The four rules of data safety

I tried to translate the four rules of gun safety to the task of data validation in order to formulate a behavioural framework of improved input safety.

firefly-gunOne of the most dangerous objects to handle is guns. No wonder there are strict and understandable rules how to handle them safely. The Canadians have The Four Firearm ACTS, but for this blog entry, I will cite the Four Rules stated by Captain Ira L. Reeves right before the first world war and restated by Colonel Jeff Cooper:

  1. All guns are always loaded
  2. Never let the muzzle (the business end of a gun) cover anything you are not willing to destroy
  3. Keep you finger off the trigger until your sights are on the target
  4. Be sure of your target and what is beyond it

Even if you accidentally break one rule (for example, rule 3 is often blatantly disobeyed on television), there are still enough precautions in place to keep you (and everybody around you) relatively safe. The rules are meant to instill a certain amount of respect for the gun into the owner so that offloading of responsibility isn’t possible any more, as in the line “I know this gun is unloaded, so it’s probably mighty fun to point it at somebody”.

The guns of software development

In software development, the most dangerous objects we can handle is user-created data or inputs. To mitigate the risks we take when we accept inputs from our users (and most software would be pretty useless otherwise), we have the concept of validation: Before anything other may happen with the data, it needs to be validated, meaning “proved to be free of danger”. Improper input validation is so prevalent in software development that it has its own CWE number (CWE-20) and ranked number 1 on the Top 25 list of “most dangerous programming errors”.

There are some concepts ready to help us tackle this task. The most promising is the Taint checking that treats all input as dangerous and therefore unworthy of further usage unless proven otherwise. Taint checking reminds you of validation, but not how to validate and isn’t available in most programming languages, unfortunately. What we need is a language agnostic set of rules that shape our behaviour in a way that we can’t make the most common mistakes of validation. It seems that gun owners have tried the same and succeeded. So Let’s formulate our Four Rules of data safety, inspired by the gun rules.

Our four rules

  1. All data always contains malicious aspects
  2. Never accept input for modules you cannot afford to have hacked
  3. Leave input data alone until you actually want to use it
  4. Be sure what aspects to validate and how to do it properly

This is just a starting ground for discussion, let’s call it the first version of the Four Rules. Here is my motivation for each rule:

All data always contains malicious aspects

Most users of most systems are in no way harmful. But if they attempt to harm a system, it better stands prepared. Problem is, even with a thorough validation in your current context, there is always the possibility that your attacker plays a rail shot, entering the system here, but causing damage somewhere else. A good example of this practice were images with Javascript code in their metadata. An adequate validation of uploaded images would check for a valid image format, but don’t mind the “dead content” in the meta tags. A browser would later discover the Javascript and execute it – a classic cross-site scripting attack. Never treat any data as fully validated. If you know that your particular code is vulnerable to a specific threat, let’s say a zero value in a variable used as a divisor, validate once more against this threat. This practice is also contained in the idea of Defensive programming.

Never accept input for modules you cannot afford to have hacked

Behind this rule lies a simple truth: Everything that can be hacked will be hacked, given enough time. The only protection against any hack is no access at all (like in “some air between network cable and network card”). If for example you run a certificate authority and absolutely cannot risk losing your secret private key, the machine using this key must not be connected to any network. If your database contains data much too valuable to be “stolen”, the database shouldn’t be accessible directly – and all access need to be validated beforehand. You need to think about a pragmatic compromise for your scenario when following this rule, but you’ve always been warned.

Leave input data alone until you actually want to use it

This was the most difficult rule for me to decide on. The rationale is that even the slightest bit of validation is actually usage of the input. Given enough knowledge about the validation, an attacker could possibly attack the system by abusing weaknesses in the validation itself (see rule 1 for inspiration). Any contact with input data is dangerous, even when it happens with the best intentions. The downside is that you won’t have a stronghold security architecture, where a mighty wall separates the danger zone from friendly territory (or tainted from cleaned data). Remember that even persisting the input data is using it in some form.

Be sure what aspects to validate and how to do it properly

If the time has come to use the input and to validate it right before, you need to think deep about the threats you want to eliminate. Just like with guns, where real bullets (as opposed by “television bullets”) won’t stop at the shooter’s convenience, your validation has consequences beyond an immediate gain of security. A common error is the rushed countermeasure, when you think of a specific threat and immediately try to abolish it. Take your time and think deep! For example, if your users can enter way too high values, it’s of no use to constrain the input field length, because direct web requests and notations like “1E9” are still possible. But converting an input string to a number to check its value might not be the smartest idea, too. Not long ago, you could crash nearly every application by entering a certain “number of death”. Following this rule requires experience and lots of reading, learning and thinking. And even then, there’s always somebody smarter than you, so ultimately, you should plan your system under the impression of rule 2.

As stated, this is just a starting point to try to formulate rules for data validation that provide a behaviour framework that avoids the most common mistakes and pitfalls. I’m highly interested to hear your thoughts about this topic. Please leave a comment below – but be gentle with the comment validation algorithm.

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