailed write-up on my GitHub page: https://github.com/webserverdude/f5_APM_API_Protection There you can find the Per Request Policy explained in all details. The Data Group with for the iRule. A demo API for testing my solution. A Postman Collection for working with my demo API.OWASP Tactical Access Defense Series: Broken Object Property Level Authorization and BIG-IP APM
AUTHOR NOTE: Unauthorized access to private/sensitive object properties may result in data disclosure, data loss, or data corruption. Under certain circumstances, unauthorized access to object properties can lead to privilege escalation or partial/full account takeover. In this article we are going through API3 item from OWASP top 10 API Security risks exploring BIG-IP Access Policy Manager (APM) role in our arsenal. Identifying Vulnerable APIs In order to identify the API endpoint is vulnerable to Broken Object Property Level Authorization, Sensitive properties exposure of certain object for non-intended user (Excessive Data Exposure). import requests # Assuming the API endpoint for retrieving user data is /api/users api_endpoint = "https://example.com/api/users" # Sending a GET request to the API endpoint response = requests.get(api_endpoint) # Checking if the request was successful (status code 200) if response.status_code == 200: # Printing the response content (which could contain excessive data) print(response.json()) else: print("Failed to retrieve data from the API") API allow to change, add or delete sensitive object property for non-intended user (Mass assignment). import requests # Assuming the API endpoint for updating user information is /api/users api_endpoint = "https://example.com/api/users" # Malicious payload containing additional fields malicious_payload = { "username": "malicious_user", "password": "password123", "isAdmin": True # Malicious user attempts to elevate privileges } # Sending a POST request with the malicious payload response = requests.post(api_endpoint, json=malicious_payload) # Checking if the request was successful (status code 200) if response.status_code == 200: print("User information updated successfully") else: print("Failed to update user information") Object Property Level Authorization involves controlling access to specific properties or attributes of an object within a system. Instead of granting blanket access to an entire object, this approach enables fine-grained control, allowing administrators to restrict or permit access to individual properties based on user roles or permissions. While implementing protection against such security risk involves different aspects, one is making sure the user is authorized to access object property, and here BIG-IP APM plays crucial role. Mitigating Risks with BIG-IP APM BIG-IP APM per-request granularity. With per-request granularity, organizations can dynamically enforce access policies based on various factors such as user identity, device characteristics, and contextual information. This enables organizations to implement fine-grained access controls at the API level, mitigating the risks associated with Broken Object Property Level Authorization. Key Features: Dynamic Access Control Policies: BIG-IP APM empowers organizations to define dynamic access control policies that adapt to changing conditions in real-time. By evaluating each API request against these policies, BIG-IP APM ensures that only authorized users can access specific resources and perform permitted actions. Granular Authorization Rules: BIG-IP APM enables organizations to define granular authorization rules that govern access to individual objects or resources within the API ecosystem. By enforcing strict authorization checks at the object level, F5 APM prevents unauthorized users from tampering with sensitive data or performing unauthorized actions. Conclusion In conclusion, BIG-IP APM per-request granularity is a powerful tool for defending against Broken Object-Level Authorization vulnerabilities in APIs. By enforcing fine-grained access controls at the API level, organizations can mitigate the risks associated with unauthorized access to sensitive data. Additionally, proactive security assessments and vulnerability scans are essential for identifying and addressing vulnerabilities in APIs, thereby strengthening overall security posture in the digital ecosystem. Related Content F5 BIG-IP Access Policy Manager | F5 Introduction to OWASP API Security Top 10 2023 OWASP Top 10 API Security Risks – 2023 - OWASP API Security Top 10 API Protection Concepts OWASP Tactical Access Defense Series: How BIG-IP APM Strengthens Defenses Against OWASP Top 10 OWASP Tactical Access Defense Series: Broken Object Level Authorization and BIG-IP APM F5 Hybrid Security Architectures (Part 5 - F5 XC, BIG-IP APM, CIS, and NGINX Ingress Controller) OWASP Tactical Access Defense Series: Broken Authentication and BIG-IP APMMitigation of OWASP API Security Risk: Unrestricted Access to Sensitive Business Flows using F5 XC
