CVE-2024-34359

{
  "id": "CVE-2024-34359",
  "cveTags": [],
  "metrics": {
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2024-34359",
          "role": "CISA Coordinator",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "version": "2.0.3",
          "timestamp": "2024-05-15T19:35:24.408358Z"
        }
      }
    ],
    "cvssMetricV31": [
      {
        "type": "Secondary",
        "source": "security-advisories@github.com",
        "cvssData": {
          "scope": "CHANGED",
          "version": "3.1",
          "baseScore": 9.6,
          "attackVector": "NETWORK",
          "baseSeverity": "CRITICAL",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H",
          "integrityImpact": "HIGH",
          "userInteraction": "REQUIRED",
          "attackComplexity": "LOW",
          "availabilityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "confidentialityImpact": "HIGH"
        },
        "impactScore": 6,
        "exploitabilityScore": 2.8
      }
    ]
  },
  "affected": [
    {
      "source": "security-advisories@github.com",
      "affectedData": [
        {
          "vendor": "abetlen",
          "product": "llama-cpp-python",
          "versions": [
            {
              "status": "affected",
              "version": ">= 0.2.30, <= 0.2.71"
            }
          ]
        }
      ]
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "affectedData": [
        {
          "cpes": [
            "cpe:2.3:a:abetlen:llama-cpp-python:*:*:*:*:*:*:*:*"
          ],
          "vendor": "abetlen",
          "product": "llama-cpp-python",
          "versions": [
            {
              "status": "affected",
              "version": "0.2.30",
              "versionType": "custom",
              "lessThanOrEqual": "0.2.71"
            }
          ],
          "defaultStatus": "unknown"
        }
      ]
    }
  ],
  "published": "2024-05-14T15:38:45.093",
  "references": [
    {
      "url": "https://github.com/abetlen/llama-cpp-python/commit/b454f40a9a1787b2b5659cd2cb00819d983185df",
      "source": "security-advisories@github.com"
    },
    {
      "url": "https://github.com/abetlen/llama-cpp-python/security/advisories/GHSA-56xg-wfcc-g829",
      "source": "security-advisories@github.com"
    },
    {
      "url": "https://github.com/abetlen/llama-cpp-python/commit/b454f40a9a1787b2b5659cd2cb00819d983185df",
      "source": "af854a3a-2127-422b-91ae-364da2661108"
    },
    {
      "url": "https://github.com/abetlen/llama-cpp-python/security/advisories/GHSA-56xg-wfcc-g829",
      "source": "af854a3a-2127-422b-91ae-364da2661108"
    }
  ],
  "vulnStatus": "Deferred",
  "weaknesses": [
    {
      "type": "Secondary",
      "source": "security-advisories@github.com",
      "description": [
        {
          "lang": "en",
          "value": "CWE-76"
        }
      ]
    }
  ],
  "descriptions": [
    {
      "lang": "en",
      "value": "llama-cpp-python is the Python bindings for llama.cpp. `llama-cpp-python` depends on class `Llama` in `llama.py` to load `.gguf` llama.cpp or Latency Machine Learning Models. The `__init__` constructor built in the `Llama` takes several parameters to configure the loading and running of the model. Other than `NUMA, LoRa settings`, `loading tokenizers,` and `hardware settings`, `__init__` also loads the `chat template` from targeted `.gguf` 's Metadata and furtherly parses it to `llama_chat_format.Jinja2ChatFormatter.to_chat_handler()` to construct the `self.chat_handler` for this model. Nevertheless, `Jinja2ChatFormatter` parse the `chat template` within the Metadate with sandbox-less `jinja2.Environment`, which is furthermore rendered in `__call__` to construct the `prompt` of interaction. This allows `jinja2` Server Side Template Injection which leads to remote code execution by a carefully constructed payload."
    },
    {
      "lang": "es",
      "value": "llama-cpp-python son los enlaces de Python para llama.cpp. `llama-cpp-python` depende de la clase `Llama` en `llama.py` para cargar `.gguf` llama.cpp o modelos de aprendizaje automático de latencia. El constructor `__init__` integrado en `Llama` toma varios parámetros para configurar la carga y ejecución del modelo. Además de `NUMA, configuración de LoRa`, `carga de tokenizadores` y `configuración de hardware`, `__init__` también carga la `plantilla de chat` desde los metadatos `.gguf` específicos y además la analiza en `llama_chat_format.Jinja2ChatFormatter.to_chat_handler ()` para construir el `self.chat_handler` para este modelo. Sin embargo, `Jinja2ChatFormatter` analiza la `plantilla de chat` dentro del Metadate con `jinja2.Environment` sin zona de pruebas, que además se representa en `__call__` para construir el `mensaje` de interacción. Esto permite la inyección de plantilla del lado del servidor `jinja2`, lo que conduce a la ejecución remota de código mediante un payload cuidadosamente construida."
    }
  ],
  "lastModified": "2026-06-17T07:33:14.750",
  "sourceIdentifier": "security-advisories@github.com"
}