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Vulnerabilities/Threats

4 MIN READ

Commentary



ON SHAKY GROUND: WHY DEPENDENCIES WILL BE YOUR DOWNFALL

There's never enough time or staff to scan code repositories. To avoid
dependency confusion attacks, use automated CI/CD tools to make fixes in
hard-to-manage software dependencies.
Nicholas Lang
Threat Research Engineer, Sysdig
March 02, 2023
Source: guy Oliver via Alamy Stock Photo
PDF


Software dependencies, or a piece of software that an application requires to
function, are notoriously difficult to manage and constitute a major software
supply chain risk. If you're not aware of what's in your software supply chain,
an upstream vulnerability in one of your dependencies can be fatal.



A simple React-based Web application can have upward of 1,700 transitive NodeJS
"npm" dependencies, and after a few months "npm audit" will reveal that a
relatively large number of those dependencies have security vulnerabilities. The
case is similar for Python, Rust, and every other programming language with a
package manager.

I like to think of dependencies as decaying fruit in the unrefrigerated section
of the code grocer, especially npm packages, which are often written by unpaid
developers who have little motivation to put in more than the bare minimum of
effort. They're often written for personal use and they're open sourced by
chance, not by choice. They're not written to last.

NodeJS dependencies, highly vulnerable, live in production! I took this from an
actual, production React-Native application found on GitHub.

Recently (as of the writing of this article), PyTorch, one of the top two most
popular machine learning libraries for Python, was compromised for five days via
its dependency "torchitron." The attacker was able to collect system information
and steal 1,000 files from each affected user's home directory. In 2021,
Log4j was famously compromised and because it was bundled into basically every
Web-facing Java project, DevSecOps teams were under a lot of pressure to
identify where exactly they were vulnerable.


Too many dependencies to reliably keep track of, even if most are "mostly"
secure.

Even 20 intentional dependencies are too many for a development team to
continually audit, much less 2,000 transitive ones.




WHAT'S BEEN DONE SO FAR?

Not all hope is lost. For known (reported and accepted) vulnerabilities, tools
exist, such as pip-audit, which scans a developer's Python working environment
for vulnerabilities. Npm-audit does the same for nodeJS packages. Similar tools
exist for every major programming language and, in fact, Google recently
released OSV-Scanner, which attempts to be a Swiss Army knife for software
dependency vulnerabilities. Whether developers are encouraged (or forced) to run
these audits regularly is beyond the scope of this analysis, as is whether they
actually take action to remediate these known vulnerabilities.

However, luckily for all of us, automated CI/CD tools like Dependabot exist to
make these fixes as painless as possible. These tools will continually scan your
code repositories for out-of-date packages and automatically submit a pull
request (PR) to fix them. Searching for "dependabot[bot]" or "renovate[bot]" on
GitHub and filtering to active PRs yields millions of results! However, 3
million dependency fixes versus hundreds of millions of active PRs at any given
time is an impossible quantification to attempt to make outside of an in-depth
analysis.


STATE OF THE ART ISN'T QUITE GOOD ENOUGH

You need to keep in mind that these auditing tools do nothing to protect
developers against zero-day exploits or N-days that were reported but have yet
to be officially accepted. In the case of the PyTorch vulnerability previously
mentioned, none of the auditing tools would have caught this class of dependency
vulnerability, because there was no traditional "software vulnerability" being
exploited. These "dependency confusion" attacks take advantage of the fact that
package managers look to their "default" repository before checking other
repositories that dependencies may select for their dependencies. In the case of
PyTorch, the "torchitron" dependency was self-hosted by the PyTorch Foundation.
When the attacker uploaded their version to PyPI, it took precedence over the
official version.



How long will this continue? Forever. If there is value in exploiting these
vulnerabilities, attackers will continue to take advantage of them. Luckily, in
the case of PyTorch, "only" data exfiltration took place. Future victims might
not be so lucky, however. A sophisticated attacker only needs one successful
attempt at access to remain persistent in a victim's system (and network).


WHAT DOES THIS MEAN FOR ME?

Did you install your packages from the command line? If so, did you type them in
properly? Now that you've installed your dependencies "correctly," did you
verify that the code for each dependency does exactly what you think it does?
Did you verify that each dependency was installed from the expected package
repository? Did you ….

Probably not, and that's OK! It's inhumane to expect developers to do this for
every single dependency. The best bet for software developers, software
companies, and even individual tinkerers is to have some form of runtime
protection/detection. Luckily for us all, there are detection and response tools
that have relatively recently been created which are now part of a healthy and
competitive ecosystem! Many of them, like Falco, Sysdig Open Source, and
Osquery, even have free and open source components. Most even come with a
default set of rules/protections.

Keep up with the latest cybersecurity threats, newly-discovered vulnerabilities,
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