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Sean WhalenandClaude Opus 4.8
319986f5fd
Two CBN behaviors, confirmed against Google's own "How to parse JSON data"
guide (statedump shows JSON true/199 retaining boolean/integer type) and the
published Corelight production parser:
1. The json{} filter preserves the original JSON type, so parsedmarc's boolean
*_aligned / testing / normalized_timespan and numeric count / *_session_count
/ source_asn would never match string comparisons. Add a mutate{convert} step
turning them into strings before any == "true"/"false" test or %{...} use.
2. CBN raises _failed_parsing_ when an `if [field]` references a field absent
from the log, and most detection/mapping fields are absent in 2 of the 3
report shapes (or null within one). Initialize every conditionally-checked
field to "" before the json{} filter.
Without these, DMARC-fail records would not be categorized AUTH_VIOLATION and
aggregate/TLS reports could fail parsing outright. README caveat and PR
validation steps updated accordingly.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
parsedmarc
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.
Sponsors
This project is maintained by one developer. Please consider sponsoring my work if you or your organization benefit from it.
Features
- Parses aggregate/rua DMARC reports: the legacy draft and 1.0 schemas (RFC 7489) and the new RFC 9990 schema for the final DMARC standard (RFC 9989)
- Parses failure/ruf DMARC reports (RFC 6591 and RFC 9991; formerly called forensic reports)
- Parses reports from SMTP TLS Reporting (TLS-RPT, RFC 8460)
- 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, Splunk, or PostgreSQL, for use with premade dashboards
- Optionally send the results to Apache Kafka, Amazon S3, Azure Log Analytics (Microsoft Sentinel), a Graylog (GELF) endpoint, a syslog server, or an HTTP webhook
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) |