FKIE_CVE-2024-42477
Vulnerability from fkie_nvd - Published: 2024-08-12 15:15 - Updated: 2026-06-17 07:49
Severity
5.3 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.1/
Summary
llama.cpp provides LLM inference in C/C++. The unsafe `type` member in the `rpc_tensor` structure can cause `global-buffer-overflow`. This vulnerability may lead to memory data leakage. The vulnerability is fixed in b3561.
References
{
"affected": [
{
"affectedData": [
{
"product": "llama.cpp",
"vendor": "ggerganov",
"versions": [
{
"status": "affected",
"version": "\u003c b3561"
}
]
}
],
"source": "security-advisories@github.com"
},
{
"affectedData": [
{
"cpes": [
"cpe:2.3:a:ggerganov:llama.cpp:*:*:*:*:*:*:*:*"
],
"defaultStatus": "unknown",
"product": "llama.cpp",
"vendor": "ggerganov",
"versions": [
{
"lessThan": "b3561",
"status": "affected",
"version": "0",
"versionType": "custom"
}
]
}
],
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:ggml:llama.cpp:*:*:*:*:*:*:*:*",
"matchCriteriaId": "F9488233-BF84-4967-A11E-1A30EEEC9B9A",
"versionEndExcluding": "b3561",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "llama.cpp provides LLM inference in C/C++. The unsafe `type` member in the `rpc_tensor` structure can cause `global-buffer-overflow`. This vulnerability may lead to memory data leakage. The vulnerability is fixed in b3561."
},
{
"lang": "es",
"value": "llama.cpp proporciona inferencia LLM en C/C++. El miembro \"tipo\" inseguro en la estructura \"rpc_tensor\" puede provocar un \"desbordamiento de b\u00fafer global\". Esta vulnerabilidad puede provocar una p\u00e9rdida de datos de la memoria. La vulnerabilidad est\u00e1 solucionada en b3561."
}
],
"id": "CVE-2024-42477",
"lastModified": "2026-06-17T07:49:31.667",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2024-42477",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2024-08-13T14:06:01.701592Z",
"version": "2.0.3"
}
}
]
},
"published": "2024-08-12T15:15:21.050",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/ggerganov/llama.cpp/commit/b72942fac998672a79a1ae3c03b340f7e629980b"
},
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/ggerganov/llama.cpp/security/advisories/GHSA-mqp6-7pv6-fqjf"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-125"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-401"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…