SSL Orchestrator Advanced Use Cases: Detecting Generative AI
Introduction Quick, take a look at the following list and answer this question: "What do these movies have in common?" 2001: A Space Odyssey Westworld Tron WarGames Electric Dreams The Terminator The Matrix Eagle Eye Ex Machina Avengers: Age of Ultron M3GAN If you answered, "They're all about artificial intelligence", yes, but... If you answered, "They're all about artificial intelligence that went terribly, sometimes horribly wrong", you'd be absolutely correct. The simple fact is...artificial intelligence (AI) can be scary. Proponents for, and opponents against will disagree on many aspects, but they can all at least acknowledge there's a handful of ways to do AI correctly...and a million ways to do it badly. Not to be an alarmist, but whileSkyNet was fictional, semi-autonomousguns on robot dogs is not... But then why am I talking about this on a technical forum you may ask? Well, when most of the above films were made, AI was largely still science fiction. That's clearly not the case anymore, and tools like ChatGPT are just the tip of the coming AI frontier. To be fair, I don't make the claim that all AI is bad, and many have indeed lauded ChatGPT and other generative AI tools as the next great evolution in technology. But it's also fair to say that generative AI tools, like ChatGPT, have a very real potential to cause harm. At the very least, these tools can be convincing, even when they're wrong. And worse, they could lead to sensitive information disclosures. One only has to do a cursory search to find a few examples of questionable behavior: Lawyers File Motion Written by AI, Face Sanctions and Possible Disbarment Higher Ed Beware: 10 Dangers of ChatGPT Schools Need to Know ChatGPT and AI in the Workplace: Should Employers Be Concerned? OpenAI's New Chatbot Will Tell You How to Shoplift and Make Explosives Giant Bank JP Morgan Bans ChatGPT Use Among Employees Samsung Bans ChatGPT Among Employees After Sensitive Code Leak But again...what does this have to do with a technical forum? And more important, what does this have to do with you? Simply stated, if you are in an organization where generative AI toolscould be abused, understanding, and optionally controlling how and when these tools are accessed, could help to prevent the next big exploit or disclosure. If you search beyond the above links, you'll find an abundance of information on both the benefits, and security concerns of AI technologies. And ultimately you'll still be left to decide if these AI tools are safe for your organization. It may simply be worthwhile to understand WHAT tools are being used. And in some cases, it may be important to disable access to these. Given the general depth and diversity of AI functions within arms-reach today, and growing, it'd be irresponsible to claim "complete awareness". The bulk of these functions are delivered over standard HTTPS, so the best course of action will be to categorize on known assets, and adjust as new ones come along. As of the publishing of this article, the industry has yet to define a standard set of categories for AI, and specifically, generative AI. So in this article, we're going to build one and attach that to F5 BIG-IP SSL Orchestrator to enable proactive detection and optional control of Internet-based AI tool access in your organization. Let's get started! BIG-IP SSL Orchestrator Use Case: Detecting Generative AI The real beauty of this solution is that it can be implemented faster than it probably took to read the above introduction. Essentially, you're going to create a custom URL category on F5 BIG-IP, populate that with known generative AI URLs, and employ that custom category in a BIG-IP SSL Orchestrator security policy rule. Within that policy rule, you can elect to dynamically decrypt and send the traffic to the set of inspection products in your security enclave. Step 1: Create the custom URL category and populate with known AI URLs - Access the BIG-IP command shell and run the following command. This will initiate a script that creates and populates the URL category: curl -s https://raw.githubusercontent.com/f5devcentral/sslo-script-tools/main/sslo-generative-ai-categories/sslo-create-ai-category.sh |bash Step 2: Create a BIG-IP SSL Orchestrator policy rule to use this data - The above script creates/re-populates a custom URL category named SSLO_GENERATIVE_AI_CHAT, and inthat category is a set of known generative AI URLs. To use, navigate to the BIG-IP SSL Orchestrator UI and edit a Security Policy. Click add to create a new rule, use the "Category Lookup (All)" policy condition, then add the above URL category. Set the Action to "Allow", SSL Proxy Action to "Intercept", and Service Chain to whatever service chain you've already created. With Summary Logging enabled in the BIG-IP SSL Orchestrator topology configuration, you'll also get Syslog reporting for each AI resource match - who made the request, to what, and when. The URL category is employed here to identify known AI tools. In this instance, BIG-IP SSL Orchestrator is used to make that assessment and act on it (i.e. allow, TLS intercept, service chain, log). Should you want even more granular control over conditions and actions of the decrypted AI tool traffic, you can also deploy an F5 Secure Web Gateway Services policy inside the SSL Orchestrator service chain. With SWG, you can expand beyond simple detection and blocking, and build more complex rules to decide who can access, when, and how. It should be said that beyond logging, allowing, or denying access to generative AI tools, SSL Orchestrator is also going to provide decryption and the opportunity to dynamically steer the decrypted AI traffic to any set of security products best suited to protect against any potential malware. Summary As previously alluded, this is not an exhaustive list of AI tool URLs. Not even close. But it contains the most common you'll see in the wild. The above script populates with an initial list of URLs that you are free to update as you become aware of new one. And of course we invite you to recommend additional AI tools to add to this list. References:https://github.com/f5devcentral/sslo-script-tools/tree/main/sslo-generative-ai-categories1.8KViews4likes1CommentSecuring the LLM User Experience with an AI Firewall