We have already covered different OWASP API risks in our previous articles (check reference section for more details). OWASP continuously analysed API threats in the last few years and has identified new types of risks which are not part of API Security Top 10 - 2019 edition. So, they added these new ones in the 2nd edition of OWASP API Security Top 10 2023 list and this article will cover the nuances of the newly added risk: Unrestricted Access to Sensitive Business Flows. Introduction: API owners should be very cautious of all the API endpoint’s exposed to users and they should identify each endpoint’s business justification. When developing an API Endpoint, we shall understand API use case and its intended scope of user action. Some business flows need to be monitored, restricted or blocked depending on the sensitivity of endpoint data. If any sensitive business flow is not protected, attackers can exploit them and cause some serious damage to the business. Using wide variety of automated tools available in market, hackers can automate the manual processthereby adversely impacting the genuine business workflows. That’s all the theory I have !!. Let’s plunge into a demo application use case and discover how F5 Distributed Cloud Platform (XC) can detect and guard our API application endpoints against this vulnerability. Use case: As part of testing, I was exploring the options available in one of the demo application “F5AIR” which is used for booking some dummy flight tickets and as a promotion this application is also offering 200$ as account balance after every user signup. In the 3rd tab we observed that this balance can be used to create gift cards which can be redeemed by users. After doing thorough research we have identified there are no restrictions on this workflow and it can be exploited using automated tools. Automated tools can be used to create multiple users, generate gift cards from each user and then redeem them into a single valid account to further book flight tickets without paying anything. Because of this risk, businesses can incur losses and so this is marked as a sensitive business flow. Artificial Intelligence is a truly disruptive technology spreading like wildfire and so for the purpose of today’s demo, I am using AXIOM.AI browser extension to automate the above manual workflow steps. It just took me around 30 minutes to understand how it works and was able to automate the above exploited manual steps. After 10 user creations and redeeming their gift cards valid main user will have around 2000$ which can be used to book flight tickets. Note: To showcase how AI tools can be leveraged to exploit modern applications we are using axiom ai tool and intended only for educational purposes. Mitigation Steps: A straightforward one-point solution may not be appropriate for different types of these vulnerabilities. Secops team should dig deeper into their incoming application traffic, differentiate genuine & malicious security data and then identify the API endpoints which are sensitive to their business flows. Once they have analyzed the traffic then they can apply below solutions as per their requirements Configure API Discovery to detect different API vulnerabilities like sensitive Data, API Attributes like Login page, Zombie API, security Posture, etc. You can find more details in this article Configure rate limiting on the sensitive business end points to keep a limit on number of requests - check here for more details on rate limiting Configure API Protection rules for these business API’s to restrict access to applications – check here for more details on API rules Configure Bot Defense to prevent bot attacks – check here for more details on bot protection As an example, let’s consider the above demonstrated AI tool example, to block any botsfrom accessing demo application we can apply bot defense configurations in root folder location “/” as shown below after which bot AI exploit requests can be mitigated. Note: Above config is for this article’s use case, but users must understand the API endpoint’s which should be protected and apply configs appropriately. We can also try other automation tools like postman which may also be blocked as below In F5 XC console if we navigate to this load balancer security events and bot defense dashboards, we can see these requests are blocked. Conclusion: In this article we explored some insights on this newly added OWASP API Security Top 10 risk, then we shed some light on how AI tools have opened floodgates to a new approach of application threats. Finally, we also revealed the final puzzle of how F5 XC Bot defense can become our elixir in identifying and protecting against this OWASP API risk along with novel AI threats. For more information or to get started check links below: OWASP API Security Top 10 2023 OWASP API Security Top 10 - 2019 F5 Distributed Cloud WAAP
