GHSA-JMW2-399F-6MWG
Vulnerability from github – Published: 2024-04-10 18:30 – Updated: 2024-04-10 18:30
VLAI
Details
parisneo/lollms-webui is vulnerable to stored Cross-Site Scripting (XSS) that leads to Remote Code Execution (RCE). The vulnerability arises due to inadequate sanitization and validation of model output data, allowing an attacker to inject malicious JavaScript code. This code can be executed within the user's browser context, enabling the attacker to send a request to the /execute_code endpoint and establish a reverse shell to the attacker's host. The issue affects various components of the application, including the handling of user input and model output.
Severity
8.8 (High)
{
"affected": [],
"aliases": [
"CVE-2024-1602"
],
"database_specific": {
"cwe_ids": [
"CWE-79"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-04-10T17:15:52Z",
"severity": "HIGH"
},
"details": "parisneo/lollms-webui is vulnerable to stored Cross-Site Scripting (XSS) that leads to Remote Code Execution (RCE). The vulnerability arises due to inadequate sanitization and validation of model output data, allowing an attacker to inject malicious JavaScript code. This code can be executed within the user\u0027s browser context, enabling the attacker to send a request to the `/execute_code` endpoint and establish a reverse shell to the attacker\u0027s host. The issue affects various components of the application, including the handling of user input and model output.",
"id": "GHSA-jmw2-399f-6mwg",
"modified": "2024-04-10T18:30:47Z",
"published": "2024-04-10T18:30:47Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-1602"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/59be0d5a-f18e-4418-8f29-72320269a097"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/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.
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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