CVE-2024-34359

{"id": "CVE-2024-34359", "metrics": {"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": 6.0, "exploitabilityScore": 2.8}]}, "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": "Awaiting Analysis", "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 autom\u00e1tico de latencia. El constructor `__init__` integrado en `Llama` toma varios par\u00e1metros para configurar la carga y ejecuci\u00f3n del modelo. Adem\u00e1s de `NUMA, configuraci\u00f3n de LoRa`, `carga de tokenizadores` y `configuraci\u00f3n de hardware`, `__init__` tambi\u00e9n carga la `plantilla de chat` desde los metadatos `.gguf` espec\u00edficos y adem\u00e1s 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 adem\u00e1s se representa en `__call__` para construir el `mensaje` de interacci\u00f3n. Esto permite la inyecci\u00f3n de plantilla del lado del servidor `jinja2`, lo que conduce a la ejecuci\u00f3n remota de c\u00f3digo mediante un payload cuidadosamente construida."}], "lastModified": "2024-11-21T09:18:30.130", "sourceIdentifier": "security-advisories@github.com"}