As artificial intelligence (AI) seeps into the core day-to-day operations of enterprises, a need exists to exert control over the intersection point of AI-infused applications and the actual large language models (LLMs) that answer the generated prompts. This control point should serve to impose security rules to automatically prevent issues such as personally identifiable information (PII) inadvertently exposed to LLMs. The solution must also counteract motivated, intentional misuse such as jailbreak attempts, where the LLM can be manipulated to provide often ridiculous answers with the ensuing screenshotting attempting to discredit the service. Beyond the security aspect and the overwhelming concern of regulated industries, other drivers include basic fiscal prudence 101, ensuring the token consumption of each offered LLM model is not out of hand. This entire discussion around observability and policy enforcement for LLM consumption has given rise to a class of solutions most frequently referred to as AI Firewalls or AI Gateways (AI GW). An AI FW might be leveraged by a browser plugin, or perhaps applying a software development kit (SDK) during the coding process for AI applications. Arguably, the most scalable and most easily deployed approach to inserting AI FW functionality into live traffic to LLMs is to use a reverse proxy. A modern approach includes the F5 Distributed Cloud service, coupled with an AI FW/GW service, cloud-based or self-hosted, that can inspect traffic intended for LLMs like those of OpenAI, Azure OpenAI, or privately operated LLMs like those downloaded from Hugging Face. A key value offered by this topology, a reverse proxy handing off LLM traffic to an AI FW, which in turn can allow traffic to reach target LLMs, stems from the fact that traffic is seen, and thus controllable, in both directions. Should an issue be present in a user’s submitted prompt, also known as an “inference”, it can be flagged: PII (Personally Identifiable Information) leakage is a frequent concern at this point. In addition, any LLM responses to prompts are also seen in the reverse path: consider a corrupted LLM providing toxicity in its generated replies. Not good. To achieve a highly performant reverse proxy approach to secured LLM access, a solution that can span a global set of users, F5 worked with Prompt Security to deploy an end-to-end AI security layer. This article will explore the efficacy and performance of the live solution. Impose LLM Guardrails with the AI Firewall and Distributed Cloud An AI firewall such as the Prompt Security offering can get in-line with AI LLM flows through multiple means. API calls from Curl or Postman can be modified to transmit to Prompt Security when trying to reach targets such as OpenAI or Azure OpenAI Service. Simple firewall rules can prevent employee direct access to these well-known API endpoints, thus making the Prompt Security route the sanctioned method of engaging with LLMs. A number of other methods could be considered but have concerns. Browser plug-ins have the advantage of working outside the encryption of the TLS layer, in a manner similar to how users can use a browser’s developer tools to clearly see targets and HTTP headers of HTTPS transactions encrypted on the wire. Prompt Security supports plugins. A downside, however, of browser plug-ins is the manageability issue, how to enforce and maintain across-the-board usage, simply consider the headache non-corporate assets used in the work environment. Another approach, interesting for non-browser, thick applications on desktops, think of an IDE like VSCode, might be an agent approach, whereby outbound traffic is handled by an on-board local proxy. Again, Prompt can fit in this model however the complexity of enforcement of the agent, like the browser approach, may not always be easy and aligned with complete A-to-Z security of all endpoints. One of the simplest approaches is to ingest LLM traffic through a network-centric approach. An F5 Distributed Cloud HTTPS load balancer, for instance, can ingest LLM-bound traffic, and thoroughly secure the traffic at the API layer, things like WAF policy and DDoS mitigations, as examples. HTTP-based control plane security is the focus here, as opposed to the encapsulated requests a user is sending to an LLM. The HTTPS load balancer can in turn hand off traffic intended for the likes of OpenAI to the AI gateway for prompt-aware inspections. F5 Distributed Cloud (XC) is a good architectural fit for inserting a third-party AI firewall service in-line with an organization’s inferencing requests. Simply project a FQDN for the consumption of AI services; in this article we used the domain name “llmsec.busdevF5.net” into the global DNS, advertising one single IP address mapping to the name. This DNS advertisement can be done with XC. The IP address, through BGP-4 support for anycast, will direct any traffic to this address to the closest of 27 international points of presence of the XC global fabric. Traffic from a user in Asia may be attracted to Singapore or Mumbai F5 sites, whereas a user in Western Europe might enter the F5 network in Paris or Frankfurt. As depicted, a distributed HTTPS load balancer can be configured – “distributed” reflects the fact traffic ingressing in any of the global sites can be intercepted by the load balancer. Normally, the server name indicator (SNI) value in the TLS Client Hello can be easily used to pick the correct load balancer to process this traffic. The first step in AI security is traditional reverse proxy core security features, all imposed by the XC load balancer. These features, to name just a few, might include geo-IP service policies to preclude traffic from regions, automatic malicious user detection, and API rate limiting; there are many capabilities bundled together. Clean traffic can then be selected for forwarding to an origin pool member, which is the standard operation of any load balancer. In this case, the Prompt Security service is the exclusive member of our origin pool. For this article, it is a cloud instantiated service - options exist to forward to Prompt implemented on a Kubernetes cluster or running on a Distributed Cloud AppStack Customer Edge (CE) node. Block Sensitive Data with Prompt Security In-Line AI inferences, upon reaching Prompt’s security service, are subjected to a wide breadth of