BREW-AIDER-CVE-2026-37004 (GHSA-6WVF-77M9-58RM)
Vulnerability from osv_homebrew – Published: 2026-09-09 23:40 – Updated: 2026-09-11 15:21 – Source website
VLAI
Summary
LiteLLM vulnerable to server-side template injection in the /prompts/test endpoint
Details
BerriAI litellm <=1.82.4 is vulnerable to Server-Side Template Injection (SSTI), which allows unauthenticated remote attackers to execute arbitrary OS commands via a crafted dotprompt_content parameter in the /prompts/test endpoint due to use of an unsandboxed jinja2.Environment.
Severity
9.8 (Critical)
References
{
"affected": [
{
"ecosystem_specific": {
"fix": null,
"range_state": "affected",
"resource": "litellm",
"resource_purl": "pkg:pypi/litellm@1.81.10",
"upstream_fixed_in": "1.83.7"
},
"package": {
"ecosystem": "Homebrew",
"name": "aider",
"purl": "pkg:brew/aider"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/litellm@1.81.10",
"name": "litellm",
"resource": "litellm",
"strategy": "registry",
"subject_version": "1.81.10"
}
]
},
"details": "BerriAI litellm \u003c=1.82.4 is vulnerable to Server-Side Template Injection (SSTI), which allows unauthenticated remote attackers to execute arbitrary OS commands via a crafted dotprompt_content parameter in the /prompts/test endpoint due to use of an unsandboxed jinja2.Environment.",
"id": "BREW-aider-CVE-2026-37004",
"modified": "2026-09-11T15:21:01Z",
"published": "2026-09-09T23:40:56Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-37004"
},
{
"type": "WEB",
"url": "https://github.com/BerriAI/litellm/commit/d910a95661fce3cdd36f3b06c03ecf9c46c6457c"
},
{
"type": "PACKAGE",
"url": "https://github.com/BerriAI/litellm"
},
{
"type": "WEB",
"url": "https://github.com/BerriAI/litellm/blob/244bdffd1bfe7bebdfdef516e1ebe426a898e2f0/litellm/proxy/prompts/prompt_endpoints.py#L1073"
},
{
"type": "WEB",
"url": "https://github.com/BerriAI/litellm/releases/tag/v1.83.7-stable"
},
{
"type": "WEB",
"url": "https://yerangamage.com/cves/detail/?slug=litellm-ssti-rce"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "LiteLLM vulnerable to server-side template injection in the /prompts/test endpoint",
"upstream": [
"GHSA-6wvf-77m9-58rm",
"CVE-2026-37004",
"PYSEC-2026-3861"
]
}
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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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