The Potential Ramifications of Platform-Based Vulnerabilities on Cloud Computing

#infosec #adcfw #cloud Alternate title: How to take out an entire PaaS cloud with one vulnerability

Apache Killer.

Post of Doom.

What do these two vulnerabilities have in common? Right, they’re platform-based vulnerabilities. Meaning they are vulnerabilities peculiar to the web or application server platform upon which applications are deployed. Mitigations for such vulnerabilities generally point to changes in configuration of the platform – limit post size, header value sizes, turn off some value in the associated configuration.

But they also have something else in common – risk. And not just risk in general, but risk to cloud providers whose primary value is in offering not just a virtual server but an entire, pre-integrated and pre-configured application deployment stack. Think LAMP, as an example, and providers like Microsoft (Azure) and VMware (CloudFoundry), more commonly adopting the moniker of PaaS. It’s an operational dream to have a virtual server pre-configured and ready to go with the exact application deployment stack needed and offers a great deal of value in terms of efficiency and overall operational investment, but it is – or should be – a security professional’s nightmare. It’s not unlike the recent recall of Chevy Volts – a defect in the platform needs to be mitigated. The only way to do it, for car owners, is to effectively shut down their ability to drive while a patch is applied. It’s disruptive, it’s expensive (you still have to get to work, after all), and it’s frustrating for the consumer. For the provider, it’s bad PR and negatively impacts the brand. Neither of which is appealing.

A vulnerability in the application stack, in the web or application server, can be operationally devastating to the provider – and potentially disruptive to the consumer whether the vulnerability is exploited or not.

STANDARDIZATION is a DOUBLE-EDGED SWORD

Assume a homogeneous cloud environment offering an application stack based on Microsoft ASP. Assume now an exploit, oh say like Post of Doom, is discovered whose primary mitigation lies in modifying the configuration of each and every instance. Virtualization of any kind provides a solution, of course, but introduces the possibility of disruption in the impact to consumer applications from the configuration change. A primary mitigation for the Post of Doom is to limit the size of data in a POST to under 8MB. Depending on the application, this has to potential to “break” application functionality, particularly those for which uploading big data is a focus. Images, video, documents, etc… These all may be impacted negatively, disrupting applications and angering consumers.

Patching, of course, is preferred, as it eliminates the underlying vulnerability without potentially breaking applications. But patching takes time – time to develop, time to test, time to deploy. The actual delivery of such patches in a PaaS environment is a delicate operation. You can’t just shut the whole cloud down and restart it after the patches are applied to the base images, can you? Do you wait, quiesce the vulnerable images and only force the patched ones when new instances are provisioned? A configuration-based mitigation, too, has these same issues. You can’t just shut down the whole cloud, apply the change, and reboot.

It’s a delicate balance of security versus availability that must struck for the provider, and certainly their position in such cases is one not to be envied. Damned if they do, damned if they don’t.

Then there is the risk of exploitation before any mitigation is applied. If I want to wreak havoc on a PaaS, I may be able to accomplish simply by finding one with the appropriate platform vulnerable to a given exploit, and attack. Cycling through applications deployed in that environment (easily identified at the network layer by the IP ranges assigned to the provider) should result in a wealth of chaos being wrought. The right vulnerability could take out a significant enough portion of the environment to garner attention from the outages caused.

Enterprise organizations that think they are immune from such issues should think again, as even a cloud provider is often not as standardized on a single application platform as an enterprise is, and it is that standardization that is at the root of the potential risk from platform-based vulnerabilities. Standardization, commoditization, these are good things in terms of many financial and operational benefits, but they can also cause operational risk to increase.

MITIGATE in the MIDDLE

There is a better solution, a better strategy, a better operational means of mitigating platform-based risks.

This is where the role of a flexible, broad-spectrum layer of security applies. One that enables security professionals to broadly apply security policies to quickly mitigate potentially disastrous vulnerabilities. Without disrupting a single running instance, an organization can deploy a mitigating solution that detects and prevents the effects of such vulnerabilities. Applying security policies that mitigate such vulnerabilities before they reach the platform is critical to preventing a disaster of epic (and newsworthy) proportions.

Whether stop gap or a permanent solution, by leveraging the application delivery tier of any data center – enterprise or cloud provider – such vulnerabilities can be addressed without imposing harsh penalties on applications and application owners, such as requiring complete shutdown and reboots.

Leveraging such a flexible data center tier insulates the platform from exploitation while insulating customers from the disruption required to mitigate immediately on the platform layer, allowing time to redress through patches or, at least, understand the potential implication to the application from the platform configuration changes required to mitigate the vulnerability.