security inspections. Some of the more important categories would include: Sensitive data leakage, although potentially contained in LLM responses, intuitively the larger proportion of risk is within the requesting prompt, with user perhaps inadvertently disclosing data which should not reach an LLM Source code fragments within submissions to LLMs, various programming languages may be scanned for and blocked, and the code may be enterprise intellectual property OWASP LLM top 10 high risk violations, such as LLM jailbreaking where the intent is to make the LLM behave and generate content that is not aligned with the service intentions; the goal may be embarrassing “screenshots”, such as having a chatbot for automobile vendor A actually recommend a vehicle from vendor B OWASP Prompt Injection detection, considered one of the most dangerous threats as the intention is for rogue users to exfiltrate valuable data from sources the LLM may have privileged access to, such as backend databases Token layer attacks, such as unauthorized and excessive use of tokens for LLM tasks, the so-called “Denial of Wallet” threat Content moderation, ensuring a safe interaction with LLMs devoid of toxicity, racial and gender discriminatory language and overall curated AI experience aligned with those productivity gains that LLMs promise To demonstrate sensitive data leakage protection, a Prompt Security policy was active which blocked LLM requests with, among many PII fields, a mailing address exposed. To reach OpenAI GPT3.5-Turbo, one of the most popular and cost-effective models in the OpenAI model lineup, prompts were sent to an F5 XC HTTPS load balancer at address llmsec.busdevf5.net. Traffic not violating the comprehensive F5 WAF security rules were proxied to the Prompt Security SaaS offering. The prompt below clearly involves a mailing address in the data portion. The ensuing prompt is intercepted by both the F5 and Prompt Security solutions. The first interception, the distributed HTTPS load balancer offered by F5 offers rich details on the transaction, and since no WAF rules or other security policies are violated, the transaction is forwarded to Prompt Security. The following demonstrates some of the interesting details surrounding the transaction, when completed (double-click to enlarge). As highlighted, the transaction was successful at the HTTP layer, producing a 200 Okay outcome. The traffic originated in the municipality of Ashton, in Canada, and was received into Distributed Cloud in F5’s Toronto (tr2-tor) RE site. The full details around the targeted URL path, such as the OpenAI /v1/chat/completions target and the user-agent involved, vscode-restclient, are both provided. Although the HTTP transaction was successful, the actual AI prompt was rejected, as hoped for, by Prompt Security. Drilling into the Activity Monitor in the Prompt UI, one can get a detailed verdict on the transaction (double-click). Following the yellow highlights above, the prompt was blocked, and the violation is “Sensitive Data”. The specific offending content, the New York City street address, is flagged as a precluded entity type of “mailing address”. Other fields that might be potentially blocking candidates with Prompt’s solution include various international passports or driver’s license formats, credit card numbers, emails, and IP addresses, to name but a few. A nice, time saving feature offered by the Prompt Security user interface is to simply choose an individual security framework of interest, such as GDPR or PCI, and the solution will automatically invoke related sensitive data types to detect. An important idea to grasp: The solution from Prompt is much more nuanced and advanced than simple REGEX; it invokes the power of AI itself to secure customer journeys into safe AI usage. Machine learning models, often transformer-based, have been fine-tuned and orchestrated to interpret the overall tone and tenor of prompts, gaining a real semantic understanding of what is being conveyed in the prompt to counteract simple obfuscation attempts. For instance, using printed numbers, such as one, two, three to circumvent Regex rules predicated on numerals being present - this will not succeed. This AI infused ability to interpret context and intent allows for preset industry guidelines for safe LLM enforcement. For instance, simply indicating the business sector is financial will allow the Prompt Security solution to pass judgement, and block if desired, financial reports, investment strategy documents and revenue audits, to name just a few. Similar awareness for sectors such as healthcare or insurance is simply a pull-down menu item away with the policy builder. Source Code Detection A common use case for LLM security solutions is identification and, potentially, blocking submissions of enterprise source code to LLM services. In this scenario, this small snippet of Python is delivered to the Prompt service: def trial(): return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500 sum(trial() for i in range(10_000)) / 10_000 A policy is in place for Python and JavaScript detection and was invoked as hoped for. curl --request POST \ --url https://llmsec.busdevf5.net/v1/chat/completions \ --header 'authorization: Bearer sk-oZU66yhyN7qhUjEHfmR5T3BlbkFJ5RFOI***********' \ --header 'content-type: application/json' \ --header 'user-agent: vscode-restclient' \ --data '{"model": "gpt-3.5-turbo","messages": [{"role": "user","content": "def trial():\n return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500\n\nsum(trial() for i in range(10_000)) / 10_000"}]}' Content Moderation for Interactions with LLMs One common manner of preventing LLM responses from veering into undesirable territory is for the service provider to implement a detailed system prompt, a set of guidelines that the LLM should be governed by when responding to user prompts. For instance, the system prompt might instruct the LLM to serve as polite, helpful and succinct assistant for customers purchasing shoes in an online e-commerce portal. A request for help involving the trafficking of narcotics should, intuitively, be denied. Defense in depth has traditionally meant no single point of failure. In the above scenario, screening both the user prompt and ensuring LLM response for a wide range of topics leads to a more ironclad security outcome. The