OWASP Tactical Access Defense Series: Broken Authentication and BIG-IP APM
The threat of broken authentication poses a significant risk to organizations, potentially leading to unauthorized access and data breaches. In the face of this formidable challenge, F5's Access Policy Manager (APM) emerges as a robust and indispensable solution. By seamlessly integrating advanced authentication mechanisms and comprehensive access controls, F5 BIG-IP APM stands as a stalwart guardian against the vulnerabilities associated with broken authentication. This article explores the pivotal role played by BIG-IP APM in fortifying authentication protocols, mitigating risks, and ensuring a resilient defense against unauthorized access, ultimately safeguarding the integrity and security of sensitive data in today's dynamic digital environment. Broken Authentication Broken Authentication Examples BIG-IP APM and Broken Authentication Related Content Broken Authentication Authentication mechanism is an exposed target due to the nature of this function, as authentication is the first point of entry to any platform. The difficulty to exploit authentication weaknesses differs based on how the authentication platform is secured. In the current digital era the security perimeters are very fluid, and so are the trust boundries for our authentication platforms those require more cautions from the developers and security architects regarding authentication flows. Not only we need to protect authentication endpoints and flows, but also some overlooked items like forget and reset password endpoints. How can we consider endpoint to be vulnerable? Credential stuffing. Brute force attacks targetting users' accounts. Weak Passwords. Sensitive details in the URL (passwords, Tokens). Allow users sensitive actions without confirmation. No validation for the tokens authenticity. Accept unsigned or weak jwt tokens. No validation for jwt expiration. Use of plain-text, non-encrypted or non-hashed passwords. Use of weak encryption algorithms. Endpoint can access each other without proper authentication. Use weak or predictable tokens for intra-endpoint authentication. Broken Authentication Examples Making use of GraphQL query patching to bypass API ratelimiting and brute force user's login. POST /graphql [ {"query":"mutation{login(username:\"victim\",password:\"password\"){token}}"}, {"query":"mutation{login(username:\"victim\",password:\"123456\"){token}}"}, {"query":"mutation{login(username:\"victim\",password:\"qwerty\"){token}}"}, ... {"query":"mutation{login(username:\"victim\",password:\"123\"){token}}"}, ] Update / modify user's sensitive information without API authorization token. PUT /account Authorization: Bearer <token> { "newpassword": "<new_password>" } BIG-IP APM and Broken Authentication We start with creating our Per-Request policy, this policy works in a different way than the per-session policy, as the flow will be evaluted on a per-request basis, making sure to consider variations throught the session life-time. Below are some of the key benefits: Wide range of Authentication, SSO, and MFA mechanisms to properly identify the initiating machine or user. Ability to integrate with 3rd parties to provide additional enforcement decisions based on the organization's policy. Ability to apply endpoint checks on the client side before session initiation. This goes to BIG-IP in general, the ability to apply custom traffic control on both of the traffic sides, Client and Server. The ability to create whitelist / blacklist for API Access tokens, JSON Web Tokens ID (JTI) or a different element based on the used authentication method, below example steps for JWT: Extract JTI value from Access token. Add JTI value to whether Allow/Block lists. Related Content F5 BIG-IP Access Policy Manager | F5 Introduction to OWASP API Security Top 10 2023 OWASP Top 10 API Security Risks – 2023 - OWASP API Security Top 10 API Protection Concepts OWASP Tactical Access Defense Series: How BIG-IP APM Strengthens Defenses Against OWASP Top 10 OWASP Tactical Access Defense Series: Broken Object Level Authorization and BIG-IP APM F5 Hybrid Security Architectures (Part 5 - F5 XC, BIG-IP APM, CIS, and NGINX Ingress Controller)F5 Distributed Cloud (XC) API Security in Out-of-Band Mode using BIG-IP
