Syft Misses Python Dependencies: A Deep Dive & Fix

Nov 24, 2025 by Alex Johnson 51 views

Syft's Incomplete Dependency Graph: Missing Requires-Dist Relationships in Python Packages

Introduction: Unveiling the Issue with Syft and Python Dependencies

In the realm of software development, Software Bill of Materials (SBOMs) have become increasingly crucial for managing software supply chain security. An SBOM provides a comprehensive list of components and dependencies within a software application, enabling organizations to identify and mitigate potential vulnerabilities. Syft, a popular open-source tool, aims to generate SBOMs for various software artifacts, including Python packages. However, a critical issue has been identified: Syft's dependency graph sometimes fails to include all Requires-Dist relationships defined in a package's METADATA file. This omission can lead to significant problems, particularly in vulnerability detection, as missing dependencies may result in overlooked security risks. This article delves into this issue, exploring the details of the problem, its impact, and potential solutions. We'll examine the specific scenario where Syft (version 1.38.0) doesn't capture all dependencies for Python packages, using Flask as a prime example. Understanding this issue is crucial for anyone relying on Syft for accurate SBOM generation and vulnerability management in Python projects. The ramifications of incomplete dependency information can be far-reaching, affecting not only security but also compliance and overall software supply chain visibility. Therefore, a thorough investigation and a clear understanding of the root cause are essential steps toward ensuring the reliability of SBOMs generated by Syft.

The Problem: Syft's Limited Dependency Mapping

The core of the issue lies in Syft's handling of Python package dependencies. Specifically, when Syft generates a CycloneDX SBOM for Python packages, it doesn't always capture all the dependencies declared in the package's METADATA file. The METADATA file is a crucial component of a Python package, as it contains essential information about the package, including its dependencies. These dependencies are typically listed using the Requires-Dist field, which specifies the other packages that the current package relies on to function correctly. However, Syft sometimes misses these Requires-Dist relationships, leading to an incomplete dependency graph in the generated SBOM. For instance, consider the example of Flask 1.1.2. When Syft analyzes this package, it might only report two dependencies, such as click and itsdangerous, while omitting others like Jinja2 and Werkzeug. This is despite the fact that all four dependencies are explicitly declared in Flask's METADATA file and are indeed installed in the environment. This discrepancy between the declared dependencies and the dependencies captured by Syft can have serious consequences, especially in the context of vulnerability management. If a dependency is missing from the SBOM, any vulnerabilities associated with that dependency will not be identified, potentially leaving the software application vulnerable to attacks. The impact of this issue is further amplified by the fact that many tools and processes rely on SBOMs for vulnerability scanning and compliance checks. Therefore, ensuring the completeness and accuracy of SBOMs is of paramount importance. The example provided in the initial report highlights this problem effectively, demonstrating how Syft's incomplete dependency mapping can lead to significant gaps in vulnerability detection.

A Concrete Example: Flask 1.1.2 and Missing Dependencies

To illustrate the problem more clearly, let's delve into the specific case of Flask 1.1.2. As mentioned earlier, Syft might only identify click and itsdangerous as dependencies for Flask 1.1.2, while omitting Jinja2 and Werkzeug. This omission is problematic because Flask relies heavily on these two libraries for its functionality. Jinja2 is a powerful templating engine that Flask uses to render dynamic web pages, while Werkzeug provides essential utilities for handling HTTP requests and responses. The METADATA file for Flask 1.1.2 clearly lists all four dependencies:

Requires-Dist: Werkzeug (>=0.15)
Requires-Dist: Jinja2 (>=2.10.1)
Requires-Dist: itsdangerous (>=0.24)
Requires-Dist: click (>=5.1)

However, when Syft generates an SBOM for Flask 1.1.2, it might produce a dependency graph similar to the following:

"dependencies": [
 {
 "ref": "pkg:pypi/flask@1.1.2?package-id=7c9599e2d82779f5",
 "dependsOn": [
 "pkg:pypi/click@8.3.1?package-id=f5afd6ba55e9b798",
 "pkg:pypi/itsdangerous@2.2.0?package-id=1b2e20a8cd0be3ca"
 ]
 },
 {
 "ref": "pkg:pypi/importlib-metadata@8.0.0?package-id=587e5d030197d8be"
 }
]

As you can see, only click and itsdangerous are listed as dependencies for Flask, while Jinja2 and Werkzeug are missing. This incomplete dependency graph can have serious implications for vulnerability detection. For instance, if there are any known vulnerabilities in Jinja2 or Werkzeug, they will not be associated with Flask in SBOM-based scans, potentially leaving applications vulnerable to exploitation. This example highlights the critical need for Syft to accurately capture all dependencies declared in the METADATA file to ensure the reliability of SBOMs and the effectiveness of vulnerability management processes. The omission of key dependencies like Jinja2 and Werkzeug demonstrates a significant gap in Syft's dependency mapping capabilities, which must be addressed to maintain the integrity of software supply chain security.