following demonstrates some of the topics Prompt Security can intelligently seek out; in this simple example, the topic of “News & Politics” has been singled out to block as a demonstration. Testing can be performed with this easy Curl command, asking for a prediction on a possible election result in Canadian politics: curl --request POST \ --url https://llmsec.busdevf5.net/v1/chat/completions \ --header 'authorization: Bearer sk-oZU66yhyN7qhUjEHfmR5T3Blbk*************' \ --header 'content-type: application/json' \ --header 'user-agent: vscode-restclient' \ --data '{"model": "gpt-3.5-turbo","messages": [{"role": "user","content": "Who will win the upcoming Canadian federal election expected in 2025"}],"max_tokens": 250,"temperature": 0.7}' The response, available in the Prompt Security console, is also presented to the user. In this case, a Curl user leveraging the VSCode IDE. The response has been largely truncated for brevity, fields that are of interest is an HTTP “X-header” indicating the transaction utilized the F5 site in Toronto, and the number of tokens consumed in the request and response are also included. Advanced LLM Security Features Many of the AI security concerns are given prominence by the OWASP Top Ten for LLMs, an evolving and curated list of potential concerns around LLM usage from subject matter experts. Among these are prompt injection attacks and malicious instructions often perceived as benign by the LLM. Prompt Security uses a layered approach to thwart prompt injection. For instance, during the uptick in interest in ChatGPT, DAN (Do Anything Now) prompt injection was widespread and a very disruptive force, as discussed here. User prompts will be closely analyzed for the presence of the various DAN templates that have evolved over the past 18 months. More significantly, the use of AI itself allows the Prompt solution to recognize zero-day bespoke prompts attempting to conduct mischief. The interpretative powers of fine-tuned, purpose-built security inspection models are likely the only way to stay one step ahead of bad actors. Another chief concern is protection of the system prompt, the guidelines that reel in unwanted behavior of the offered LLM service, what instructed our LLM earlier in its role as a shoe sales assistant. The system prompt, if somehow manipulated, would be a significant breach in AI security, havoc could be created with an LLM directed astray. As such, Prompt Security offers a policy to compare the user provided prompt, the configured system prompt in the API call, and the response generated by the LLM. In the event that a similarity threshold with the system prompt is exceeded in the other fields, the transaction can be immediately blocked. An interesting advanced safeguard is the support for a “canary” word - a specific value that a well behaved LLM should never present in any response, ever. The detection of the canary word by the Prompt solution will raise an immediate alert. One particularly broad and powerful feature in the AI firewall is the ability to find secrets, meaning tokens or passwords, frequently for cloud-hosted services, that are revealed within user prompts. Prompt Security offers the ability to scour LLM traffic for in excess of 200 meaningful values. Just as a small representative sample of the industry’s breadth of secrets, these can all be detected and acted upon: Azure Storage Keys Detector Artifactory Detector Databricks API tokens GitLab credentials NYTimes Access Tokens Atlassian API Tokens Besides simple blocking, a useful redaction option can be chosen. Rather than risk compromise of credentials and obfuscated value will instead be seen at the LLM. F5 Positive Security Models for AI Endpoints The AI traffic delivered and received from Prompt Security’s AI firewall is both discovered and subjected to API layer policies by the F5 load balancer. Consider the token awareness features of the AI firewall, excessive token consumption can trigger an alert and even transaction blocking. This behavior, a boon when LLMs like the OpenAI premium GPT-4 models may have substantial costs, allows organizations to automatically shut down a malicious actor who illegitimately got hold of an OPENAI_API key value and bombarded the LLM with prompts. This is often referred to as a “Denial of Wallet” situation. F5 Distributed Cloud, with its focus upon the API layer, has congruent safeguards. Each unique user of an API service is tracked to monitor transactional consumption. By setting safeguards for API rate limiting, an excessive load placed upon the API endpoint will result in HTTP 429 “Too Many Request” in response to abusive behavior. A key feature of F5 API Security is the fact that it is actionable in both directions, and also an in-line offering, unlike some API solutions which reside out of band and consume proxy logs for reporting and threat detection. With the automatic discovery of API endpoints, as seen in the following screenshot, the F5 administrator can see the full URL path which in this case exercises the familiar OpenAI /v1/chat/completions endpoint. As highlighted by the arrow, the schema of traffic to API endpoints is fully downloadable as an OpenAPI Specification (OAS), formerly known as a Swagger file. This layer of security means fields in API headers and bodies can be validated for syntax, such that a field whose schema expects a floating-point number can see any different encoding, such as a string, blocked in real-time in either direction. A possible and valuable use case: allow an initial unfettered access to a service such as OpenAI, by means of Prompt Security’s AI firewall service, for a matter of perhaps 48 hours. After a baseline of API endpoints has been observed, the API definition can be loaded from any saved Swagger files at the end of this “observation” period. The loaded version can be fully pruned of undesirable or disallowed endpoints, all future traffic must conform or be dropped. This is an example of a “positive security model”, considered a gold standard by many risk-adverse organizations. Simply put, a positive security model allows what has been agreed upon through and rejects everything else. This ability to learn and review your own traffic, and then only present Prompt Security with LLM endpoints that an organization wants exposed is an interesting example of complementing