Introduction There are API Security use cases, which need to be deployed in out-of-band mode. An example of such use cases is an existing very high-critical API deployment, which cannot risk a new inline point. Another example is API security visibility deployment with so many deployment points, which renders new inline/proxy points becoming too expensive. F5 Distributed Cloud (XC) API Security solution by default, requires an inline deployment to be the most effective solution. It needs a different approach if out-of-band deployment is required. Out-of-band mode in this context, means that F5 XC is deployed outside of the API traffic path flow. This implies that F5 XC is in monitoring mode by receiving the API traffic from mirroring devices or API logging systems. While in monitoring mode, it cannot provide any protection or enforcement without any integration with existing security solutions; inline or F5 XC deployment is changed to be inline mode. The best deployment position for F5 XC API Security remains the inline mode. Architecture F5 XC is designed to be an inline solution. It receives the API request traffic, analyzes it, and forwards it to the backend/origin servers. Receives the API response traffic, analyzes it, and forwards it to the clients. F5 XC is not designed to handle API logs or mirrored traffic. Hence, it requires a separate component to receive the API logs or mirrored traffic and translate them into API request and response traffic, and send them to F5 XC. The architecture below uses BIG-IP as the separate component carrying out the needed functions. The above architecture takes an example of Apigee API Gateway platform, which sends an API trace in XML format containing the request and response traffic of an API call passing through it to a BIG-IP VE (Virtual Edition) deployed in AWS. The BIG-IP VE is licensed with LTM and configured with 3 virtual servers and 3 iRules to carry out the required functions. Step (1): BIG-IP VE receives the uploaded API trace in XML format via vs_log_receiver VS attached with an iRule 01_incoming_xml_to_log_converter. The iRule parses the trace, captures the API request and response traffic, create+save the simulated API response in the cache, create a unique link ID, create a simulated API request, and send the simulated API request to vs_http_to_https using Sideband iRule. Step (2): The vs_http_to_https VS receives the simulated API request traffic and forwards it as an HTTPS request to F5 Distributed Cloud (XC). F5 XC receives the simulated API request and forwards it back to the BIG-IP VE. Step (3): The BIG-IP VE receives the simulated API request from F5 XC, finds the corresponding API response from the cache using the unique link ID inside the request header, and sends the simulated API response to F5 XC. F5 XC then forwards the simulated API response to the BIG-IP VE completing the traffic flow cycle. With those steps, they mimic the situation where F5 XC receives normal API request and response traffic. The difference here is that the client and the server are the simulated one, not the real client and server. F5 XC processes the API traffic according to the API security configuration in a more passive role instead of an active role. The iRules are available at this link. Benefits The benefits of this architecture are: Deploy F5 XC API Security in out-of-band mode without disturbing/changing the existing API traffic flow, which makes the deployment faster and less intrusive. Get the API Discovery from F5 XC to discover API traffic endpoints and provide the analysis. Verify the API Authentication in each API request traffic. Conduct API Security posture management. Display the API security visibility in centralized manner without changing the API environment. Create the right justification cases for the higher-management approval to deploy the F5 XC API Security solution in inline mode. Getfull support for the solution from F5 ecosystem because the solution uses standard F5 solution components. Scaling Ifsignificant capacity is required to process the mirrored API traffic, it is possible to deploy multiple BIG-IP VE instances and load-balance them using AWS Network Load Balancer. A specific iRule to synchronize the response cache entries between BIG-IP VE instances needs to be developed. I leave this scope for further details on implementation. Let me know your thoughts by leaving a comment or two below 🙂Enhance your Application Security using Client-side signals
In this two-part post, I’m describing the concept of Client-side signals and how they can be used to enhance your web and mobile application security strategy. As is well known, API and application security are currently at the forefront of today's CISO agenda. These are real concerns, especially due to the accelerated digital transformation that has forced all companies to adapt and quickly build new applications to stay competitive and keep their relevance through the stormy times we have lived these past 3 years. According to the World Bank, over 40% of adults who made merchant in-store and online payments using a card, phone, or the internet did so for the first time since the start of the pandemic. This is an attestation of how much more people are consuming online applications. Delving further into the “adapt and quickly build” of new applications, what transpired was the creation of numerous new APIs, Web and Mobile applications, and an entire ecosystem facilitating