Описание
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.
A flaw was found in PyTorch-Lightning. This vulnerability, categorized as insecure deserialization (CWE-502), exists in the checkpoint loading mechanism. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, which, when loaded by the LightningModule.load_from_checkpoint() method, allows the deserialization of arbitrary Python objects. This can lead to arbitrary code execution on the victim's system.
Отчет
This is an Important arbitrary code execution flaw in PyTorch-Lightning due to insecure deserialization in its checkpoint loading mechanism. Red Hat products utilizing PyTorch-Lightning, such as Red Hat Enterprise Linux AI and Red Hat AI Inference Server, are affected if they process untrusted checkpoint files. An attacker can craft a malicious checkpoint file, which, when loaded, can lead to arbitrary code execution on the system.
Меры по смягчению последствий
To mitigate this issue, avoid loading PyTorch-Lightning checkpoint files from untrusted or unverified sources. