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Copilot
ae1e5adb66
* Add DMARCbis report support; rename forensic→failure project-wide
Rebased on top of master @ 2cda5bf (9.9.0), which added the ASN
source attribution work (#712, #713, #714, #715). Individual Copilot
iteration commits squashed into this single commit — the per-commit
history on the feature branch was iterative (add tests, fix lint,
move field, revert, etc.) and not worth preserving; GitHub squash-
merges PRs anyway.
New fields from the DMARCbis XSD, plumbed through types, parsing, CSV
output, and the Elasticsearch / OpenSearch mappings:
- ``np`` — non-existent subdomain policy (``none`` / ``quarantine`` /
``reject``)
- ``testing`` — testing mode flag (``n`` / ``y``), replaces RFC 7489
``pct``
- ``discovery_method`` — policy discovery method (``psl`` /
``treewalk``)
- ``generator`` — report generator software identifier (metadata)
- ``human_result`` — optional descriptive text on DKIM / SPF results
RFC 7489 reports parse with ``None`` for DMARCbis-only fields.
Forensic reports have been renamed to failure reports throughout the
project to reflect the proper naming since RFC 7489.
- Core: ``types.py``, ``__init__.py`` — ``ForensicReport`` →
``FailureReport``, ``parse_forensic_report`` →
``parse_failure_report``, report type ``"failure"``.
- Output modules: ``elastic.py``, ``opensearch.py``, ``splunk.py``,
``kafkaclient.py``, ``syslog.py``, ``gelf.py``, ``webhook.py``,
``loganalytics.py``, ``s3.py``.
- CLI: ``cli.py`` — args, config keys, index names
(``dmarc_failure``).
- Docs + dashboards: all markdown, Grafana JSON, Kibana NDJSON,
Splunk XML.
Backward compatibility preserved: old function / type names remain as
aliases (``parse_forensic_report = parse_failure_report``,
``ForensicReport = FailureReport``, etc.), CLI accepts both the old
(``save_forensic``, ``forensic_topic``) and new (``save_failure``,
``failure_topic``) config keys, and updated dashboards query both
old and new index / sourcetype names so data from before and after
the rename appears together.
Merge conflicts resolved in ``parsedmarc/constants.py`` (took bis's
10.0.0 bump), ``parsedmarc/__init__.py`` (combined bis's "failure"
wording with master's IPinfo MMDB mention), ``parsedmarc/elastic.py``
and ``parsedmarc/opensearch.py`` (kept master's ``source_asn`` /
``source_asn_name`` / ``source_asn_domain`` on the failure doc path
while renaming ``forensic_report`` → ``failure_report``), and
``CHANGELOG.md`` (10.0.0 entry now sits above the 9.9.0 entry).
All 324 tests pass; ``ruff check`` / ``ruff format --check`` clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* Apply post-RFC review fixes: RFC 9990 detection, langAttrString, CFWS-aware RUF parsing
Aligns the implementation with the final RFCs (9989/9990/9991) instead of
inferring DMARCbis support from the version element or the namespace alone.
Aggregate parsing (RFC 9990):
- _text() helper unwraps langAttrString values (extra_contact_info, error,
comment, human_result, generator) — when reporters include the lang
attribute, xmltodict yields {"#text": ..., "@lang": ...} dicts instead
of strings; the parser now stores the text payload in both shapes.
- New xml_namespace field on AggregateReport records the declared XML
namespace (urn:ietf:params:xml:ns:dmarc-2.0 for RFC 9990 reports).
- RFC 9990 detection accepts namespaceless reports that follow the
RFC 9990 shape (presence of np / testing / discovery_method / generator),
so reporters that don't declare the namespace still receive RFC 9990-
aware validation.
- Warnings: missing DKIM <selector> (REQUIRED in RFC 9990); legacy
forwarded / sampled_out policy-override types (removed by RFC 9990);
unknown policy-override types per the RFC 9990 enumeration.
- xml_namespace added to Elasticsearch and OpenSearch document mappings.
Failure parsing (RFC 9991):
- Identity-Alignment and Auth-Failure are split on commas with CFWS
whitespace stripped per the RFC 9991 ABNF; previously "dkim, spf"
yielded ["dkim", " spf"] with a leading space on the second token.
- Warnings logged when either REQUIRED field is missing.
Terminology: every reference to "DMARCbis" in code, tests, sample
filenames, AGENTS.md, and CHANGELOG.md is replaced with the appropriate
RFC number (9989 for the policy spec, 9990 for aggregate reports, 9991
for failure reports). Sample contents are unchanged.
Docs: corrects the prior claim that fo was dropped from RFC 9990 (only
pct was), reframes testing as a new field (not a pct replacement, since
RFC 9989 Appendix A.6 removed pct with no per-message substitute), and
documents the policy_override_reason enum changes (added policy_test_mode;
removed forwarded / sampled_out).