deep interdependency between them. When you visit any online application today, be it Web or Mobile, it is highly likely that this application is constructed using APIs and is leveraging a broad array of 3rd-party integrations in the form of APIs or other web components. Below, you will find a simple example of a Web page calling external entities as it loads in a browser: Figure 1. Waterfall The figure above illustrates what we could characterize as an ecosystem comprising internal and external web components. These components provide the necessary services to enable the application to fulfill its intended purpose. A browser loading components of web applications isn’t something new; in fact, browsers are created to perform exactly this function; what is new is the fact that this application requires a whole set of external components that are fetched directly or indirectly by the browser, and, despite these external dependencies being transparent for the end-user, they represent a real challenge from the application security strategy point of view. At F5, we embody what I personally refer to as the “web proxy mindset”. For the past 27 years, a substantial part of our work, of course, in an oversimplified way, has to do with proxying network communications between a client and a server; thereby, proxying these communications gives us the ability to guarantee that all requirements for an application or API are met, whether related to security or connectivity. Figure 2. Typical client-server communication If you search online for the definition of a proxy server, you will find that it is defined as “a server application that acts as an intermediary between a client requesting a resource and the server providing the resource, and it improves privacy, security, and performance in the process.” The paradigmshift for protecting modern applications To fight the new generation of attackers and fraudsters, the ones that usually sell the attack as-a-Service on the dark forums, F5 has enhanced its solutions by integrating our web proxy approach with the client-side signals. We will get into deeper details later in this post, but it could be seen as having a magnified glass to closely monitor the activities within the browser as a person interacts with the application prior to having the actual request sent to the application server. Combining both techniques is a strong strategy to enhance your application security. When discussing the concept of client-side signals with CISOs and their teams, I often find that this methodology is not widely known and is largely underutilized by companies seeking to bolster their security detection capabilities. What are the Client-side signals? Client-side signals are the telemetry that can be pulled from the browser or mobile app while a person or an entity interacts with the application. These signals can typically be categorized into three main groups: a) human interaction signals, b) device environment signals, and c) network signals. Human Interaction Signals This category of signals will allow us to determine if what is using an application is a person or not and if this person is bad or good intended. But how and why is this relevant to your security strategy? Is this a person or not? When this question is raised, how confidently can you answer that? It is well-known in the application security industry that bots are a real problem. These software entities are created for several different purposes; one of them is to “mimic” a person's behavior and, by doing that, imitate the steps and behaviors a human would do while interacting with a page or app. Distinguishing if an entity visiting your application is a Human or a Bot before having your application servers process an HTTP request will automatically improve the overall user experience, as you will offload the burden of processing noise from your servers. It is important to emphasize that Bots are not typically created to overload your servers; they’re created to “mimic” humans with the intention of abusing a legit business logic your application provides and do not get caught as they do that. The examples below were extracted from a real Login application and can give a visual perspective of how Bots can “mimic” humans: Figure 3. - Bot Figure 4. - Human Is this person good or bad intended? I must admit that this is a very tricky question to answer; however, answering it at an initial stage of interaction can significantly enhance your security strategy. Determining intention is a typical methodology used by fraud prevention solutions, but having a glimpse of how a person behaves while interacting with the application and using it to either permit or deny a request is also applicable for the application security teams as it can help detect the initial stages of an attack, which usually involves reconnaissance techniques. What distinguishes a human performing an