GHSA-7PGR-32FX-C6X9
Vulnerability from github – Published: 2024-10-11 15:30 – Updated: 2024-10-11 17:47
VLAI
Summary
Lord of Large Language Models (LoLLMs) Server path traversal vulnerability in lollms_file_system.py
Details
A path traversal vulnerability exists in the ParisNeo/lollms repository, specifically in the lollms_file_system.py file. The functions add_rag_database, toggle_mount_rag_database, and vectorize_folder do not implement security measures such as sanitize_path_from_endpoint or sanitize_path. This allows an attacker to perform vectorize operations on .sqlite files in any directory on the victim's computer, potentially installing multiple packages and causing a crash.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "lollms"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "9.5.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2024-6971"
],
"database_specific": {
"cwe_ids": [
"CWE-22"
],
"github_reviewed": true,
"github_reviewed_at": "2024-10-11T17:47:38Z",
"nvd_published_at": "2024-10-11T13:15:16Z",
"severity": "LOW"
},
"details": "A path traversal vulnerability exists in the ParisNeo/lollms repository, specifically in the `lollms_file_system.py` file. The functions `add_rag_database`, `toggle_mount_rag_database`, and `vectorize_folder` do not implement security measures such as `sanitize_path_from_endpoint` or `sanitize_path`. This allows an attacker to perform vectorize operations on `.sqlite` files in any directory on the victim\u0027s computer, potentially installing multiple packages and causing a crash.",
"id": "GHSA-7pgr-32fx-c6x9",
"modified": "2024-10-11T17:47:39Z",
"published": "2024-10-11T15:30:32Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-6971"
},
{
"type": "WEB",
"url": "https://github.com/ParisNeo/lollms/commit/aeace796d861e922133b769710019608a6363264"
},
{
"type": "PACKAGE",
"url": "https://github.com/ParisNeo/lollms"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/fbfe7cd0-99fb-4305-bd07-8b573364109e"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:H/UI:N/S:U/C:N/I:L/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:H/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:P",
"type": "CVSS_V4"
}
],
"summary": "Lord of Large Language Models (LoLLMs) Server path traversal vulnerability in lollms_file_system.py"
}
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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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