In today’s data center, time is perhaps the biggest benefit afforded to IT by any solution, and yet the one least likely to be provided. A flexible application delivery tier capable of mitigating threats across the network and application stack without disruption is one of the few solutions available that offers the elusive and very valuable benefit of time. Providers and enterprises alike need to consider their current data center architecture and whether it supports the notion of such a dynamic tier. If not, it’s time to re-evaluate and determine whether a strategic change of direction is necessary to ensure the ability of operations and security teams to address operational risk as quickly and efficiently as possible.



Published Feb 08, 2012
Version 1.0
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This short WAF security article covered the critical security gaps present in current generative AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. Finally we also demonstrated how F5 Nginx App Protect v5 offers an effective solution to mitigate the OWASP LLM Top 10 risks.

","body":"

Introduction:

\n

This article covers the basics of the rise of AI (Gen AI) and how F5 products can protect these AI backends. The rise of Large Language Models (LLMs) has marked a transformative era in AI, enabling machines to produce and comprehend text with human-like proficiency. These sophisticated models are now integral to applications in customer support, content creation, and even scientific research. However, their advanced capabilities also cause big security worries, especially around accidentally sharing sensitive information. These models can sometimes share private data from their training data. This means we need strong protection systems to reduce these risks. Addressing these challenges, the OWASP LLM Top 10 project has been created to identify and prioritize the most critical security threats associated with LLMs. Out of this top 10, LLM-06 risk specifically focuses on sensitive information disclosure, emphasizing the importance of stringent data handling protocols and privacy safeguards to prevent unintended data leaks to ensure the secure and ethical use of LLM technology. In this article, we are going to see how F5 Nginx App Protect v5 can protect the LLM backends from LLM06: Sensitive Information Disclosure risk.

\n

 

\n

Use case:

\n

We are going to deploy a Gen AI application which takes URL hosting data and is passed on to a backend LLM application. Once data is analyzed by LLM, users can ask questions about this data and LLM will come back with the right answers. We have deployed this application inside AWS EKS cluster and 2 application services are running inside this cluster. Front-end services serve the UI, and the backend hosts the LLM model. How and what of this application with its internal tools and LLM model is not of importance and can find many free tools online. Since this article focusses on LLM06: Sensitive Information Disclosure risk, we will pass a website URL containing some dummy SSN of random users. Once this website data is loaded to LLM, we can ask for the SSN of a user and LLM will return the SSN from this data. SSNs are sensitive information and should always be protected as it will lead to personal data exploitation. In this case, LLM model does not have security rules to find and protect this data. So, it will be directly shown in the response as shown below.

\n\n

To protect this LLM backend service, we are going to deploy and configure NGINX App Protect Version 5 as a k8s workload in the data path. The latest release of NGINX App Protect v5 has made the WAF process more ridiculously easy, thereby making it more efficient and optimized. All data traffic will be validated by the NGINX App Protect before being exposed in the response. In this use case, since we want to mask the SSN, we are going to configure the data-guard feature with its appropriate configuration files onboarded to this container. 

\n\n

Above configuration file can be downloaded from NGINX App Protect WAF configuration guide.

\n

Data guard is a WAF feature which detects and masks Credit Card Number (CCN) and/or U.S. Social Security Number (SSN) and/or custom patterns in HTTP responses. Since data-guard feature is enabled, SSNs of users in the LLM backend response are detected and masked by the NGINX App Protect thereby protecting the personal data. For more info on NGINX App Protect data guard feature, check this link.
 
NOTE: Since this is just for demo and focuses only on LLM workload protection, we are using NGINX App Protect v5. But as per customer practices, users can configure NGINX Ingress Controller, Secure Mesh, etc.

\n\n

 

\n

Deployment Steps: 

