GHSA-G26J-5385-HHW3
Vulnerability from github – Published: 2024-09-13 18:31 – Updated: 2024-09-13 19:29
VLAI
Summary
LiteLLM Server-Side Request Forgery (SSRF) vulnerability
Details
A Server-Side Request Forgery (SSRF) vulnerability exists in berriai/litellm version 1.38.10. This vulnerability allows users to specify the api_base parameter when making requests to POST /chat/completions, causing the application to send the request to the domain specified by api_base. This request includes the OpenAI API key. A malicious user can set the api_base to their own domain and intercept the OpenAI API key, leading to unauthorized access and potential misuse of the API key.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "litellm"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.44.8"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2024-6587"
],
"database_specific": {
"cwe_ids": [
"CWE-918"
],
"github_reviewed": true,
"github_reviewed_at": "2024-09-13T19:29:08Z",
"nvd_published_at": "2024-09-13T16:15:04Z",
"severity": "HIGH"
},
"details": "A Server-Side Request Forgery (SSRF) vulnerability exists in berriai/litellm version 1.38.10. This vulnerability allows users to specify the `api_base` parameter when making requests to `POST /chat/completions`, causing the application to send the request to the domain specified by `api_base`. This request includes the OpenAI API key. A malicious user can set the `api_base` to their own domain and intercept the OpenAI API key, leading to unauthorized access and potential misuse of the API key.",
"id": "GHSA-g26j-5385-hhw3",
"modified": "2024-09-13T19:29:08Z",
"published": "2024-09-13T18:31:47Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-6587"
},
{
"type": "WEB",
"url": "https://github.com/berriai/litellm/commit/ba1912afd1b19e38d3704bb156adf887f91ae1e0"
},
{
"type": "PACKAGE",
"url": "https://github.com/berriai/litellm"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/4001e1a2-7b7a-4776-a3ae-e6692ec3d997"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "LiteLLM Server-Side Request Forgery (SSRF) vulnerability"
}
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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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