Tests: 8 new tests covering xml_namespace capture, RFC 9990 detection
from field shape, missing-DKIM-selector warning, legacy-override-type
warning, langAttrString unwrapping across all four affected elements,
and CFWS-aware Identity-Alignment / Auth-Failure parsing plus their
missing-field warnings. 276 tests total, all passing; ruff clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Sean Whalen <44679+seanthegeek@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
104 lines
4.6 KiB
Markdown
104 lines
4.6 KiB
Markdown
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# Using the Kibana dashboards
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The Kibana DMARC dashboards are a human-friendly way to understand the
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results from incoming DMARC reports.
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There is no separate Kibana export — Kibana 8.x's saved-object migration
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handlers accept the OpenSearch Dashboards format directly, so Kibana
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users import the bundled
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[`dashboards/opensearch/opensearch_dashboards.ndjson`](https://raw.githubusercontent.com/domainaware/parsedmarc/master/dashboards/opensearch/opensearch_dashboards.ndjson)
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in *Stack Management → Saved Objects → Import*. A CI check imports the
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same file into a Kibana 8.x container on every change so this stays
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compatible.
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:::{note}
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The default dashboard is DMARC aggregate reports. To switch between
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dashboards, click on the Dashboard link on the left side menu of Kibana.
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:::
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## DMARC aggregate reports
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As the name suggests, this dashboard is the best place to start
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reviewing your aggregate DMARC data.
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Across the top of the dashboard, three pie charts display the percentage of
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alignment pass/fail for SPF, DKIM, and DMARC. Clicking on any chart segment
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will filter for that value.
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:::{note}
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Messages should not be considered malicious just because they failed to pass
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DMARC; especially if you have just started collecting data. It may be a
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legitimate service that needs SPF and DKIM configured correctly.
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:::
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Start by filtering the results to only show failed DKIM alignment. While DMARC
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passes if a message passes SPF or DKIM alignment, only DKIM alignment remains
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valid when a message is forwarded without changing the from address, which is
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often caused by a mailbox forwarding rule. This is because DKIM signatures are
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part of the message headers, whereas SPF relies on SMTP session headers.
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Underneath the pie charts. you can see graphs of DMARC passage and message
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disposition over time.
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Under the graphs you will find the most useful data tables on the dashboard. On
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the left, there is a list of organizations that are sending you DMARC reports.
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In the center, there is a list of sending servers grouped by the base domain
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in their reverse DNS. On the right, there is a list of email from domains,
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sorted by message volume.
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By hovering your mouse over a data table value and using the magnifying glass
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icons, you can filter on our filter out different values. Start by looking at
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the Message Sources by Reverse DNS table. Find a sender that you recognize,
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such as an email marketing service, hover over it, and click on the plus (+)
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magnifying glass icon, to add a filter that only shows results for that sender.
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Now, look at the Message From Header table to the right. That shows you the
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domains that a sender is sending as, which might tell you which brand/business
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is using a particular service. With that information, you can contact them and
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have them set up DKIM.
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:::{note}
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If you have a lot of B2C customers, you may see a high volume of emails as
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your domains coming from consumer email services, such as Google/Gmail and
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Yahoo! This occurs when customers have mailbox rules in place that forward
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emails from an old account to a new account, which is why DKIM
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authentication is so important, as mentioned earlier. Similar patterns may
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be observed with businesses who send from reverse DNS addressees of
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parent, subsidiary, and outdated brands.
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:::
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Further down the dashboard, you can filter by source country or source IP
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address.
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Tables showing SPF and DKIM alignment details are located under the IP address
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table.
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:::{note}
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The alignment tables (SPF details, DKIM details) and the per-IP source
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table live on the same dashboard, further down. To view failures only,
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use the pie chart at the top of the page as a filter.
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:::
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Any other filters work the same way. You can also add your own custom temporary
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filters by clicking on Add Filter at the upper right of the page.
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## DMARC failure reports
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The DMARC failure reports dashboard (formerly DMARC Forensic Samples) contains
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information on DMARC failure reports (also known as forensic or ruf reports).
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These reports contain samples of emails that have failed to pass DMARC.
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:::{note}
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Most recipients do not send failure/ruf reports at all to avoid
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privacy leaks. Some recipients (notably Chinese webmail services) will only
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supply the headers of sample emails. Very few provide the entire email.
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:::
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## SMTP TLS reporting
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The SMTP TLS reporting dashboard surfaces aggregate counts of TLS-RPT
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reporting organizations, the policy domains they report on, and the
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specific failure types — certificate expiry, STARTTLS not supported,
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STS policy fetch errors, validation failures, and similar — together with
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the sending and receiving MTA addresses involved.
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