\n
    \n
  1. Check the service cluster IP of backend LLM service and update it in below yaml file upstream server to create nginx config file configmap
    apiVersion: v1\nkind: ConfigMap\nmetadata:\n  name: nginx-conf-map-api\n  namespace: default\ndata:\n  nginx.conf: |\n    user nginx;\n\n    worker_processes auto;\n    load_module modules/ngx_http_app_protect_module.so;\n\n    error_log /var/log/nginx/error.log debug;\n\n    events {\n        worker_connections 10240;\n    }\n\n    http {\n        include /etc/nginx/mime.types;\n        default_type application/octet-stream;\n        sendfile on;\n        keepalive_timeout 65;\n        \n        # NGINX App Protect WAF\n        app_protect_enforcer_address 127.0.0.1:50000;\n\n        upstream main_DNS_name {\n            server 172.20.41.242:8000;\n        }\n        server {\n            listen 80;\n            proxy_http_version 1.1;\n            proxy_read_timeout 600;\n            proxy_connect_timeout 600;\n            proxy_send_timeout 600;\n\n            app_protect_enable on;\n            app_protect_policy_file \"/etc/app_protect/bundles/NAP_API_Policy.tgz\";\n            app_protect_security_log_enable on;\n            app_protect_security_log log_all /etc/app_protect/bundles/security.log;\n\n            location / {\n                client_max_body_size 0;\n                default_type text/html;\n                # set your backend here\n                proxy_pass http://main_DNS_name;\n                proxy_set_header Host $host;\n            }\n        }\n    }\n
     
  2. \n
  3. Build and push a NGINX Plus docker image to your private registry by following this link
  4. \n
  5. Copy your JWT token and run below command to create a k8s secret
    # kubectl create secret docker-registry regcred --docker-server=private-registry.nginx.com --docker-username=<JWT Token> --docker-password=none

  6. \n
  7. Check the below file, update API policy bundle URL in init container and docker image info in nginx container. Apply this file to install nginx deployment and pods.
    apiVersion: apps/v1\nkind: Deployment\nmetadata:\n  name: nap5-deployment\nspec:\n  selector:\n    matchLabels:\n      app: nap5\n  replicas: 1\n  template:\n    metadata:\n      labels:\n        app: nap5\n    spec:\n      imagePullSecrets:\n        - name: regcred\n      initContainers:\n      - name: init-fetchbundle\n        image: curlimages/curl:8.9.1\n        command: \n        - sh\n        - -c\n        - |\n          echo \"Downloading file...\"\n          curl -vvv -L https://github.com/f5devcentral/f5-xc-terraform-examples/raw/main/workflow-guides/NAP_API_Policy.tgz -o /etc/app_protect/bundles/NAP_API_Policy.tgz \n        volumeMounts:\n        - name: app-protect-bundles\n          mountPath: /etc/app_protect/bundles         \n      containers:\n        - name: nginx\n          image: <registry-url>:tag-name\n          imagePullPolicy: IfNotPresent\n          volumeMounts:\n            - name: app-protect-bd-config\n              mountPath: /opt/app_protect/bd_config\n            - name: app-protect-config\n              mountPath: /opt/app_protect/config\n            - name: nginx-conf-map-api-volume\n              mountPath: /etc/nginx/nginx.conf\n              subPath: nginx.conf\n            - name: nap-api-policy-volume\n              mountPath: /etc/nginx/NAP_API_Policy.json\n              subPath: NAP_API_Policy.json   \n              \n        - name: waf-enforcer\n          image: private-registry.nginx.com/nap/waf-enforcer:5.2.0\n          imagePullPolicy: IfNotPresent\n          env:\n            - name: ENFORCER_PORT\n              value: \"50000\"\n          volumeMounts:\n            - name: app-protect-bd-config\n              mountPath: /opt/app_protect/bd_config \n          \n        - name: waf-config-mgr\n          image: private-registry.nginx.com/nap/waf-config-mgr:5.2.0\n          imagePullPolicy: IfNotPresent\n          securityContext:\n            allowPrivilegeEscalation: false\n            capabilities:\n              drop:\n                - all\n          volumeMounts:\n            - name: app-protect-bd-config\n              mountPath: /opt/app_protect/bd_config\n            - name: app-protect-config\n              mountPath: /opt/app_protect/config\n            - name: app-protect-bundles\n              mountPath: /etc/app_protect/bundles\n              \n      volumes:\n        - name: app-protect-bd-config\n          emptyDir: {}\n        - name: app-protect-config\n          emptyDir: {}\n        - name: app-protect-bundles\n          emptyDir: {}\n        - name: nginx-conf-map-api-volume\n          configMap:\n            name: nginx-conf-map-api\n        - name: nap-api-policy-volume\n          configMap:\n            name: nap-api-policy            


  8. \n
  9. Next deploy NGINX App Protect service using below file
    apiVersion: v1\nkind: Service\nmetadata:\n  name: nap5\n  labels:\n    app: nap5\n    service: nap5\nspec:\n  ports:\n    - protocol: TCP\n      port: 80\n      targetPort: 80\n  selector:\n    app: nap5\n  type: ClusterIP
    \n

     

    \n
  10. \n
  11. Check the cluster services and copy the NGINX App Protect service cluster ip
  12. \n
  13. Update App Protect cluster ip address in your Gen AI application frontend yaml file openAI address to create deployment and load balancer service

  14. \n
\n

Testing:

\n\n

Conclusion:

\n

This article highlights the critical security gaps present in current Gen AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. In the latter half, we demonstrated how F5 NGINX App Protect v5, with its advanced security features, offers an effective solution to mitigate the OWASP LLM Top 10 risks. By leveraging these capabilities, organizations can significantly enhance the security and resilience of their AI applications.