attack reconnaissance technique from a regular human using your application? There are several factors at play here, and employing AI strategies can probabilistically determine the typical behavior of well-intended users on your application by analyzing things like: How common is having users often leave the active browser tab while interacting with the application? How typical is having users press unusual keys while filling out forms? What is the average time a user spends on each step of your application? How frequently does a user change or use different devices to access the application? Is the mouse moving like an untrained human? Is this person actively submitting any data while using the application or sitting there and clicking randomly, or, maybe, apparently doing nothing? …and several others. Device Environment Signals This category of signals is based on fingerprinting techniques. Device fingerprinting isn’t something new, and there are known ways to get around fingerprinting. However, the goal here isn’t only to fingerprint a device but also to check if the collected signals are contradicting somehow, thus giving the signs that something has been spoofed to avoid real identification. Remember, we are looking for lies! Application security teams should be looking for signals like: Screen Size Fonts Plugins/Extensions CPU/GPU capabilities Graphic rendering capabilities Canvas configurations Browser configurations Time zones …and hundreds of others. Figure 5. Emoji rendering Examples of questions to help identify spoofed devices using these signals could be: Why is this browser saying it is a certain version of Firefox for MacOS, but some properties are only found on Windows OS? Why is this browser rendering a certain emoji as if it were for a different browser or OS? Why is this mobile app showing properties as if it is an emulator? Why is this session coming from a remote desktop? Why is this a Virtual Machine? …and several other questions that can lead to identifying a spoofed device and eventually trigger a security policy. Network Signals This category of signals will provide insights into where this request is coming from. Typically, security solutions rely on IP addresses to determine if a request is permitted or denied, but today, relying only on IP addresses is a very ineffective way of determining the real source of a request. Numerous companies provide VPN and forward proxy services, commonly found in the attacker's toolkit, but the real danger lives on the Botnets. Today’s Botnets are comprised of regular end-users who willingly participate in the network and compromised devices acting as internet proxies without the owner's knowledge. In such cases, relying on a Geo-blocking or IP reputation database may not flag these requests as malicious since the source IP address is often associated with a regular ADSL or LTE/5G mobile network and is not necessarily engaged in malicious activities. Additional effort is required to enhance your ability to identify the true source of a request. Collecting network-based signals, including IP addresses, HTTP headers, TLS fingerprints, and a portion of the request's payload, and combining these signals with device environment and human interaction signals can provide robust insights into the malicious or non-malicious nature of a request. It is important to note that these alone are not a solution to detect a malicious source. More advanced techniques like TCP handshake timing, TCP-related variances and others can also come into play and enhance the overall detection. Also, every browser has its own way of crafting an HTTP request, so the goal here is to detect inconsistencies in the HTTP requests and enhance anomaly detection capabilities. Now that you know some capabilities of the Client-side signals, the next questions might be: How to collect Client-side Signals? How can you guarantee the signals are not altered or faked while they are collected? Making decisions based on bad data leads to false positives and false negatives. How can you confidently say that after collecting the signals, they are sent to your backend in a protected way and are not altered while in transit? What happens when no signals are collected? What practical improvements will you get when you start using the client-side signals? What if you have a compromised 3rd-partycomponent? How can you detect a potential supply-chain issue? These are all interesting questions, and the answers are coming in the second part of this article. Please stay tuned, and thank you for your interest and time to read this article. References: World Bank https://www.worldbank.org/en/news/press-release/2022/06/29/covid-19-drives-global-surge-in-use-of-digital-payments Proxy Server https://en.wikipedia.org/wiki/Proxy_server
The PingIntelligence and F5 BIG-IP Solution for Securing APIs
