Surviving Apache Struts CVE-2017-5638

If you’re a software engineer or work in tech, there’s a decent chance that your first thought after hearing about the Equifax breach was “oh my god, how incompetent do you have to be to get owned like that?” Don’t worry, I had the same reaction. After a few days of introspection and reviewing the evidence, I’ve come to the conclusion that Equifax made one uncommonly disastrous mistake: not upgrading Struts immediately after a remote-code-execution vulnerability was disclosed in it; everything else about the situation was exceptionally common. My best bet is 497 of the Fortune 500 couldn’t survive that mistake either. “Just upgrade” is both valuable advice, and not particularly interesting to explore. What if we wanted to design a system that could withstand this mistake?

Or better yet, what if we wanted to survive unknown mistakes? Equifax screwed up huge because there was a known RCE and they didn’t patch, however for many years before that there was an unknown RCE. But they, and many other companies, also screw up by not having an architecture which is resilient to such mistakes. If the person who discovered the Apache Struts vulnerability had been motivated differently companies could have been exploited without warning. What would we have to do to survive that situation?

I have no particular insider information on what Equifax’s environment looks like (or any of the Fortune 500’s for that matter), but let’s imagine they look like what a reasonably savvy startup using AWS has:

They’ve got a VPC in us-east-1, and some EC2 instances in it, perhaps even in a multiple availability zones. Each AZ’s EC2 instances are in a security group, and the only things with ingress to the SG is an ELB, in HTTP/HTTPS mode and a bastion server. EC2 instances have private IPs only, but can access the internet through a NAT gateway. The application uses an RDS PostgreSQL database, it has full disk encryption enabled, requires TLS for connections, and is accessible exclusively via our security groups.

This is a pretty well put together infrastructure for a startup. And if the EC2 instances were running a vulnerable copy of Struts, it would be game over for our startup. Once an attacker had RCE, they’d grab the DB credentials from disk, from an environment variable, or right out of memory if they had to. Pump the DB for data, and then ship it off network, they could even upload it to S3 if you want the outbound traffic to be clandestine. Pwned.

Now, perhaps there are things on the detection front that could be done to allow us to notice slightly more quickly than the amount of time it takes to ship 143 million people’s data off the network, but I’m not going to focus on monitoring or incident response. I want to explore just what it would take to make this foot hold of arbitrary code execution on our application web server useless.

There are two routes I see to making our system resilient. One involves some crypto, the other involves a distributed system (and a tiny bit of crypto). The ground rules we’ll use when analyzing our proposed solutions: 1) No degrading the functionality of our application, 2) We assume our attacker has an RCE against Struts but no other exploits, anything else they accomplish should be inherent in our design, 3) We’re trying to prevent stealing 143 million records, not stealing 14 records.

With those rules, let’s forge ahead!

The one with crypto

Immediately following a breach the battle cry of the uninformed is always the same, “just encrypt it!”. A little knowledge can be dangerous, serious security practitioners know that even the strongest encryption algorithm means nothing without a key management scheme that matches our threat model.

This fact should be evident from our problem description: we already had full disk encryption enabled on our RDS database and TLS for data in transit! And of course they do nothing in this attack scenario, full disk encryption protects against someone with access to the physical disk or the raw block device, not someone able to interact with the database; at that level the data has already been decrypted. Any encryption scheme where the keys for every single row are accessible from our web application will meet the same fate.

In short, we want to encrypt records under a key that is specific to that record, and which the application server does not have ambient access to, the application server must only have this key when the user is trying to access their own records. How about a key derived from the user’s password?

When the user logs in, we check their password against the stored hash in the database, and if it’s correct we compute a derived encryption key and store it in the session. Now, whenever we need to read or write a record from the database, we decrypt or encrypt it using this key. Now an attacker can steal records that they see in the application server itself, but can’t make off with our entire database.

However, as described, this puts serious constraints on the rest of our system; we can no longer have any backend systems which read or write the user’s data. Considering Equifax’s entire business is storing data about people without their direct involvement, this constraint is probably a no go, we need a scheme that permits backends, disconnected from the web application, to access users' data.