\n

 

\n
References:
\n\n

 

\n

NOTE: This article covered only one risk and stay tuned for more exciting articles on remaining OWASP LLM Top 10 risks prevention using F5 products.

","body@stringLength":"23740","rawBody":"

Introduction:

\n

This article covers the basics of the rise of AI (Gen AI) and how F5 products can protect these AI backends. The rise of Large Language Models (LLMs) has marked a transformative era in AI, enabling machines to produce and comprehend text with human-like proficiency. These sophisticated models are now integral to applications in customer support, content creation, and even scientific research. However, their advanced capabilities also cause big security worries, especially around accidentally sharing sensitive information. These models can sometimes share private data from their training data. This means we need strong protection systems to reduce these risks. Addressing these challenges, the OWASP LLM Top 10 project has been created to identify and prioritize the most critical security threats associated with LLMs. Out of this top 10, LLM-06 risk specifically focuses on sensitive information disclosure, emphasizing the importance of stringent data handling protocols and privacy safeguards to prevent unintended data leaks to ensure the secure and ethical use of LLM technology. In this article, we are going to see how F5 Nginx App Protect v5 can protect the LLM backends from LLM06: Sensitive Information Disclosure risk.

\n

 

\n

Use case:

\n

We are going to deploy a Gen AI application which takes URL hosting data and is passed on to a backend LLM application. Once data is analyzed by LLM, users can ask questions about this data and LLM will come back with the right answers. We have deployed this application inside AWS EKS cluster and 2 application services are running inside this cluster. Front-end services serve the UI, and the backend hosts the LLM model. How and what of this application with its internal tools and LLM model is not of importance and can find many free tools online. Since this article focusses on LLM06: Sensitive Information Disclosure risk, we will pass a website URL containing some dummy SSN of random users. Once this website data is loaded to LLM, we can ask for the SSN of a user and LLM will return the SSN from this data. SSNs are sensitive information and should always be protected as it will lead to personal data exploitation. In this case, LLM model does not have security rules to find and protect this data. So, it will be directly shown in the response as shown below.

\n\n

To protect this LLM backend service, we are going to deploy and configure NGINX App Protect Version 5 as a k8s workload in the data path. The latest release of NGINX App Protect v5 has made the WAF process more ridiculously easy, thereby making it more efficient and optimized. All data traffic will be validated by the NGINX App Protect before being exposed in the response. In this use case, since we want to mask the SSN, we are going to configure the data-guard feature with its appropriate configuration files onboarded to this container. 

\n\n

Above configuration file can be downloaded from NGINX App Protect WAF configuration guide.

\n

Data guard is a WAF feature which detects and masks Credit Card Number (CCN) and/or U.S. Social Security Number (SSN) and/or custom patterns in HTTP responses. Since data-guard feature is enabled, SSNs of users in the LLM backend response are detected and masked by the NGINX App Protect thereby protecting the personal data. For more info on NGINX App Protect data guard feature, check this link.
 
NOTE: Since this is just for demo and focuses only on LLM workload protection, we are using NGINX App Protect v5. But as per customer practices, users can configure NGINX Ingress Controller, Secure Mesh, etc.

\n\n

 

\n

Deployment Steps: 

\n
    \n
  1. Check the service cluster IP of backend LLM service and update it in below yaml file upstream server to create nginx config file configmap
    apiVersion: v1\nkind: ConfigMap\nmetadata:\n name: nginx-conf-map-api\n namespace: default\ndata:\n nginx.conf: |\n user nginx;\n\n worker_processes auto;\n load_module modules/ngx_http_app_protect_module.so;\n\n error_log /var/log/nginx/error.log debug;\n\n events {\n worker_connections 10240;\n }\n\n http {\n include /etc/nginx/mime.types;\n default_type application/octet-stream;\n sendfile on;\n keepalive_timeout 65;\n \n # NGINX App Protect WAF\n app_protect_enforcer_address 127.0.0.1:50000;\n\n upstream main_DNS_name {\n server 172.20.41.242:8000;\n }\n server {\n listen 80;\n proxy_http_version 1.1;\n proxy_read_timeout 600;\n proxy_connect_timeout 600;\n proxy_send_timeout 600;\n\n app_protect_enable on;\n app_protect_policy_file \"/etc/app_protect/bundles/NAP_API_Policy.tgz\";\n app_protect_security_log_enable on;\n app_protect_security_log log_all /etc/app_protect/bundles/security.log;\n\n location / {\n client_max_body_size 0;\n default_type text/html;\n # set your backend here\n proxy_pass http://main_DNS_name;\n proxy_set_header Host $host;\n }\n }\n }\n 
  2. \n
  3. Build and push a NGINX Plus docker image to your private registry by following this link
  4. \n
  5. Copy your JWT token and run below command to create a k8s secret
    # kubectl create secret docker-registry regcred --docker-server=private-registry.nginx.com --docker-username=<JWT Token> --docker-password=none

