FKIE_CVE-2026-24486
Vulnerability from fkie_nvd - Published: 2026-01-27 01:16 - Updated: 2026-08-07 12:17
Severity
8.6 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:H/A:L
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N
8.6 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:H/A:L
7.5 (High) - CVSS:3.1/
8.6 (High) - CVSS:3.1/
Summary
Python-Multipart is a streaming multipart parser for Python. Prior to version 0.0.22, a Path Traversal vulnerability exists when using non-default configuration options `UPLOAD_DIR` and `UPLOAD_KEEP_FILENAME=True`. An attacker can write uploaded files to arbitrary locations on the filesystem by crafting a malicious filename. Users should upgrade to version 0.0.22 to receive a patch or, as a workaround, avoid using `UPLOAD_KEEP_FILENAME=True` in project configurations.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| fastapiexpert | python-multipart | * |
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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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