FKIE_CVE-2024-23730
Vulnerability from fkie_nvd - Published: 2024-01-21 17:15 - Updated: 2026-06-17 07:13
Severity
9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
9.8 (Critical) - CVSS:3.1/
Summary
The OpenAPI and ChatGPT plugin loaders in LlamaHub (aka llama-hub) before 0.0.67 allow attackers to execute arbitrary code because safe_load is not used for YAML.
References
{
"affected": [
{
"affectedData": [
{
"product": "n/a",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "n/a"
}
]
}
],
"source": "cve@mitre.org"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:llamahub:llamahub:*:*:*:*:*:*:*:*",
"matchCriteriaId": "6AAAC629-1512-4B10-B87A-7817B24731D9",
"versionEndExcluding": "0.0.67",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The OpenAPI and ChatGPT plugin loaders in LlamaHub (aka llama-hub) before 0.0.67 allow attackers to execute arbitrary code because safe_load is not used for YAML."
},
{
"lang": "es",
"value": "Los cargadores de complementos OpenAPI y ChatGPT en LlamaHub (tambi\u00e9n conocido como llama-hub) anteriores a 0.0.67 permiten a los atacantes ejecutar c\u00f3digo arbitrario porque safe_load no se usa para YAML."
}
],
"id": "CVE-2024-23730",
"lastModified": "2026-06-17T07:13:28.320",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9,
"source": "nvd@nist.gov",
"type": "Primary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9,
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2024-23730",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "total"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-05-09T23:34:22.867082Z",
"version": "2.0.3"
}
}
]
},
"published": "2024-01-21T17:15:44.373",
"references": [
{
"source": "cve@mitre.org",
"tags": [
"Release Notes"
],
"url": "https://github.com/run-llama/llama-hub/blob/v0.0.67/CHANGELOG.md"
},
{
"source": "cve@mitre.org",
"tags": [
"Patch"
],
"url": "https://github.com/run-llama/llama-hub/pull/841/commits/9dc9c21a5c6d0226d1d2101c3121d4f085743d52"
},
{
"source": "cve@mitre.org",
"tags": [
"Release Notes"
],
"url": "https://github.com/run-llama/llama-hub/releases/tag/v0.0.67"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Release Notes"
],
"url": "https://github.com/run-llama/llama-hub/blob/v0.0.67/CHANGELOG.md"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Patch"
],
"url": "https://github.com/run-llama/llama-hub/pull/841/commits/9dc9c21a5c6d0226d1d2101c3121d4f085743d52"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Release Notes"
],
"url": "https://github.com/run-llama/llama-hub/releases/tag/v0.0.67"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-noinfo"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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