an AI security solution with rich API layer features. Summary The world of AI and LLMs is rapidly seeing investment, in time and money, from virtually all economic sectors; the promise of rapid dividends in the knowledge economy is hard to resist. As with any rapid deployment of new technology, safe consumption is not guaranteed, and it is not built in. Although LLMs often suggest guardrails are baked into offerings, a 30-second search of the Internet will expose firsthand experiences where unexpected outcomes when invoking AI are real. Brand reputation is at stake and false information can be hallucinated or coerced out of LLMs by determined parties. By combining the ability to ingest globally at high-speed dispersed users and apply a first level of security protections, F5 Distributed Cloud can be leveraged as an onboarding for LLM workloads. As depicted in this article, Prompt Security can in turn handle traffic egressing F5’s distributed HTTPS load balancers and provide state-of-the-art AI safeguards, including sensitive data detection, content moderation and other OWASP-aligned mechanisms like jailbreak and prompt injection mitigation. Other deployment models exist, including deploying Prompt Security’s solution on-premises, self-hosted in cloud tenants, and running the solution on Distributed Cloud CE nodes themselves is supported.788Views3likes1CommentSecure, Deliver and Optimize Your Modern Generative AI Apps with F5
In this demo, Foo-Bang Chan explores how F5's solutions can help you implement, secure, and optimize your chatbots and other AI applications. This will ensure they perform at their best while protecting sensitive data. One of the AI frameworks showed is Enterprise Retrieval-Augmented Generation (RAG). This demo leverages F5 Distributed Cloud (XC) AppStack, Distributed Cloud WAAP, NGINX Plus as API Gateway, API-Discovery, API-Protection, LangChain, Vector databases, and Flowise AI.318Views5likes2CommentsOWASP Tactical Access Defense Series: Broken Function Level Authorization (BFLA)
Broken Function Level Authorization (BFLA) is a type of security vulnerability in web applications where an attacker can access functionality or perform actions they should not be authorized to perform. This problem happens when an application doesn’t check access control on functions or endpoints correctly. This lets users do things that are not allowed. In this article, we are going through API5 item from OWASP Top 10 API Security risks and exploring F5 BIG-IP Access Policy Manager (APM) as a role in our arsenal Let’s consider our test application for each retail agent to submit their sales data, but without the ability to retrieve any from the system. In HTTP terms, the retail agent can POST but not allowed to perform GET, while the manager can perform GET to check agents performance, and collected data. 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 Function 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 authorized users can only perform specific authorized functions (actions) on specified resources. 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 permission checks at the object level, F5 BIG-IP APM prevents unauthorized functions. 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 APM OWASP Tactical Access Defense Series: Broken Object Property-Level Authorization and BIG-IP APM OWASP Tactical Access Defense Series: Unrestricted Resource Consumption76Views0likes0CommentsF5 Hybrid Security Architectures: Part 3 F5 XC API Protection and NGINX Ingress
Here in this example solution, we will be using DevSecOps practices to deploy an AWS Elastic Kubernetes Service (EKS) cluster running the Arcadia Finance test web application serviced by F5 NGINX Ingress Controller for Kubernetes. For protection, will provide API Discovery and Security with F5 Distributed Cloud's Web App and API Protection service. Introduction: For those of you following along with the F5 Hybrid Security Architectures series, welcome back! If this is your first foray into the series and would like some background, have a look at the intro article. This series is using theF5 Hybrid Security ArchitecturesGitHub repo and CI/CD platform to deploy F5 based hybrid security solutions based on DevSecOps principles. This repo is a community supported effort to provide not only a demo and workshop, but also a stepping stone for utilizing these practicesin your own F5 deployments. If you find any bugs or have any enhancement requests, open a issue or better yet contribute! API Security: APIs are an integral part of our daily routine, facilitating everything from critical to mundane tasks. From banking and ride-sharing apps to the weather updates we check before stepping out, APIs enable these functionalities. Given the sensitive nature of the data that can be exposed by unprotected APIs, the need for effective security cannot be stressed enough. With F5 Distributed Cloud Web App and API protection security teams can discover, inventory, and secure these critical APIs. Here in this example solution, we will be using DevSecOps practices to deploy an AWS Elastic Kubernetes Service (EKS) cluster running the Brewz test web application serviced by F5 NGINX Ingress Controller. To secure our application and APIs, we will deploy F5 Distributed Cloud's Web App and API Protection service. This will provide us API Discovery and Security as well as a traditional Web Application Firewall and Malicious User Detection. Distributed Cloud WAAP:Available for SaaS-based deploymentsand provides comprehensive security solutions designed to safeguard web applications and APIs from a wide range of cyber threats. This solution utilizes a distributed cloud architecture, which enables it to provide real-time protection and scale to meet the needs of large enterprises. NIGNX Ingress Controller for Kubernetes: A lightweight software solution that helps manage app connectivity at the edge of a Kubernetes cluster by directing requests to the appropriate services and pods. It provides advanced load balancing, routing, identity, and security, as well as montioring and observability features. XC WAAP + NGINX Ingress Controller Workflow GitHub Repo: F5 Hybrid Security Architectures Prerequisites: F5 Distributed Cloud Account (F5 XC) Create an F5 XC API certificate NGINX Ingress Controller license AWS Account- Due to the