Software Supply Chain Attacks

Supply chain attacks—compromising an organization via insecure components in its software supply chain—are a growing concern for organizations. Throughout the past three years, an increasing number of open source software package repositories have been found to contain malware, making it clear that all installation and update pathways for software and library code must have security controls applied to prevent and mitigate supply chain attacks.

Growing Risks of Open Source Software to the Business

Digital transformation has made every company a software company. As a result, open source software has grown 75% over the past two years. Despite this growth, few companies accurately track the components used in their code. Most lack the policies, processes and tools to keep up with the choices made by their developers, which introduces risk. In fact, 60% of the codebases audited in 2018 contained at least one vulnerability.

Bad actors have turned to exploiting these weaknesses to compromise organizations. If production code is infected with malware or vulnerabilities, inadvertently sourced from the repository, it may contaminate all organizations that come in contact with it—whether by using the code already in their SDLC or by launching presumed trusted applications from third parties who failed to validate their own code.

Attackers are also shifting their techniques to mask malware among large packaged objects using obscure formats we refer to as destructive objects. They infiltrate at various points in the development processes opportunistically, knowing they can infiltrate systems outside a managed operational environment. They take advantage of third-party dependencies and use payloads that can sit and wait until an unsuspecting developer implements their software. Hence, software supply chain attacks are difficult to detect, and often achieve dwell-time of hundreds of days.

Assessing Supply Chain Surface and Sources

In order to assess weaknesses and identify what warrants continuous monitoring, we need to understand both the surface and sources.

When evaluating surface risks, consider the following questions regarding the development environment:

What technologies, repositories and packages are in use?

  • Inventory technologies used in development: Python, Ruby, JavaScript, Node.js, .NET, etc. This can also refer to operating systems.
  • Third-party repositories of code: PyPI, RubyGems, \NPM, NuGet, PEAR or PECL, and OS repositories such as DebianRepository, CentOS, Ubuntu, AUR and more.
  • Packages: There can be multiple packages from a single repository. How often are packages updated? How often are new packages added?

Are there update procedures in place?

  • What are the dependencies for every package? How many other packages are pulled from a single package update, and how often are they updated and/or reviewed?

Who’s managing the verification processes across all these dependencies? What update procedures are in place if there is a personnel change?

Once these questions have been answered, they should be addressed regularly. The process must be iterative to be successful.

When considering attack sources, it’s important to understand the characteristics of repositories teams draw from in order to understand potential areas of weakness. Third-party repositories rarely infect a big project as they are typically found elsewhere (e.g. GitHub) and have thorough code reviews. Official repositories, unofficial mirrors and internal code repositories represent increasing risk potential.

Random malicious packages, typosquatting, bad code review and phishing developer credentials to hijack accounts are all less technical ploys used by attackers, but equally dangerous.

The Software Supply Chain Challenge

The size, breadth of formats, trusted third-party and open source risks, and misused certificates have made it nearly impossible to stop malware-infected files and objects with today’s signature based, sandbox and vulnerability-based solutions.

Threats evade dynamic analysis remaining unknown to defenders or lacking details when detected. The explicitness of signature blacklists misses new or never-seen-before code, and sandboxes or dynamic analysis solutions are resource intensive and unable to scale to the breadth and volumes of formats required. Without a way to inspect large or cross platform archives, installers and source code, it’s impossible to extract indicators of compromise. These destructive objects can also impact SOC triage, response and continuous improvement.

Rising to the Challenge

How can you address these risks, not only in the software development life cycle (SDLC), but as SOCs address vulnerabilities that may come through other applications and repositories? How do you evaluate and inspect wrappers, digital certificates, executables, scripts, files and libraries, resources, documents and icons?

It’s vital to establish security practices within SDLC, from training developers on secure coding practices using open source libraries to factoring in detection capabilities including static analysis, dynamic analysis, software composition analysis and manual penetration testing. Implementing a secure SDLC process ensures that the development effort is protected by these activities, augmenting code reviews and infrastructure analysis.

Teams can extend security controls into the SDLC with technology that provides deep visibility into all aspects of the supply chain, from open source dependencies through continuous integration and delivery of packaged applications. Capabilities that provide greater application security across code development and deployment activities include:

  • File reputation identifies and classifies “known bad” and “known good” files by applying a file’s unique hash against a database of goodware and malware.
  • Automated static analysis detects and classifies unknown malware in code repositories by deobfuscating and decomposing objects and files without executing, and extracting threat indicators.
  • Dynamic analysis executes files in a safe environment and captures the behaviors and results of malware, allowing the security team to understand what is going to happen.
  • Software composition analysis analyzes applications to detect vulnerabilities, dependencies and licensing requirements in software.
  • Threat intelligence is served up at various stages of the analysis cycle, and represents evidence-based knowledge including context, mechanisms, indicators, implications and action-oriented advice about existing or emerging threats.

By incorporating these technologies into the SDLC, development teams can obtain continuous validation across the software supply chain. Further automation, implementing new security guidelines and advancing the activities within the existing framework will allow the enterprise to progress its security measures and close gaps in the SDLC and supply chain operations.

At the same time, it’s important to recognize that threats can still make their way into the software supply chain, and it’s equally important that the SOC remain vigilant about maintaining a clear triage and escalation process. Continuous monitoring of software repositories should be an integral part of good hygiene processes, and incorporated into best practices applied to operational infrastructure, including the SOC. The SOC serves as a centralized management infrastructure to respond, contain and remediate attacks. This includes aggregating and consolidating threat data, building a case for managing a response which can include actions to formulate triage and incident response plans, and moving beyond SIEM.

[“source=devops”]