Name confusion attacks

Name confusion attacks are a form of supply chain attack where malicious actors create components with names resembling legitimate open-source or system components. These attacks exploit naming similarities to deceive developers into downloading and using these malicious components. Common tactics include:

• Typo-squatting: Mimicking popular package names with slight variations.
• Brand-jacking: Suggesting reputable authorship.
• Pattern manipulation: Leveraging naming conventions across ecosystems.

By introducing such components into the software supply chain, attackers can execute malicious code, risking the confidentiality, integrity, and availability of systems and data.

In this lesson, you will learn how name confusion attacks work and the security implications they entail. You'll explore real-world scenarios demonstrating the risks and discover strategies to safeguard your software supply chain.

Let’s dissect the scenario above and examine the mechanics of the attack.

Name confusion attacks rely on deceptive tactics to compromise software supply chains, and their mechanisms can be better understood by examining the scenario involving Sarah, a junior developer. The attacker created a malicious package named "logger," which mimicked the legitimate "loggerr" library. This package replicated the functionality of the legitimate library, ensuring that it appeared functional during Sarah's testing. However, the attacker embedded malicious code within the package to exfiltrate sensitive information like API keys and system logs.

The exploitation of trust is central to name confusion attacks. In this case, the attacker leveraged typo-squatting, counting on a developer's accidental mistyping or misunderstanding of the legitimate package name. Once Sarah included the malicious package in her project and deployed the application, the hidden script within the package was activated. It secretly collected environment variables and transmitted them to an external server controlled by the attacker. During initial testing, the malicious activity remained concealed, as the package appeared to function as intended.

The consequences of such an attack became evident when the organization’s monitoring tools detected irregular API usage. This led the security team to trace the anomaly back to the dependency list, ultimately revealing the presence of the malicious package. The technical details of this type of attack can vary across programming languages, but the underlying approach remains consistent: malicious actors exploit trust and naming conventions to introduce harmful code into the supply chain.

For example, in JavaScript, a package may appear to log user activity as expected while secretly including a script that sends sensitive data to an attacker-controlled server. In Python, the malicious payload could leverage popular libraries to blend in while exfiltrating sensitive environment variables. PHP, C#, Go, and Java implementations all similarly demonstrate how attackers can exploit the implicit trust in dependency ecosystems to inject harmful behaviors, such as sending sensitive server or environment data to remote endpoints.

The code snippet below imports the malicious package logger, which appears to provide legitimate logging functionality. Sarah’s code invokes the logger.info method to log a message, which performs as expected during testing. However, the logger package contains a hidden script that collects environment variables and sends them to a malicious server via an HTTP POST request. The vulnerability lies in executing untrusted code from an external source, allowing the malicious payload to steal sensitive information without detection.

The Python code snippet below demonstrates the use of the logger package, which seemingly provides standard logging capabilities. When the info method is called, it logs the provided message. However, embedded within the package is a hidden function, exfiltrate, which accesses environment variables and sends them to an attacker-controlled server using the requests library. The vulnerability is caused by relying on an unverified package, which permits the execution of harmful code in a trusted environment.

In the PHP code snippet below, the logger.php library is included to perform logging tasks. Sarah uses this package to record session information, which functions as expected. However, the malicious package also contains a script that serializes sensitive server data and transmits it to a malicious endpoint using a GET request. The vulnerability arises because the third-party library’s behavior is not validated, allowing hidden malicious functionality to operate.

The code snippet below in C# defines a Logger class with a public Info method for logging messages. While the method logs messages as intended, it also calls a private Exfiltrate method, which collects environment variables and sends them to a malicious server via an HTTP POST request. The vulnerability stems from the lack of inspection of the library’s internal code, which enables attackers to inject malicious functionality into otherwise standard operations.

The Go code snippet below shows an Info function that logs a message and then calls a hidden exfiltrate function. This hidden function retrieves environment variables and sends them to a malicious server using an HTTP POST request. The vulnerability lies in the package’s unverified inclusion, which allows harmful code to execute without detection. The static nature of Go code makes it challenging to identify such malicious behavior at runtime.

The Java code snippet below includes a Logger class with an info method to log messages. While the info method functions as expected, it also calls a private exfiltrate method, which retrieves system properties and transmits them to a remote server using an HTTP POST request. The vulnerability is rooted in the unverified inclusion of the library, which executes hidden malicious code under the guise of legitimate functionality.

This vulnerable code dynamically loads a logging library (logger.so) without verifying its authenticity or integrity. If an attacker replaces logger.so with a malicious version, it could execute harmful code, such as exfiltrating sensitive information or compromising system security. The lack of any integrity checks or verification mechanisms makes this code susceptible to supply chain attacks.

