CVE-2024-26151 (GCVE-0-2024-26151)
Vulnerability from cvelistv5 – Published: 2024-02-22 18:37 – Updated: 2024-08-22 18:00
VLAI
EPSS
VEX
Title
Potentially untrusted input is rendered as HTML in final output
Summary
The `mjml` PyPI package, found at the `FelixSchwarz/mjml-python` GitHub repo, is an unofficial Python port of MJML, a markup language created by Mailjet. All users of `FelixSchwarz/mjml-python` who insert untrusted data into mjml templates unless that data is checked in a very strict manner. User input like `<script>` would be rendered as `<script>` in the final HTML output. The attacker must be able to control some data which is later injected in an mjml template which is then send out as email to other users. The attacker could control contents of email messages sent through the platform. The problem has been fixed in version 0.11.0 of this library. Versions before 0.10.0 are not affected by this security issue. As a workaround, ensure that potentially untrusted user input does not contain any sequences which could be rendered as HTML.
Severity
8.2 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-02-22 20:16 UTC
CWE
- CWE-20 - Improper Input Validation
Assigner
GitHub_M
CNA
References
6 references
| URL | Tags |
|---|---|
| https://github.com/FelixSchwarz/mjml-python/secur… | x_refsource_CONFIRM |
| https://github.com/FelixSchwarz/mjml-python/issues/52 | x_refsource_MISC |
| https://github.com/FelixSchwarz/mjml-python/commi… | x_refsource_MISC |
| https://github.com/FelixSchwarz/mjml-python/commi… | x_refsource_MISC |
| https://github.com/FelixSchwarz/mjml-python/relea… | x_refsource_MISC |
| https://www.vicarius.io/vsociety/posts/cve-2024-2… |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| FelixSchwarz | mjml-python |
Affected:
= 0.10.0
|
|
| felixschwarz | mjml-python |
Affected:
0 , < 0.11.0
(custom)
cpe:2.3:a:felixschwarz:mjml-python:*:*:*:*:*:*:*:* |
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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