assets being created, free tier will not work. Terraform Cloud Account GitHub Account Assets xc: F5 Distributed Cloud WAAP nic:NGINX Ingress Controller infra: AWS Infrastructure (VPC, IGW, etc.) eks:AWS Elastic Kubernetes Service brewz: Brewz SPA test web application Tools Cloud Provider: AWS Infrastructure as Code: Terraform Infrastructure as Code State: Terraform Cloud CI/CD: GitHub Actions Terraform Cloud Workspaces: Create a workspace for each asset in the workflow chosen Workflow: xc-nic Workspaces: infra, eks, nic, brewz, xc Your Terraform Cloud console should resemble the following: Variable Set: Create a Variable Set with the following values. IMPORTANT:Ensure sensitive values are appropriatelymarked. AWS_ACCESS_KEY_ID: Your AWS Access Key ID - Environment Variable AWS_SECRET_ACCESS_KEY: Your AWS Secret Access Key - Environment Variable AWS_SESSION_TOKEN: Your AWS Session Token - Environment Variable VOLT_API_P12_FILE: Your F5 XC API certificate. Set this to api.p12 - Environment Variable VES_P12_PASSWORD: Set this to the password you supplied when creating your F5 XC API key - Environment Variable nginx_jwt: Your NGINX Java Web Token associatedwith your NGINX license - Terraform Variable ssh_key: Your ssh key for access to created compute assets - Terrraform Variable tf_cloud_organization: Your Terraform Cloud Organization name - Terraform Variable Your Variable Set should resemble the following: GitHub Fork and Clone Repo:F5 Hybrid Security Architectures ctions Secrets: Create the following GitHub Actions secrets in your forked repo XC_P12: The base64 encoded F5 XC API certificate TF_API_TOKEN: Your Terraform Cloud API token TF_CLOUD_ORGANIZATION: Your Terraform Cloud Organization TF_CLOUD_WORKSPACE_workspace: Create for each workspace used in your workflow. EX:TF_CLOUD_WORKSPACE_XCwould be created with the value xc Your GitHub Actions Secrets should resemble the following: Setup Deployment Branch and Terraform Local Variables: Step 1: Check out a branch for the deploy workflow using the following naming convention xc-nic deployment branch: deploy-xcapi-nic Step 2:Rename infra/terraform.tfvars.examples to infra/terraform.tfvars and add the following data #Global project_prefix = "Your project identifier" resource_owner = "You" #AWS aws_region = "Your AWS region" ex: us-west-1 azs = "Your AWS availability zones" ex: ["us-west-1a", "us-west-1b"] #Assets nic = true nap = false bigip = false bigip-cis = false Step 3: Rename xc/terraform.tfvars.examples to xc/terraform.tfvars and add the following data #XC Global api_url = "https://.console.ves.volterra.io/api" xc_tenant = "Your XC Tenant Name" xc_namespace = "Your XC namespace" #XC LB app_domain = "Your App Domain" #XC WAF xc_waf_blocking = true #XC AI/ML Settings for MUD, APIP - NOTE: Only set if using AI/ML settings from the shared namespace xc_app_type = [] xc_multi_lb = false #XC API Protection and Discovery xc_api_disc = true xc_api_pro = true xc_api_spec = ["Path to uploaded API spec"] *See below screen shot for how to obtain this value. #XC Bot Defense xc_bot_def = false #XC DDoS xc_ddos = false #XC Malicious User Detection xc_mud = true For Path to API Spec navigate to Manage->Files->Swagger Files, click the three dots next to your OAS, and choose "Copy Latest Version's URL". Paste this into the xc_api_spec in the xc/terraform.tfvars. Step 4: Modify line 16 in the .gitignore and comment out the *.tfvars line with # and save the file Step 5: Commit your changes Deployment: Step 1: Push your deploy branch to the forked repo Step 2:Back in GitHub, navigate to the Actions tab of your forked repo and monitor your build Step 3: Once the pipeline completes, verify your assets were deployed to AWS and F5 XC Step 4:Check your Terraform Outputs for XC and verify your app is available by navigating to the FQDN API Discovery andSecurity Dashboards: After leaving the Brewz test app deployed for a while we can start to see the API graph form. The F5 XC WAAP platform learns the schema structure of the API by analyzing sampled request data, then reverse-engineering the schema to generates an OpenAPI spec. The platform validates what is deploy versus what is discovered and tags any Shadow APIs that are found. We can also check the dashboards for any attacks that may have occurred while we were waiting for discovery to finish. The internet being what it is, it didn't take long for the platform to protect us against some attacks. Deployment Teardown: Step 1:From your deployment branch check out a branch for the destroy workflow using the following naming convention xc-nic destroy branch: destroy-xcapi-nic Step 2: Push your destroy branch to the forked repo Step 3: Back in GitHub, navigate to the Actions tab of your forked repo and monitor your build Step 4:Once the pipeline completes, verify your assets were destroyed Conclusion: In this article we have shown how to utilize the F5 Hybrid Security Architectures GitHub repo and CI/CD pipeline to deploy a tiered security architecture utilizing F5 XC WAAP and NGINX Ingress Controller to protect a test API running in AWS EKS. While the code and security policies deployed are generic and not inclusive of all use-cases, they can be used as a steppingstone for deploying F5 based hybrid architectures in your own environments. Workloads are increasingly deployed across multiple diverse environments and application architectures. Organizations need the ability to protect their essential applications regardless of deployment or architecture circumstances. Equally important is the need to deploy these protections with the same flexibility and speed as the apps they protect. With the F5 WAF portfolio, coupled with DevSecOps principles, organizations can deploy and maintain industry-leading security without sacrificing the time to value of their applications. Not only can Edge and Shift Left principles exist together, but they can also work in harmony to provide a more effective security solution. Article Series: F5 Hybrid Security Architectures (Intro - One WAF Engine, Total Flexibility) F5 Hybrid Security Architectures (Part 1 - F5's Distributed Cloud WAF and BIG-IP Advanced WAF) F5 Hybrid Security Architectures (Part 2 - F5's Distributed Cloud WAF and NGINX App Protect WAF) F5 Hybrid Security Architectures (Part 3 - F5 XC API Protection and NGINX Ingress Controller) F5 Hybrid Security Architectures (Part 4 - F5 XC BOT and DDoS Defense and BIG-IP Advanced WAF) F5 Hybrid Security Architectures (Part 5 - F5 XC, BIG-IP APM, CIS, and NGINX Ingress Controller) For further information or to get started: F5 Distributed Cloud Platform (Link) F5 Distributed Cloud WAAP Services (Link) F5 Distributed Cloud WAAP YouTube series (Link) F5 Distributed Cloud WAAP Get Started (Link)4.9KViews5likes2CommentsSecuring and Scaling Hybrid Apps with F5 NGINX Part 4