This article describes the PingIntelligence and F5 BIG-IP solution deployment for securing APIs. The integration identifies and automatically blocks cyber attacks on APIs, exposes active APIs, and provides detailed reporting on all API activity. Solution Overview PingIntelligence is deployed in a side-band configuration with F5 BIG-IP. A PingIntelligence policy is installed in F5 BIG-IP and passes API metadata to PingIntelligence for detailed API activity reporting and attack detection with optional client blocking. PingIntelligence software includes support for reporting and attack detection based on usernames captured from JSON Web Token (JWT). Following is a description of the traffic flow through F5 BIG-IP and PingIntelligence API Security Enforcer (ASE): The client sends an incoming request to F5 BIG-IP F5 BIG-IP makes an API call to send the request metadata to ASE ASE checks the request against a registered set of APIs and looks for the origin IP, cookie, OAuth2 token, or API key in PingIntelligence AI engine generated Blacklist. If all checks pass, ASE returns a 200-OK response to the F5 BIG-IP. If not, a different response code is sent to F5 BIG-IP. The request information is also logged by ASE and sent to the AI Engine for processing. F5 BIG-IP receives a 200-OK response from ASE, then it forwards the request to the backend server pool. A request is blocked only when ASE sends a 403 error code. The response from the back-end server poll is received by F5 BIG-IP. F5 BIG-IP makes a second API call to pass the response information to ASE which sends the information to the AI engine for processing. ASE receives the response information and sends a 200-OK to F5 BIG-IP. F5 BIG-IP sends the response received from the backend server to the client. Pre-requisites BIG-IP system must be running TMOS v13.1.0.8 or higher version. Sideband authentication is enabled on PingIntelligence for secure communication with the BIG-IP system. Download the PingIntelligence policy from the download site. Solution Deployment Step-1: Import and Configure PingIntelligence Policy Login to your F5 BIG-IP web UI and navigate to Local Traffic > iRules > LX Workspaces. On the LX Workspaces page, click on the Import button. Enter a Name and choose the PingIntelligence policy that you downloaded from the Ping Identity download site. Then, click on the Import button. This creates LX workspace Open the Workspace by clicking on the name. The policy is pre-loaded with an extension named oi_ext . Edit the ASE configuration by clicking on the ASEConfig.js file. It opens the PingIntelligence policy in the editor: Click on this link to understand various ASE variables. Step-2: Create LX Plugin Navigate to Local Traffic > iRules > LX Plugins. On the New Plugin page, click on the Create button to create a new plugin with the name pi_plugin. Select the workspace that you created earlier from the Workspace drop-down list and click on the Finished button. Step-3: Create a Backend Server Pool and Frontend Virtual Server (Optional) If you already created the virtual server, skip this step Create a Backend Server pool Navigate to Local Traffic > Pools > Pool List and click on the Create button. In the configuration page, configure the fields and add a new node to the pool. When done, click on the Finished button. This creates a backend server pool that is accessed from clients connecting to the frontend virtual server Create a Frontend Virtual Server Navigate to Local Traffic > Virtual Server > Virtual Server List and click on the Create button. Configure the virtual server details. At a minimum, configure the Destination Address, Client SSL Profile and Server SSL Profile When done, click on the Finished button. Under the Resource tab, add the backend server pool to the virtual server and click on the Update button. Step-4: Add PingIntelligence Policy The imported PingIntelligence policy must be tied to a virtual server. Add the PingIntelligence policy to the virtual server. Navigate to Local Traffic > Virtual Servers > Virtual Server List. Select the virtual server to which you want to add the PingIntelligence policy. Click on the Resources tab. In the iRules section, click on the Manage button. Choose the iRule under the pi_plugin that you want to attach to the virtual server. Move the pi_irule to the Enabled window and click on the Finished button. Once the solution is deployed, you can gain insights into user activity, attack information, blocked connections, forensic data, and much more from the PingIntelligence dashboard References Ping Intelligence for API Overview F5 BIG-IP PingIntelligence Integration
OpenAPI not valid?
Hi. New here. We use 15.1.4 with ASM licensed. According to documentation, OpenAPI 3.0 specifications should be available since 14.1.3. We get "openapi.json is not a valid OpenAPI spec file" when trying the "Guided Configuration" on the ASM module with a spec on 3.0.2. Any idea what we are missing? BR, Raphael
