Software Dependencies Attacks

Identifying and Managing Software Dependencies Attacks

Over 90% of enterprise commercial applications contain open-source code, making up at least 70% of their overall code base. It shows how much open-source code is critical to software development and opens up the challenges in software supply chains. A great number of programming languages are built upon community-maintained public repositories, such as Java’s Central Repository, JavaScript’s npm, Python’s PyPI, and Ruby’s RubyGems. These may give a rich third-party component and package environment. On pulling in one element as a dependency, many others could be pulled in, producing a web of interdependencies that could potentially introduce vulnerabilities.

According to the National Institute of Standards and Technology (NIST), a software dependency is defined as “a relationship that exists between software components, where one component (the dependent) relies on the presence and functionality of another (the dependency) to operate correctly.” This definition underscores the interconnected nature of software systems and the inherent risks associated with integrating external code. Despite repositories improving their security measures, attackers continually find new ways to exploit these systems, making the detection of suspect dependencies more critical than ever.

In the context of supply-chain attacks, suspect dependencies refer to external components that could introduce malicious behavior, either intentionally or unintentionally, into a system. Attackers often target these dependencies to infiltrate the supply chain, aiming to compromise the integrity, security, or functionality of the end product. Given the increasing sophistication of cyber threats, cybersecurity experts must advance their detection capabilities for suspect software dependencies.

In the context of supply-chain attacks, suspect dependencies refer to any external components, libraries, or services that could introduce vulnerabilities or malicious code into a system or application. Attackers can target these dependencies to infiltrate the supply chain and compromise the final product's integrity, security, or functionality.

Common Attacks on Software Dependencies

Understanding the critical role of software dependencies is essential to exploring the specific threats they introduce, . The complexity of software supply chains, where one dependency can pull in many others, creates opportunities for various attacks. We’ll examine how attackers exploit these dependencies, from anomalous behaviors to sophisticated dependency confusion strategies. Cybersecurity experts can develop more effective defense mechanisms by dissecting these attack vectors. Let’s now look at the types of suspect dependency attacks that pose significant risks to software security.

Anomalous Dependencies 

Anomalous dependencies refer to situations where dependencies exhibit unexpected behavior or patterns, potentially indicating malicious intent.

Examples of anomalous dependencies might include:

  • Dependencies that suddenly change behavior (e.g., a benign library suddenly starts making network requests).
  • Dependencies with suspicious code modifications (e.g., unauthorized changes to critical functions).
  • Dependencies that diverge significantly from their usual usage patterns.

Dependency Confusion

Dependency Confusion, also known as Namespace Confusion, is a newly discovered logic flaw that affects the default way software development tools pull third-party packages from both public and private repositories. Let’s dive into

How It Works:

  • When developers build applications, they pull code packages and libraries from public repositories (such as npm for JavaScript, pip for Python, and gem for Ruby). However, they also use private components developed in-house, hosted in their company’s private repositories or local feeds.
  • Attackers Exploit the Gap: Here’s where the flaw comes in. Attackers exploit this flaw by uploading a software package with the same name as an internal package in a company’s private repository to a public package repository. By using higher version numbers for these malicious public packages, they take advantage of how dependency resolution works, causing development tools to prioritize the public package over the private one.  As a result, developers might unknowingly use the malicious version of the package from the public repository instead of the intended custom package from the private repository.

Known Vulnerabilities

Known vulnerabilities are security weaknesses everyone knows about. This makes them easier to exploit (attack) and fix (mitigate). 

Here are some common types:

  • SQL Injection: attackers trick applications into running malicious code.
  • Command Injection: attackers can take control of the system by injecting commands.
  • Cross-Site Scripting (XSS): attackers inject scripts into websites to steal data or hijack accounts.
  • Cross-Site Request Forgery (CSRF): attackers trick users into performing unintended actions on trusted websites.
  • Insecure Deserialization: attackers can inject malicious code when applications process data.
  • Broken Authentication: weak passwords or forgotten session management can lead to unauthorized access.

Suspicious Scripts

Suspicious scripts are code snippets or entire packages within software dependencies that exhibit potentially harmful behavior.

These scripts can compromise an application’s security and integrity by introducing vulnerabilities, unauthorized access, or other malicious actions.

Examples of Suspicious Scripts might include

  • Malware is hidden within legitimate-looking packages.
  • Supply chain attacks where attackers inject malicious code into dependencies.
  • Code that behaves unexpectedly, such as sending sensitive data to external servers.


