Spoofy Versions Save

We are excited to announce the release of Spoofy v1.0.1! This update brings a significant enhancement to the performance of our domain analysis tool by introducing multi-threading capabilities. Now, you can process multiple domains simultaneously, leading to faster results and a more efficient experience.

Key features and improvements in Spoofy v1.0.1 include:

1. Multi-threading support: With this update, we have implemented multi-threading to parallelize domain processing. This allows the tool to analyze multiple domains concurrently, improving the overall performance and speed, especially when processing large domain lists.
2. Synchronized output: To ensure that the output from different threads does not overlap or interfere with each other, we've introduced thread synchronization using threading.Lock. This guarantees that the console output and report generation remain consistent and easy to read.
3. Code optimization: We have refactored and optimized the codebase to accommodate multi-threading, making it more robust and maintainable.

Spoofy.py v1.0.0 is the initial release of the tool, which performs the following tasks:

• Queries DNS servers to retrieve SPF and DMARC records for a given domain or list of domains.
• Determines if a domain is vulnerable to email spoofing by analyzing the retrieved SPF and DMARC records and their attributes.
• Prints the results to the console or outputs them to an Excel file.
• Changes and updates made in this release:

Initial release of Spoofy

• Supports single domain and input list of domains
• Prints results to console or saves them to an Excel file
• Utilizes DNS resolver to retrieve SPF and DMARC records
• Analyzes retrieved records to determine vulnerability to email spoofing

Future plans for the next release:

• Add support for additional email authentication protocols such as DKIM and ARC.
• Implement more advanced analysis techniques to improve accuracy of spoofing detection.
• Enhance reporting capabilities with charts, graphs, and data visualization.
• Improve the tool's performance and scalability for large-scale assessments.