Earlier this week it was reported that an Equifax web service was hacked creating a breach that existed for about 10 weeks. During that time the attackers used that breach to drain 143 million people’s private information. The precise technical details of the breach, which Equifax claims was detected and closed on July 29, has yet to be revealed. While it says it’s seen no other criminal activity on its main services since July 29th that’s of little concern as Elvis has left the building. At 143 million that means a majority of the adults in the US have been compromised. Outside of Equifax specific code vulnerabilities or further database hardening what could Equifax have done to thwart these attackers?
Most detection and preventative countermeasures that could have minimized Equifax’s exposure employ some variation of behavior detection at one network layer. They then shunt suspect traffic to a sideband queue for further detailed human analysis. Today the marketing trend to attract Venture Capital investment is to call these behavior detection algorithms Artificial Intelligence or Machine Learning. How intelligent they are, and to what degree they learn is something for a future blog post. While at the NGINX Conference this week we saw several companies selling NGINX layer-7 (application layer) plugins which analyzed traffic prior to passing it to NGINX’s HTML code evaluation engine. These plugins receive the entire HTML request after the OS stack has assembled it from multiple network packets. They then do a rapid analysis the request to determine if it poses a threat. If not then the request is passed back to NGINX for the web application to respond to. Along the way, the plugin abstracts metadata from the request and in parallel, it shoots that up to their cloud service for further evaluation. This metadata is then compared against prior history and other real-time customer data from with similar services to extract new potential threat vectors. As they are detected rules are then pushed back down into the plugin that can be applied to future packets.
Everything discussed above is layer-7, the application layer, traffic analysis, and mitigation. What does layer-7 have to do with network micro-segmentation? Nothing, what’s mentioned above is the current prevailing wisdom instantiated in several solutions that are all the rage today. There are several problems with a layer-7 solution. First, it competes with your web application for host CPU cycles. Second, if the traffic is determined to be malicious you’ve already invested tens of thousands of CPU instructions, perhaps even in excess of one hundred thousand instructions to make this determination, all that computer time is lost once the message is dropped. Third, the attack is now deep inside your web server and whose to say the attacker hasn’t learned what he needed to move to a lower layer attack vector to evade detection. Layer-7 while convenient, easy to use, and even easier to understand is very inefficient.
So what is network micro-segmentation, and how does it fit in? Network segmentation is the act of altering the flow of traffic such that only what you want is permitted to pass. Imagine the factory that makes M&Ms. These days they use high-speed cameras and other analytics that look for deformed M&Ms and when they see one they steer it away from the packaging system. They are in fact segmenting the flow of M&Ms to ensure that only perfect candy-coated pieces ever make it into our mouths. The same is true for network traffic, segmentation is the process of only allowing network packets to flow into or out of a given device via a specific policy or set of policies. Micro-segmentation is doing that down to the application level. At Layer-3, the network layer, that means separating traffic by the source and destination network address and port, while also taking into account the protocol (this is known as “the five-tuple”, a set of five elements). When we focus on filtering traffic by network port we can say that we are doing application level filtering because ports are used to map network traffic to applications. When we also take into account the local IP address for filtering then we can also say we filter by the local container (ex. Docker) or Virtual Machine (VM) as these can often get their own local IP address. Both of these items together can really define a very specific network micro-segmentation strategy.
So now imagine a firewall inside a smart network interface card (NIC) that can filter both inbound and outbound packets using this network micro-segmentation. This is at layer-3, the Network, micro-segmentation within the smart NIC. When detection is moved into the NIC no x86 CPU cycles are consumed when evaluating the traffic, and no host resources are lost if the packet is deemed malicious and is dropped. Furthermore, if it is a malicious packet and it’s stopped by a firewall in the NIC then the threat has never entered the host CPU complex, and as such, the system’s integrity is preserved. Consider how this can improve an enterprise’s security as it scales out both with new servers, as well as adding containers and VMs. So how can this be done?
Solarflare has been shipping its 8000 line of smart NICs since June of 2016, and later this fall they will release a new firmware called ServerLock(TM). ServerLock is a first generation firewall in the smart NIC that is centrally managed. Every second it sends a summary of network flows through the NIC, in both directions, to a central ServerLock Manager system. This system then allows administrators to view these network flows graphically and easily turn them into security roles and policies that can then be deployed. Policies can then be deployed to a specific local IP address, a collection of addresses (think Docker containers or VMs) called an “IP Set”, a host or host groups. When deployed policies can be placed in Monitor or Enforce mode. Monitor Mode will allow all traffic to flow, but it will generate alerts for all traffic outside of all the defined policies for a local IP address. In Enforce mode, ONLY traffic conforming to the defined policies will be permitted. Traffic outside of those policies will generate an alert and be dropped. Once a network device begins to drop traffic on purpose we say that that device is segmenting the network. So in Enforce mode, ServerLock smart NICs will actively segment that server’s network by only passing traffic for supported applications, only those for which a policy exists. This applies to traffic in both directions, so for example, if an administrator walks into the data center, grabs a keyboard and elects to Secure Copy (SCP) a file from a database server to his workstation things will get interesting. If the ServerLock smart NIC in that database server doesn’t have a policy supporting SCP (port 22) his outbound request from that database server to his workstation will be dropped in the NIC. Likely unknown to him an alert will be generated on the central ServerLock Manager console calling out the application and both the database server and his workstation, and he’ll have some explaining to do.
ServerLock begins shipping this fall so while it’s too late for Equifax it’s not too late for the next Equifax. So how would this help moving forward? Simple, if every server, including web servers and database servers, has a ServerLock smart NIC then every second these servers would report their flow data to the central Solarflare ServerLock Manager for further analysis. Solarflare is working with Cloudwick to do real-time analysis of this layer-3 traffic so that Cloudwick can then proactively suggest in real time back to ServerLock administrators new roles and policies to proactively protect servers against all sorts of threats. More to come as this product is released.
9/11/17 Update – It was released over the weekend that Equifax is now pointing the blame at an Apache Struts module. The exact module has yet to be disclosed, but it could be any one of the following that has been previously addressed. On Saturday The Apache group replied pointing to other sources that believe it might have been caused by exploiting a remote code execution bug in their REST plugin as outlined in CVE-2017-9805. More to come.
9/12/17 Update – Alert Logic has the best analysis thus far.