FKIE_CVE-2024-10188
Vulnerability from fkie_nvd - Published: 2025-03-20 10:15 - Updated: 2026-06-17 06:55
Severity
Summary
A vulnerability in BerriAI/litellm, as of commit 26c03c9, allows unauthenticated users to cause a Denial of Service (DoS) by exploiting the use of ast.literal_eval to parse user input. This function is not safe and is prone to DoS attacks, which can crash the litellm Python server.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "berriai/litellm",
"vendor": "berriai",
"versions": [
{
"lessThan": "1.53.1",
"status": "affected",
"version": "unspecified",
"versionType": "custom"
}
]
}
],
"source": "security@huntr.dev"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A vulnerability in BerriAI/litellm, as of commit 26c03c9, allows unauthenticated users to cause a Denial of Service (DoS) by exploiting the use of ast.literal_eval to parse user input. This function is not safe and is prone to DoS attacks, which can crash the litellm Python server."
},
{
"lang": "es",
"value": "Una vulnerabilidad en BerriAI/litellm, a partir del commit 26c03c9, permite a usuarios no autenticados causar una denegaci\u00f3n de servicio (DoS) al explotar el uso de ast.literal_eval para analizar la entrada del usuario. Esta funci\u00f3n no es segura y es propensa a ataques DoS, que pueden colapsar el servidor Python de litellm."
}
],
"id": "CVE-2024-10188",
"lastModified": "2026-06-17T06:55:07.207",
"metrics": {
"cvssMetricV30": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.0"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "security@huntr.dev",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2024-10188",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-03-20T17:50:45.877796Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-03-20T10:15:14.993",
"references": [
{
"source": "security@huntr.dev",
"url": "https://github.com/berriai/litellm/commit/21156ff5d0d84a7dd93f951ca033275c77e4f73c"
},
{
"source": "security@huntr.dev",
"url": "https://huntr.com/bounties/96a32812-213c-4819-ba4e-36143d35e95b"
}
],
"sourceIdentifier": "security@huntr.dev",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-400"
}
],
"source": "security@huntr.dev",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.