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A Python package and CLI for parsing aggregate and forensic DMARC reports

Project description

parsedmarc

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A screenshot of DMARC summary charts in Kibana

parsedmarc is a Python module and CLI utility for parsing DMARC reports. When used with Elasticsearch and Kibana (or Splunk), it works as a self-hosted open-source alternative to commercial DMARC report processing services such as Agari Brand Protection, Dmarcian, OnDMARC, ProofPoint Email Fraud Defense, and Valimail.

[!NOTE] Domain-based Message Authentication, Reporting, and Conformance (DMARC) is an email authentication protocol.

Help Wanted

This project is maintained by one developer. Please consider reviewing the open issues to see how you can contribute code, documentation, or user support. Assistance on the pinned issues would be particularly helpful.

Thanks to all contributors!

Features

  • Parses draft and 1.0 standard aggregate/rua DMARC reports
  • Parses forensic/failure/ruf DMARC reports
  • Parses reports from SMTP TLS Reporting
  • Can parse reports from an inbox over IMAP, Microsoft Graph, or Gmail API
  • Transparently handles gzip or zip compressed reports
  • Consistent data structures
  • Simple JSON and/or CSV output
  • Optionally email the results
  • Optionally send the results to Elasticsearch, Opensearch, and/or Splunk, for use with premade dashboards
  • Optionally send reports to Apache Kafka

Python Compatibility

This project supports the following Python versions, which are either actively maintained or are the default versions for RHEL or Debian.

Version Supported Reason
< 3.6 End of Life (EOL)
3.6 Used in RHEL 8, but not supported by project dependencies
3.7 End of Life (EOL)
3.8 End of Life (EOL)
3.9 Used in Debian 11 and RHEL 9, but not supported by project dependencies
3.10 Actively maintained
3.11 Actively maintained; supported until June 2028 (Debian 12)
3.12 Actively maintained; supported until May 2035 (RHEL 10)
3.13 Actively maintained; supported until June 2030 (Debian 13)
3.14 Supported (requires imapclient>=3.1.0)

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