The Two Types of Code Vulnerabilities

Spell checkers, grammar checkers and predictive keyboards all help reduce errors in written communication, but in a creative medium where writers need to innovate new expressions, nothing can eliminate all errors. The same is true in the code we write.

Of course, your code is probably perfect but, on average, all code has bugs. Some of those bugs are benign. Some will cause unexpected early termination. A few will cause runaway resource consumption. And some bugs lead to security vulnerabilities that let users do things that are unexpected and sometimes undesirable.

Those security bugs are behavioral vulnerabilities because they depend on how the code behaves when executed. Another type is an intelligence vulnerability, something that’s part of the correct behavior of the code, but which gives away details about how to exploit a system. These are the two main types of code vulnerabilities; both come with risks.

Behavioral Vulnerabilities at Runtime

Tl;dr: How the code works and how attackers can leverage what it does to their advantage.

This covers a broad range of problems, but all of them are a result of the behavior of the code leading to undesirable outcomes in specific circumstances. Most of these are unintentional. An unescaped user input sent to a database can lead to a SQL injection vulnerability or an unsanitized string sent to a browser could create a cross-site scripting (XSS) vulnerability. Some behaviors are intended, but have unexpected consequences; the Log4j string substitution behavior that allowed it to output environment variables or read data from external resources was intentional, but the malicious usage we now call Log4Shell was not. In another recent example, a combination of behaviors could be exploited to gain root privileges on Linux desktop computers.

Intelligence Vulnerabilities in the Content

Tl;dr: What the content of the code tells an attacker about the system and how to access it. 

In the 1950s, the phone company—there was pretty much just one at the time—began implementing completely automated dialing that didn’t require a human operator at the switching station to connect the call. The details were first published in a technical journal in November 1954 (see pages 1209 and 1309 here), and the information gleaned from that led to the creation of “blue boxes” that used insider knowledge to allow outsiders to control telephone equipment and make free long-distance calls.

That’s hardly the first example of insider knowledge being leveraged for outside advantage, but its role at the heart of computer hacking and the origins of the PC (the ‘hack’ inspired Wozniak and Jobs) is so legendary that it demands recognition here.

The phone system’s intelligence vulnerabilities were published in the company’s technical manuals—manuals that many thought were only for internal use, and nobody realized were available to untrusted readers. Today, though, intelligence vulnerabilities are in our code.

Secrets abound in code, along with the details about which systems those secrets connect to. These secrets were once difficult to manage and putting them in code was the only practical choice. Now we have tools like Hashicorp Vault, as one example, and secure keystores in almost every cloud, but the legacy and security debt of all those secrets in legacy code are a goldmine for attackers like Lapsus$ and others who specifically target code.

Security Is a Process, not a Product

There’s a lot hidden under the surface in the two categories outlined here. Each of us can name many types of behavioral vulnerabilities—many more than the different types of intelligence vulnerabilities—but the two classes of vulnerabilities represent relatively equal risk. Recent events have demonstrated how an attacker with inside knowledge gleaned from code—and with passwords and keys taken from it—can move quickly despite firewalls and other access controls.

Identifying secrets and other intelligence risks in code is relatively easy, but few teams do it because detection is the easy part. Integrating detection in developer workflows so they can take action in-context while working on and reviewing code is the most effective way to eliminate creating new security debt. And tools that give developers the information they need to understand the overall picture of intelligence risks in their code, the areas of highest risk—and that point them to actionable issues they can fix immediately to improve their code health—are necessary to help teams mitigate historical risks, ideally by rotating any secrets found.

Tools exist that can automate that process and help teams to eliminate more than half the risk in their code—but the first step is recognizing where that half resides.