CNVD-2026-31829
Vulnerability from cnvd - Published: 2026-08-12
VLAI
Title
LiteLLM SQL注入漏洞
Description
LiteLLM是Berri AI开源的一个应用程序。可以使用OpenAI格式调用所有LLM API。
LiteLLM存在SQL注入漏洞,攻击者可利用该漏洞获取数据库敏感信息。
Severity
高
Formal description
目前没有详细解决方案提供: https://github.com/BerriAI/litellm/
Impacted products
| Name | litellm litellm |
|---|
{
"description": "LiteLLM\u662fBerri AI\u5f00\u6e90\u7684\u4e00\u4e2a\u5e94\u7528\u7a0b\u5e8f\u3002\u53ef\u4ee5\u4f7f\u7528OpenAI\u683c\u5f0f\u8c03\u7528\u6240\u6709LLM API\u3002 \n\nLiteLLM\u5b58\u5728SQL\u6ce8\u5165\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u83b7\u53d6\u6570\u636e\u5e93\u654f\u611f\u4fe1\u606f\u3002",
"formalWay": "\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u89e3\u51b3\u65b9\u6848\u63d0\u4f9b\uff1a\r\nhttps://github.com/BerriAI/litellm/",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2026-31829",
"openTime": "2026-08-12",
"products": {
"product": "litellm litellm"
},
"serverity": "\u9ad8",
"submitTime": "2026-08-12",
"title": "LiteLLM SQL\u6ce8\u5165\u6f0f\u6d1e"
}
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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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