FKIE_CVE-2025-69534
Vulnerability from fkie_nvd - Published: 2026-03-05 15:16 - Updated: 2026-09-07 13:17
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
8.2 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:H
7.5 (High) - CVSS:3.1/
8.2 (High) - CVSS:3.1/
Summary
Python-Markdown version 3.8 contain a vulnerability where malformed HTML-like sequences can cause html.parser.HTMLParser to raise an unhandled AssertionError during Markdown parsing. Because Python-Markdown does not catch this exception, any application that processes attacker-controlled Markdown may crash. This enables remote, unauthenticated Denial of Service in web applications, documentation systems, CI/CD pipelines, and any service that renders untrusted Markdown. The issue was acknowledged by the vendor and fixed in version 3.8.1. This issue causes a remote Denial of Service in any application parsing untrusted Markdown, and can lead to Information Disclosure through uncaught exceptions.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| python-markdown | markdown | 3.8 |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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.
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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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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