CVE-2026-44844
eml_parser serves as a python module for parsing eml files and returning various information found in the e-mail as well as computed information.
Published May 26, 2026 · corrected/updated Jul 24, 2026 · evidence confidence medium
eml_parser serves as a python module for parsing eml files and returning various information found in the e-mail as well as computed information. Prior to 3.0.1, EmlParser.get_raw_body_text() recurses unconditionally for every nested message/rfc822 attachment without any depth limit. An attacker who can supply a badly crafted EML file with approximately 120 nested message/rfc822 parts triggers an unhandled RecursionError and aborts parsing of the message. A 12 KB EML file is enough to crash a worker. Though this causes the parser to crash, it is an unlikely scenario as the suggested EML that crashes the parser would not pass basic RFC compliance tests. This vulnerability is fixed in 3.0.1.
Grounded briefings
Catalog interpretations for three audiences. Each brief cites ingested metadata and the original advisories below. They are not independent research and they do not invent exploit steps.
Executive
CVE-2026-44844 is a medium public vulnerability record for this product. It is not currently listed in the CISA Known Exploited catalog. Check the linked vendor or CERT advisories for patch status. This summary is a catalog interpretation of the ingested metadata and cites the original advisories below.
System administrator
Identify whether this product is in your estate, then compare installed versions with the affected ranges. Affected version ranges have not been ingested yet. Check the linked vendor or CERT advisories for patch status. Prefer the vendor or distribution advisory linked in Sources before changing production systems. This is a catalog interpretation, not a runbook.
Security analyst
Signals on file: CVSS 6.3. It is not currently listed in the CISA Known Exploited catalog. Weakness classes: CWE-674. Sources contributing to this record: cve_org, nvd. Public exploit references, when present, are outbound links only and are not proof of active exploitation. This is a catalog interpretation of stored metadata.
Exploitation and remediation
Exploitation evidence
Not listed as known exploited in CISA KEV. Not in the CISA KEV catalog.
Patch and remediation
No required-action text is stored yet. Use the vendor or CERT links in Sources for patch availability.
Safe detection guidance
Compare installed versions with the affected ranges below and verify the advisory from the original publisher. This site stores metadata and outbound links only; it does not ship exploit code or proof-of-concept payloads.
Code
The project this record applies to. Exploit listings stay in References.
Timeline
Detection, updates, and resolution for this record.
May 26, 2026 · NIST NVD
Detected
eml_parser serves as a python module for parsing eml files and returning various information found in the e-mail as well as computed information. Prior to 3.0.1, EmlParser.get_raw_body_text() recurses unconditionally for every nested message/rfc822 attachment without any depth limit. An attacker who can supply a badly crafted EML file with approximately 120 nested message/rfc822 parts triggers an
Jul 24, 2026 · NIST NVD
Updated
eml_parser serves as a python module for parsing eml files and returning various information found in the e-mail as well as computed information. Prior to 3.0.1, EmlParser.get_raw_body_text() recurses unconditionally for every nested message/rfc822 attachment without any depth limit. An attacker who can supply a badly crafted EML file with approximately 120 nested message/rfc822 parts triggers an
Sources
- CVE.orgVulnerability recordCVE-2026-44844
Official CVE identifier
cve.org - NIST NVDVulnerability recordCVE-2026-44844
eml_parser serves as a python module for parsing eml files and returning various information found in the e-mail as well as computed information. Prior to 3.0.1, EmlParser.get_raw_body_text() recurses unconditionally for every nested message/rfc822 attachment without any depth li
nvd.nist.gov