Typosquatting is a malicious technique that involves registering domain names or package names that are very similar to legitimate ones, often differing by just a typo. The goal is to deceive users who make typographical errors when entering a URL or installing software packages, leading them to a malicious site or software instead of the intended legitimate one.

Developers may unknowingly introduce security risks by using these fake dependencies.

Examples of Typosquatting:

  • If the legitimate domain is, a typosquatter might register (replacing ‘l’ with ‘1’) or (missing ‘e’). Similarly, for a software package named lodash, a typosquatting package might be named lodahs.


Copycat Packages

Copycat packages involve creating malicious packages that imitate popular and legitimate ones. These packages often have similar names, descriptions, or even functionality to trick users into downloading and using them instead of the legitimate versions. The goal is usually to distribute malware, steal data, or compromise systems.

Examples of Copycat Packages:

  • A legitimate package named express might be imitated by a malicious package named  expressjs, expres, or expresjs. These copycat packages may appear legitimate and partially function like the original package but contain hidden malicious code.

For a deeper dive into the distinctions and implications of these tactics, check out our detailed blog post on Typosquatting vs. Copycat Packages: Understanding the Differences.

Mitigation Strategies and Advanced Tooling

Understanding the threats of suspect software dependencies is only the first step in securing your supply chain. Organizations need robust tools and technologies to effectively combat these vulnerabilities, providing comprehensive visibility, proactive risk management, and early threat detection. Xygeni offers a suite of advanced capabilities designed to address these needs, ensuring that your software remains secure and compliant.

Advanced Detection of Suspect Dependencies

Detecting suspect dependencies is paramount in software security. Xygeni provides a comprehensive range of detectors tailored to the unique characteristics of various software ecosystems, ensuring precise and extensive coverage. By focusing on advanced detection techniques, Xygeni helps identify and mitigate potential threats before they can cause significant damage.

Overview of Supported Open Source Suspect Dependency Detectors:

Xygeni’s detectors are designed to cater to various programming languages and package managers, among others, including:

  • Maven: Comprehensive detection for Java projects.
  • NPM: Advanced monitoring for JavaScript projects.
  • NuGet: Precise identification for .NET projects.
  • PyPI: Robust scanning for Python projects.
  • DotNet: Specialized detection for .NET applications.
  • Java: Tailored solutions for Java-based environments.
  • Python: In-depth analysis for Python dependencies.

Types of Suspect Dependency Detectors:

  • Anomalous Dependencies: This class identifies dependencies exhibiting unusual or unexpected behavior within the project context, which may signal a security concern. It helps detect potentially compromised or malicious updates to commonly used libraries.
  • Dependency Confusion: This feature detects cases where internal package names may be confused with similarly named packages from public repositories, potentially leading to security breaches. It ensures that developers do not inadvertently pull in malicious versions of internal packages.
  • Known Vulnerabilities: Flags dependencies that contain recognized security vulnerabilities, allowing organizations to address these issues promptly. By leveraging databases like NVD and CVE, Xygeni ensures up-to-date vulnerability detection.
  • Malware: Looks for dependencies known to contain malware, providing critical security alerts to prevent potential harm. This includes scanning for embedded malicious code that could compromise the integrity of the software.
  • Suspicious Scripts: Monitors for scripts within dependencies that might perform unauthorized or harmful actions. This proactive monitoring helps catch potential threats before they can execute harmful activities.
  • Typosquatting: Aims to catch potentially malicious typosquatting attempts where package names are slightly altered to trick users into installing them. By detecting these deceptive tactics, Xygeni prevents the introduction of malicious packages.
  • Unscoped Internal Components: A special detector for NPM that identifies unscoped internal components, which might be at risk of being publicly exposed or confused with external packages. This feature is crucial for maintaining the integrity of private packages.


The complexities of the software supply chain, coupled with the pervasive use of open-source components, present significant security challenges. Attackers continually evolve their tactics, exploiting dependencies to infiltrate and compromise systems. However, by leveraging advanced detection and mitigation tools like those offered by Xygeni, organizations can stay ahead of these threats. Comprehensive component identification, strategic risk prioritization, early malware detection, and continuous monitoring are critical to maintaining a secure software environment. By implementing these strategies, cybersecurity professionals can enhance the resilience and integrity of their software supply chains, ensuring robust defense against the ever-evolving landscape of cyber threats.

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