The Stakeholder-specific Vulnerability Categorization (SSVC) is a system for prioritizing actions during vulnerability management. SSVC aims to avoid one-size-fits-all solutions in favor of a modular decision-making system with clearly defined and tested parts that vulnerability managers can select and use as appropriate to their context.

SSVC is mostly conceptual tools for vulnerability management. These conceptual tools (how to make decisions, what should go into a decision, how to document and communicate decisions clearly, etc.) are described here.

Note: This repository contains the content for the main SSVC documentation hosted at

• If you are just looking for SSVC documentation, you should go there.
• If you are interested in contributing to the SSVC documentation, you are in the right place.

Here's a quick overview of the main directories and files in this repository.

Raw markdown and graphics files used to build the SSVC documentation website. See project_docs/README.md for more info.

Directory with SSVC calculator using D3 graph. See ssvc-calc/README.md for more info.

A demo version of ssvc-calc can be found at https://certcc.github.io/SSVC/ssvc-calc/

Static versions of previously issued PDF reports are stored in this directory.

The data folder contains detailed data files that define suggested prioritization results based on each combination of information on a vulnerability work item.

There are both .csv and .json files in this directory.

The .csv files are the primary data files used by the ssvc.py module.

Also included in data are the lookup tables as csv files which ssvc_v2.py reads in. These files define one row per possible path through the trees as described in the documentation. Customizing the "outcome" column in this csv is the primary recommended way that stakeholders might adapt SSVC to their environment.

These json files are generated examples from the python ssvc module.

These files are used by the ssvc-calc module.

This directory holds helper scripts that can make managing or using SSVC easier.

The ssvc python module provides tools to work with decision points, decision point groups, and outcomes. These modules are used to generate documentation for various Decision Points

Documentation for the ssvc module can be found at https://certcc.github.io/SSVC/reference/code/

A basic Python module for interacting with the SSVC trees. ssvc_v2.py has two methods: applier_tree() and developer_tree()

The two methods just loop through their respective lookup tables until they hit a match, then return the outcome. Maybe not the best implementation, but it worked well enough for what was needed at the time.

Install prerequisites:

pip install -r requirements.txt

Start a local server:

mkdocs serve

Navigate to http://localhost:8001/ to see the site.

(Hint: You can use the --dev-addr argument with mkdocs to change the port, e.g. mkdocs serve --dev-addr localhost:8000)

• SSVC Community Engagement has more detail on how to contribute to the project.
• SSVC Project Wiki for more detail how to contribute to the project (style guides, etc.)
• CONTRIBUTING.md for high-level information and legal details

To reference SSVC in an academic publication, please refer to the version presented at the 2020 Workshop on Economics of Information Security (WEIS):

@inproceedings{spring2020ssvc,  
  title={Prioritizing vulnerability response: {A} stakeholder-specific vulnerability categorization},  
  author={Jonathan M Spring and Eric Hatleback and Allen D. Householder and Art Manion and Deana Shick},  
  address={Brussels, Belgium},  
  year={2020},  
  month = dec,  
  booktitle = {Workshop on the Economics of Information Security}  
}

1. Spring, J., Hatleback, E., Householder, A., Manion, A., and Shick, D. "Prioritizing Vulnerability Response: A Stakeholder-Specific Vulnerability Categorization." White Paper, Software Engineering Institute, Carnegie Mellon University (2019). https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=636379
2. Spring, J., Hatleback, E., Householder, A., Manion, A., and Shick, D. "Towards Improving CVSS." White Paper, Software Engineering Institute, Carnegie Mellon University (2018). https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=538368