Truck-Factor VS awesome-vulnerability-assessment

Compare Truck-Factor vs awesome-vulnerability-assessment and see what are their differences.

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Truck-Factor awesome-vulnerability-assessment
1 1
155 78
0.0% -
3.7 2.3
2 months ago about 1 year ago
Java
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Truck-Factor

Posts with mentions or reviews of Truck-Factor. We have used some of these posts to build our list of alternatives and similar projects.

awesome-vulnerability-assessment

Posts with mentions or reviews of awesome-vulnerability-assessment. We have used some of these posts to build our list of alternatives and similar projects.
  • Seeking Advice on Developing a Vulnerability Management Program
    1 project | /r/cybersecurity | 28 Apr 2023
    At first glance the tool selection looks a bit counterintuitive - will your focus be EASM, vulnerability assessment (you are not managing anything unless you include risk acceptance/mitigation and remediation) or automated (atomic) red teaming? For easy exploitability checks have a look at Prelude Operator; Nuclei as a modern scanner, OpenVAS to represent the traditional approach. For theory backing check here: https://github.com/lhmtriet/awesome-vulnerability-assessment

What are some alternatives?

When comparing Truck-Factor and awesome-vulnerability-assessment you can also consider the following projects:

subaru-starlink-research - Subaru StarLink persistent root code execution.

yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

awesome-seml - A curated list of articles that cover the software engineering best practices for building machine learning applications.