PYSEC-2024-218
Vulnerability from pysec - Published: 2024-10-10 23:15 - Updated: 2025-01-19 22:22
VLAI
Details
Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves a race condition in the update_root_in_config function, allowing an attacker to modify the root URL used by the Gradio frontend to communicate with the backend. By exploiting this flaw, an attacker can redirect user traffic to a malicious server. This could lead to the interception of sensitive data such as authentication credentials or uploaded files. This impacts all users who connect to a Gradio server, especially those exposed to the internet, where malicious actors could exploit this race condition. Users are advised to upgrade to gradio>=5 to address this issue. There are no known workarounds for this issue.
Severity
8.1 (High)
Impacted products
| Name | purl | gradio | pkg:pypi/gradio |
|---|
Aliases
{
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{
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},
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{
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{
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}
],
"aliases": [
"CVE-2024-47870",
"GHSA-xh2x-3mrm-fwqm"
],
"details": "Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves a **race condition** in the `update_root_in_config` function, allowing an attacker to modify the `root` URL used by the Gradio frontend to communicate with the backend. By exploiting this flaw, an attacker can redirect user traffic to a malicious server. This could lead to the interception of sensitive data such as authentication credentials or uploaded files. This impacts all users who connect to a Gradio server, especially those exposed to the internet, where malicious actors could exploit this race condition. Users are advised to upgrade to `gradio\u003e=5` to address this issue. There are no known workarounds for this issue.",
"id": "PYSEC-2024-218",
"modified": "2025-01-19T22:22:23.803143+00:00",
"published": "2024-10-10T23:15:03+00:00",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/gradio-app/gradio/security/advisories/GHSA-xh2x-3mrm-fwqm"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
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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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