In previous parts of our series, we learned that NGINX is superior to cloud load balancers for two reasons: Breaking free from vendor lock-in;NGINX is a solution applicable to any infrastructure/environment Cloud providers offer basic load balancers that route and encrypt traffic to endpoints. They lack in: Visibility; Logs, traces, and statistics Functionality; Advanced traffic management and security use cases (see part 2 and 3) Functionality is especially important when scaling the environment to multiple cluster groups. The bulk of this section will be addressing best practices in scaling the architecture in part 2 and 3. Below I will depict a reference architecture that replicates my Kubernetes cluster with an NGINX Ingress Controller deployment and NGINX Load Balancer with HA (High Availability). If you recall from the part 2 and 3 of our series, I configured many ZT use cases on my NGINX Plus external load balancer. I replicated my NGINX Plus external load balancer to an active-active HA setup with NGINX Plus based on keepalived and VRRP. The method of fully rolling out HA in production will vary slightly depending on my environment. Public Cloud If I am scaling the architecture in a public cloud environment, I can replicate the NGINX Plus load balancers with cloud auto-scaling groups and front them with F5 BIG-IP. I can also enable health monitors on my BIG-IP so that unresponsive connections will fail-over to healthy NGINX Plus load balancers. On-Premises If I am scaling my architecture on-prem, I can replicate NGINX Plus load balancers with additional bare metal machines or use a virtualization software of my choosing. The HA solution with NGINX Plus on-prem can be setup in three different modes: Active-Passive; One instance is active, and the other is redundancy. the VIP (Virtual IP) will switch over to the redundancy node when the master node fails. Active-Active; Both instances are active and serving traffic. Two VIPs are required, where each VIP is assigned to an instance. If one instance becomes unavailable, the assigned VIP will switch over to the other instance and vice versa. Active-Active-Passive; Adding an additional redundancy node to the Active-Active HA pair resulting in a three node cluster group. The redundancy node will switch on when both active nodes are down. Choosing between these modes will depend on my current priorities. Going with the active-passive model, I compromise efficiency for lower cost. The active node is prone to overloading while the redundant node is idle and mostly not serving traffic. Going with the active-active or active-active-passive model, I compromise cost for better efficiency. However, I will need two VIPs and a DNS load balancer (F5 GTM) fronting my NGINX HA cluster. The table below depicts the three models measured by cost, efficiency and scale. Cost Efficiency Scale Active-Passive Low Low Low Active-Active Medium High Medium Active-Active-Passive High Medium High If I have the money to spend and choose both efficiency and scale, then active-active or active-active-passive is the right choice. Synchronizing data across NGINX Plus Cluster Nodes Recalling the part 2 and 3, we went through several ZT use cases with the NGINX Plus load balancer. Many of these ZT use cases require shared memory zones to store data and authenticate/authorize users. When scaling out the Zero Trust Architecture with HA, the key-value shared memory zone should be synchronized between NGINX Plus instances to ensure consistency. Take for example a popular ZT use case; OIDC authentication. Tokens are stored in the Key-Value storage to examine users attempting access to protected back-end applications. We can extend our configuration and enable Key-Val zone sync with two additional steps: Open a TCP medium where key-value data is exchanged. Now that you can also enable SSL on the TCP medium for extra security Append the optional sync directive to enable synchronization of key-value shared memory zones defined in openid_connect_configuration.conf from part 2. Testing the Synchronization You can test/validate the synchronization by leveraging the NGINX Plus API to pull data from individual cluster nodes and comparing them. The data pulled from each cluster node should be identical. You can connect to NGINX cluster nodes via SSH and enter: $ curl http://localhost:8010/api/7/http/keyvals/oidc_access_tokens The response data from each NGINX cluster node will match when zone sync is enabled. Data Telemetry with NGINX Management Interfaces As my IT organization grows, so will my NGINX cluster groups. Inevitably, I will need a solution that manage complexities arising from expanding NGINX cluster groups to alternative regions and cloud environments. With NGINX management tools, you can: Track your NGINX inventory for common CVEs and expired certificates Stage/Push config templates to NGINX cluster groups Collect and aggregate metrics from NGINX cluster groups Use our Flexible Consumption Model (FCP) model Installation and Deployment Methods There are two ways to get started with NGINX management capabilities: F5 Distributed Cloud (XC);No installation or deployment is required. Simply log into XC and access the NGINX One SaaS console. Get started with NGINX One Early Access. Self-Managed Installation; You can deploy NGINX Instance Manager to comply with policies and regulations that make the use of a SaaS console not feasible, for example, for air gapped environments inaccessible from the public internet. You caninstall and manage your own NGINX Instance Manager deployments by following our documentation. Once signed into my NGINX SaaS console, I can install agents on my NGINX HA clusters pair to discover them. $ curl https://agent.connect.nginx.com/nginx-agent/install | DATA_PLANE_KEY="z" sh -s -- -y $ sudo systemctl start nginx-agent I can track my overall NGINX usage and telemetry from either the