Reproducing the Issue: A Step-by-Step Guide

To reproduce this issue and observe the behavior firsthand, you can follow these steps. This allows you to verify the problem and understand the context in which it occurs. It also provides a basis for testing potential solutions and ensuring that the fix effectively addresses the underlying issue.

Start a Python 3.11 container:
```
docker run -it --name py-env python:3.11 bash
```
This command starts a new Docker container running Python 3.11. This provides a clean and isolated environment for testing.
Install Syft:
```
curl -sSfL https://raw.githubusercontent.com/anchore/syft/main/install.sh | sh -s -- -b /usr/local/bin
```
This command downloads and executes the Syft installation script, placing the Syft binary in /usr/local/bin, making it accessible from the command line.
Create an app directory and requirements file:
```
mkdir app && cd app
echo -e "Flask==1.1.2\nJinja2==2.11.3" > requirements.txt
```
This creates a new directory named app, navigates into it, and creates a requirements.txt file containing the dependencies Flask==1.1.2 and Jinja2==2.11.3. This file specifies the packages that will be installed in the virtual environment.
Create a virtual environment and install dependencies:
```
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
These commands create a new virtual environment named .venv, activate it, and install the dependencies listed in requirements.txt using pip. This ensures that the dependencies are installed in an isolated environment, preventing conflicts with other Python installations.
Generate SBOM:
```
syft dir:.venv -o cyclonedx-json > sbom.json
```
This command runs Syft on the .venv directory, specifying the output format as CycloneDX JSON and saving the output to a file named sbom.json. This command will generate the SBOM that exhibits the missing dependency issue.

By following these steps, you can reproduce the issue where Syft fails to include all Requires-Dist relationships in the generated SBOM. Examining the sbom.json file will reveal that Jinja2 is not listed as a dependency of Flask, even though it is declared in Flask's METADATA file and installed in the virtual environment. This hands-on experience helps to solidify the understanding of the problem and its implications.

Impact: Vulnerability Detection and SBOM Integrity

The consequences of Syft's incomplete dependency graph are far-reaching, particularly in the context of vulnerability detection and SBOM integrity. When Syft omits dependencies from the SBOM, it creates a false sense of security, as vulnerabilities associated with those missing dependencies will not be identified. This can leave applications vulnerable to attacks that could have been prevented with a complete and accurate SBOM. The example provided in the initial report illustrates this point effectively. Grype, a vulnerability scanner, reports several vulnerabilities for Jinja2, including a critical HTML attribute injection vulnerability (GHSA-h75v-3vvj-5mfj) and a cross-site scripting (XSS) risk (CVE-2024-34064). If Syft omits Jinja2 from Flask's dependency graph, these vulnerabilities will not be associated with Flask in SBOM-based scans. This means that applications using Flask with the vulnerable version of Jinja2 will not be flagged as vulnerable, potentially leading to successful exploitation by attackers. The impact extends beyond individual applications. Organizations that rely on SBOMs for compliance checks and supply chain risk management may also be affected. Incomplete SBOMs can lead to inaccurate risk assessments and compliance violations, potentially resulting in financial penalties and reputational damage. Furthermore, the lack of accurate dependency information can hinder efforts to track and manage software assets effectively. Without a complete picture of the software supply chain, it becomes challenging to identify and address potential security risks proactively. Therefore, ensuring the completeness and accuracy of SBOMs is crucial for maintaining a strong security posture and managing software supply chain risks effectively. Syft's issue with missing Requires-Dist relationships highlights the importance of thorough testing and validation of SBOM generation tools to ensure that they provide reliable and accurate dependency information.

Why This Matters: The Bigger Picture of Software Supply Chain Security