  6. \n
  7. Check the below file, update API policy bundle URL in init container and docker image info in nginx container. Apply this file to install nginx deployment and pods.
    apiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: nap5-deployment\nspec:\n selector:\n matchLabels:\n app: nap5\n replicas: 1\n template:\n metadata:\n labels:\n app: nap5\n spec:\n imagePullSecrets:\n - name: regcred\n initContainers:\n - name: init-fetchbundle\n image: curlimages/curl:8.9.1\n command: \n - sh\n - -c\n - |\n echo \"Downloading file...\"\n curl -vvv -L https://github.com/f5devcentral/f5-xc-terraform-examples/raw/main/workflow-guides/NAP_API_Policy.tgz -o /etc/app_protect/bundles/NAP_API_Policy.tgz \n volumeMounts:\n - name: app-protect-bundles\n mountPath: /etc/app_protect/bundles \n containers:\n - name: nginx\n image: <registry-url>:tag-name\n imagePullPolicy: IfNotPresent\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config\n - name: app-protect-config\n mountPath: /opt/app_protect/config\n - name: nginx-conf-map-api-volume\n mountPath: /etc/nginx/nginx.conf\n subPath: nginx.conf\n - name: nap-api-policy-volume\n mountPath: /etc/nginx/NAP_API_Policy.json\n subPath: NAP_API_Policy.json \n \n - name: waf-enforcer\n image: private-registry.nginx.com/nap/waf-enforcer:5.2.0\n imagePullPolicy: IfNotPresent\n env:\n - name: ENFORCER_PORT\n value: \"50000\"\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config \n \n - name: waf-config-mgr\n image: private-registry.nginx.com/nap/waf-config-mgr:5.2.0\n imagePullPolicy: IfNotPresent\n securityContext:\n allowPrivilegeEscalation: false\n capabilities:\n drop:\n - all\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config\n - name: app-protect-config\n mountPath: /opt/app_protect/config\n - name: app-protect-bundles\n mountPath: /etc/app_protect/bundles\n \n volumes:\n - name: app-protect-bd-config\n emptyDir: {}\n - name: app-protect-config\n emptyDir: {}\n - name: app-protect-bundles\n emptyDir: {}\n - name: nginx-conf-map-api-volume\n configMap:\n name: nginx-conf-map-api\n - name: nap-api-policy-volume\n configMap:\n name: nap-api-policy

  8. \n
  9. Next deploy NGINX App Protect service using below file
    apiVersion: v1\nkind: Service\nmetadata:\n name: nap5\n labels:\n app: nap5\n service: nap5\nspec:\n ports:\n - protocol: TCP\n port: 80\n targetPort: 80\n selector:\n app: nap5\n type: ClusterIP\n

     

    \n
  10. \n
  11. Check the cluster services and copy the NGINX App Protect service cluster ip
  12. \n
  13. Update App Protect cluster ip address in your Gen AI application frontend yaml file openAI address to create deployment and load balancer service

  14. \n
\n

Testing:

\n\n

Conclusion:

\n

This article highlights the critical security gaps present in current Gen AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. In the latter half, we demonstrated how F5 NGINX App Protect v5, with its advanced security features, offers an effective solution to mitigate the OWASP LLM Top 10 risks. By leveraging these capabilities, organizations can significantly enhance the security and resilience of their AI applications.

\n

 

\n
References:
\n\n

 

\n

NOTE: This article covered only one risk and stay tuned for more exciting articles on remaining OWASP LLM Top 10 risks prevention using F5 products.

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This short WAF security article covered the critical security gaps present in current generative AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. Finally we also demonstrated how F5 Nginx App Protect v5 offers an effective solution to mitigate the OWASP LLM Top 10 risks.

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