Name confusion attacks can have severe consequences for organizations and their systems. By introducing malicious code into the software supply chain, attackers can compromise the confidentiality, integrity, and availability of critical systems and data. For instance, sensitive information such as API keys, passwords, or environment variables can be exfiltrated to unauthorized third parties. These attacks can also lead to further exploitation, such as privilege escalation or lateral movement within an organization’s network.

In addition to direct data theft, name confusion attacks can disrupt operations by introducing vulnerabilities that enable denial-of-service attacks or other forms of system sabotage. Furthermore, these attacks damage the reputation of affected organizations, erode trust in the ecosystem, and impose significant recovery costs, including time spent investigating and remediating the issue.

To protect against name confusion attacks, developers and organizations must adopt a proactive and vigilant approach to dependency management. The first step is implementing robust dependency validation processes. Before installing or using any package, review its source code, metadata, and maintainers. Examine its activity and release history for signs of legitimacy, such as regular updates and a meaningful number of contributors.

Leveraging secure package repositories and tools is another essential measure. For example, repositories offering signature verification or provenance tracking can help ensure the authenticity of packages. Tools that analyze and flag risky dependencies can provide an additional layer of protection. Dependency locking or pinning mechanisms can further safeguard projects by ensuring consistent and predictable versions across environments.

Finally, ongoing monitoring and auditing of dependencies in use are critical. Automated tools can help track changes to installed dependencies and detect when a dependency starts exhibiting suspicious behavior. Regularly updating dependencies to the latest secure versions and promptly removing unused packages from your project also reduce the attack surface.

The mitigated JavaScript code explicitly imports the legitimate loggerr package, ensuring it is sourced from a verified repository. Additionally, the code checks for unexpected properties, such as the exfiltrationScript, to detect potentially malicious functionality. This differs from the vulnerable version, which blindly executes code from an unverified dependency. By performing validation and using a known-good package, the mitigated code ensures that no unauthorized scripts can execute, securing the application.

The Python example now verifies the integrity of the package by comparing its hash with a known secure value. This step ensures the package has not been tampered with or replaced by a malicious version. The vulnerable version, in contrast, directly used an unverified package, allowing malicious payloads to execute. By incorporating checksum validation, the mitigated code prevents attackers from substituting a malicious package while maintaining the desired functionality.

The mitigated PHP code leverages Composer, a trusted dependency manager, and performs an integrity check by hashing the library file and comparing it against a trusted value. This ensures that the included package has not been modified or compromised. In the vulnerable version, the package was directly included without verification, exposing the application to malicious functionality. By introducing these validation measures, the mitigated code ensures only safe, unaltered packages are used.

In the mitigated C# code, the integrity of the loggerr.dll library is verified by calculating its hash and comparing it to a known good value before using the library. This is a stark contrast to the vulnerable version, which directly invoked potentially malicious functionality embedded within the Logger class. By verifying the package’s authenticity, the mitigated version prevents attackers from substituting the legitimate library with a malicious one, ensuring only secure code is executed.

The mitigated Go example reads the dependency file and computes its SHA-256 hash, comparing it to an expected value. This additional step ensures that the dependency has not been tampered with before it is loaded and used. The vulnerable version omitted such checks, allowing a malicious library to execute harmful code. By implementing this verification process, the mitigated version secures the application from supply chain attacks, maintaining the integrity of the software.

The Java code now includes a verification step to compute and compare the hash of the loggerr.jar file against a trusted value before it is used. This differs from the vulnerable version, which directly executed code from the package without inspecting its authenticity. By adding this integrity check, the mitigated code ensures that only trusted, untampered libraries are loaded, protecting against malicious payloads embedded in spoofed or compromised dependencies.

The mitigated code differs from the vulnerable version by adding a verification step to ensure the integrity of the logger.so file. Before the file is used, its SHA-256 hash is calculated and compared against a known secure hash value. If the hashes do not match, the code halts execution and logs an error, preventing the use of potentially malicious files.

By introducing integrity checks, this version ensures that only trusted and unaltered libraries are loaded. It effectively guards against attackers replacing the logger.so file with a malicious version, thus securing the application against supply chain attacks.

To further enhance your understanding of name confusion attacks and secure software supply chains, explore the following resources:

• Snyk Supply Chain Security Solution
• Secure Supply Chain Consumption Framework (S2C2F): A comprehensive guide to secure software supply chain practices.
• MITRE ATT&CK T1195.001 – A detailed explanation of compromising software dependencies and development tools.