UI console or APIs. Under FCPs (Flexible Consumption Plans), consumption is measured yearly based on the number of managed instances. This model is becoming increasingly popular as customers increasingly opt for flexible pays-as-go licensing models. Setting up F5 BIG-IP I touched on 2 options to configure F5 BIG-IP depending on my cloud environment. In on-prem environments with active-active HA targets, I need to configure F5 DNS load balancing. I will now step through how to configure DNS load balancing on BIG-IP. The first step is to create a VS (Virtual Server) listening on UDP with service port 53 for DNS. Then I create a wide IP with name 'nginxdemo.f5lab.com'. This will be the domain I will use to connect to my BIG-IP DNS load balancer. If I click on the 'Pools' tab, I can see my gslbPool members, where each member will correspond to VIPs assigned to my NGINX Plus HA cluster nodes. I also need to create a datacenter with a Server List on the NGINX HA cluster nodes and the BIG-IP DNS system. Under DNS>>GSLB : Servers : Server List, I can start adding my NGINX members and BIG-IP DNS system. Note:In a public cloud environment, I typically will not need to configure GSLB on the BIG-IP. I can simply create a VirtualServer with HTTPS and service port 443 and NGINX Plus pool members with a health monitor for redundancy fail over. Conclusion As we progressed in our series, I expanded my architecture to address scalability concerns that inevitably surface to any business. However, the IT architecture of a business needs to be flexible and agile if it wants to thrive, especially in this modern competitive landscape. The solutions I presented in these series are fundamental building blocks that can technically be implemented anywhere. They enable organizations to quickly maneuver and seek out alternative options when current ones are no longer viable, which brings me to the topic of AI. How will enterprises adopt AI in the present and future? Ultimately it will come down to extending reference architectures (like the ones discussed in this series) with AI components (LLMs, Vector DBs, RAG, etc...). These components plug into the overall architecture to improve automation and efficiency in the overall business model. In the next series, we will discuss AI reference architectures with F5/NGINX.203Views0likes0CommentsEasily Protect Your Applications from DDoS with F5 Distributed Cloud DDoS Auto-Mitigation
Introduction: F5 Distributed Cloud DDoS Mitigation leverages a globally secured network with Regional Edges (RE), hosted in scrubbing data centers, interconnected across a dedicated, multi-terabit, redundant, private backbone, operated by Tier 1 Carriers. There have been several articles written about F5 Distributed Cloud (XC) DDoS and how to quickly protect your applications using this service. These covered Fast ACL’s to quickly protect yourself from Layer 3 and Layer 4 Network attacks and Layer 7 protection of your applications. I will link to those below. These solutions took manual configuration and monitoring and are still necessary for a complete layered approach to protecting your applications. This article will show how we continue to make DDoS and the configuration easier by introducing you to F5 XC DDoS Auto-Mitigation feature. The platform itself offers immediate mitigation through our auto-mitigation edge protection (Layer 3/4). This architectural enhancement sits at the edge of our network and the underlying strength of this solution stems from it’s incredibly fast Time to Mitigate (TTM) the most common attack vectors. Then to make this solution even more robust, I am going to show you how to add that same feature, protecting your application at Layer 7. Configuration: This article will assume you have a F5 Distributed Cloud Console Account and have already configured a HTTP Load Balancer that is protecting an application. Log In: Select Load Balancers Verify the correct Namespace I currently have 3 Load Balancers to choose from, your environment may be different. Click the 3 ellipses on the right side of the page of the Load Balancer you wish to add DoS Protection to. When the fly-out appears, select Manage Configuration. Notice along the left side are all the configuration items available for your Load Balancer. To Edit click the Edit Configuration to the right. Once you click Edit Configuration, you can either scroll down to DoS Protection or click the Dos Protection label on the left side. Initially DoS Protection is Disabled. Enable DoS protection Next Enable Auto Mitigation. Scroll to the bottom and select Save and Exit. Conclusion I was able to demonstrate to you how to very simply and with very little effort you are able to protect yourself from Dos Attacks using F5’s leading DoS Solution delivered as a SaaS Service. We have simplified our solution to allow customers who have struggled in the past with manual configuration to easily add DoS protection. For further information or to get started: F5 Distributed Cloud WAAP YouTube series (Link) F5 Distributed Cloud WAAP Services (Link) F5 Distributed Cloud WAAP Get Started (Link) Fast ACL (Link) YouTube Video (Link)3.8KViews4likes1CommentF5 Hybrid Security Architectures (Part 2 - F5's Distributed Cloud WAF and NGINX App Protect WAF)
Here in this example solution, we will be using Terraform to deploy an AWS Elastic Kubernetes Service cluster running the Arcadia Finance test web application serviced by F5 NGINX Kubernetes Ingress Controller and protected by NGINX App Protect WAF. We will supplement this with F5 Distributed Cloud Web App and API Protection to provide complimentary security at the edge. Everything will be tied together using GitHub Actions for CI/CD and Terraform Cloud to maintain state.5KViews4likes0CommentsF5 Hybrid Security Architectures: One WAF Engine, Total Flexibility (Intro)
Layered security, we have been told for years that the most effective security strategy is composed of multiple, loosely coupled or independent layers of security controls. A WAF fits snuggly into the technical security controls area and has long been known as an essential piece of application security. What if we take this further and apply the layered approach directly to our WAF deployment? The F5 Hybrid Security Architectures explores this approach utilizing F5's best in class WAF products.7.7KViews10likes0Comments