To accomplish this, we can introduce a second key. Now, instead of deriving a key from the password and encrypting our data with that, we’ll generate a random key for each record, and encrypt that key with a key derived from the password, and then store a second copy of that key, encrypted with Amazon’s KMS. That was probably hard to follow, let’s try some pseudo-code:

raw_key = os.urandom(32)
password_key = derive_key(password)

password_encrypted_key = encrypt_with_key(key=password_key, data=raw_key)
kms_encrypted_key = encrypt_with_kms(raw_key)

encrypted_data = encrypt_with_key(key=raw_key, data=data)

    INSERT INTO users (password_encrypted_key, kms_encrypted_key, encrypted_data)
    VALUES (?, ?, ?)
    (password_encrypted_key, kms_encrypted_key, encrypted_data)

Now our web application can read and write data for the logged in user by decrypting password_encrypted_key with the user’s password when they log in. Our backends can read and write the data for any user with kms_encrypted_key. To prevent the web application from reading arbitrary users' data with kms_encrypted_key we give it IAM permissions to encrypt with our KMS key, but not decrypt. Our backend services run under a different IAM user, which does have decrypt permissions.

This scheme works, but there are definitely disadvantages and caveats:

The one with a distributed system

Our first approach was based on addressing the problem that with access to the DB, you could read all the records. This approach is going to be based on removing the ability to read arbitrary records from the DB from the web server. To do that, we need to sever our application’s access to the SQL database.

We’ll introduce a service oriented architecture. Instead of our application directly executing SQL queries against the DB, we’ll have a service, on its own isolated machine, that exposes APIs like get_user_for_ssn and queries the DB for us. Instead of our web server having credentials for and a connection to the database, it now has a connection to this backend RPC server. This means the web server has no ability to execute SELECT * FROM users and walk off with the data.

Ooops, except the spaceĀ of SSNs is small enough that given our get_user_for_ssn method, one can just enumerate all possible SSNs and query for them. We need to somehow bind a request to the user on whos behalf it’s being made (we’ll call this the “principal”). Now our get_user_for_ssn RPC method takes (principal, ssn), and the backend can perform authorization checks that the principal is allowed to request that ssn.

What is a principal? It’s an assertion of the identity of the user who we’re making requests for. The simplest possible implementation would be just the user’s ID, except those are trivial to forge, so we need something that can’t just be ginned up out of thin air.

A more sophisticated implementation is principal = HMAC(K, "user-id=...") + "user-id=...", where K is a key that both the login page and our RPC server share. The login page generates a principal when a user logs in, and the RPC server validates the HMAC on requests, and then performs the authorization checks. These principals can’t just be generated out of thin air, you need K. If these look a lot like signed cookies to you, that’s because they are (and just like with a web application’s session, we could have also implemented this with a database shared between our login page and the RPC server).

One small snag, right now our login page is part of our main application server, so the box that our attacker is on has K. We can solve this by moving the login process – validating a user’s password and generating a principal – into its own service. Now the web application server has no ability to generate principals to authorize requests to the backend service. Problem solved!

Our attacker can, as always, steal principals for sessions that happen while they are watching, but this affects a small portion of users out of our 143 million, and is basically an unavoidable problem. We can timebox the impact by including a TTL in our principal that limits how long it can be used for, now our attacker can only steal data for the lifetime of the principal, not for as long as they’re in our network.

If our backend services are built on Struts, we’re still screwed. The same exploit which got onto our service could be used to get into the login or backend service, so we need to use a different technology stack. This is reasonable. Building applications for the public web involves a lot of complexity (HTML templates, Content-Type negotiation, localization, etc.). Internal services don’t require any of this functionality and therefore can makes a lot of simplifying assumptions, so an RPC framework like GRPC or Apache Thrift makes more sense. Even if we don’t use a different technology stack, this intermediate service gives us a valuable vantage point for additional monitoring; for example, while a public server can expect to receive many invalid requests everyday, an internal server is not, so aggressive logging of malformed requests gives us an opportunity to catch our attacker exploring the attack surface.


We’ve just designed two alternate architectures that make us resilient to RCE in our web application. A vulnerability like the one in Apache Struts which was Equifax’s downfall can no longer be used to steal all of our data. We’ve also seen that it’s difficult; both of these designs are objectively more complex than the one we started with, and require expertise in distributed systems and cryptography. That sort of talent is unfortunately rare. While this post focused on prevention, it’s important to recognize that detection and incident response are critical components of a complete security strategy.

If you want to explore more into these topics, I recommend reading up on Kerberos, Macaroons, and BeyondCorp. I hope that eventually we grow mature open source frameworks for building systems like these, in the same way Django and other web frameworks provided defenses against XSS, SQL injection, and CSRF out of the box. In the meantime, the next time you go to mock Equifax, ask yourself: could your systems survive an RCE on your web server? And if not, do you at least know when your dependencies have critical security vulnerabilities?