This issue with Syft's dependency graph underscores a critical aspect of software supply chain security: the accuracy and completeness of SBOMs are paramount. In today's complex software ecosystems, applications often rely on a vast network of dependencies, many of which are transitive (i.e., dependencies of dependencies). Understanding these relationships is crucial for identifying and mitigating potential vulnerabilities. If an SBOM is incomplete or inaccurate, it can create blind spots in the security landscape, leaving organizations vulnerable to attacks. The omission of dependencies, as seen in the Syft example, is a significant concern because it can lead to a chain reaction of missed vulnerabilities. If a direct dependency is missing from the SBOM, any vulnerabilities in that dependency and its transitive dependencies will also be overlooked. This can have a cascading effect, potentially exposing applications to a wide range of security risks. The increasing reliance on open-source software and third-party libraries further amplifies the importance of accurate SBOMs. While these components offer numerous benefits, they also introduce potential security risks. Open-source vulnerabilities are often publicly disclosed, making them attractive targets for attackers. Therefore, organizations need to have a clear understanding of the open-source components they are using and their associated vulnerabilities. SBOMs provide this visibility, but only if they are complete and accurate. The Syft issue serves as a reminder that SBOM generation tools are not foolproof and should be thoroughly tested and validated. It also highlights the need for organizations to implement robust processes for SBOM management, including regular generation, validation, and analysis. By prioritizing SBOM accuracy and completeness, organizations can significantly enhance their software supply chain security posture and reduce their risk of exploitation.

Potential Solutions and Mitigation Strategies

Addressing the issue of Syft's incomplete dependency graph requires a multi-faceted approach, involving both immediate mitigation strategies and long-term solutions. In the short term, users can implement workarounds to improve the accuracy of SBOMs generated by Syft. One approach is to manually verify the dependency graph by comparing it with the METADATA files of the Python packages. This can help identify missing dependencies and allow users to add them manually to the SBOM. However, this is a time-consuming and error-prone process, especially for large and complex applications with numerous dependencies. Another mitigation strategy is to use alternative SBOM generation tools to complement Syft. There are several other tools available that can generate SBOMs for Python packages, and using multiple tools can help to identify discrepancies and improve overall accuracy. In the long term, the most effective solution is to address the underlying issue in Syft's dependency mapping logic. This requires a thorough investigation of Syft's codebase to identify the root cause of the problem and implement a fix. The Syft development team is actively working on this issue, and contributions from the open-source community are welcome. In addition to fixing the dependency mapping logic, it is also important to improve Syft's testing and validation processes. This includes adding more comprehensive test cases that cover various scenarios, including packages with complex dependency relationships. Regular testing and validation can help to identify and prevent similar issues in the future. Furthermore, it is crucial to educate users about the limitations of SBOM generation tools and the importance of verifying the accuracy of SBOMs. Users should be aware that SBOMs are not a silver bullet and should be used in conjunction with other security measures, such as vulnerability scanning and penetration testing. By combining these mitigation strategies and long-term solutions, organizations can improve the accuracy and reliability of their SBOMs and enhance their software supply chain security posture.

Conclusion: Ensuring Accurate SBOMs for a Secure Future

In conclusion, the issue of Syft's incomplete dependency graph highlights the critical importance of accurate and complete SBOMs in software supply chain security. The omission of Requires-Dist relationships in Python packages can lead to missed vulnerabilities and a false sense of security. While Syft is a valuable tool for SBOM generation, it is essential to be aware of its limitations and implement appropriate mitigation strategies. The steps outlined in this article, including reproducing the issue, understanding its impact, and exploring potential solutions, provide a comprehensive guide for addressing this problem. By manually verifying dependency graphs, using alternative SBOM generation tools, and contributing to the Syft project, users can help improve the accuracy and reliability of SBOMs. The long-term solution lies in addressing the underlying issue in Syft's dependency mapping logic and improving its testing and validation processes. The Syft development team is actively working on this, and the open-source community's involvement is crucial for ensuring a timely and effective fix. Ultimately, the goal is to create a secure software ecosystem where organizations can confidently rely on SBOMs for vulnerability management and compliance checks. This requires a collaborative effort from tool developers, users, and the broader security community. By prioritizing SBOM accuracy and completeness, we can build a more resilient and secure software supply chain for the future.

For more information on SBOMs and software supply chain security, visit the National Telecommunications and Information Administration (NTIA) website.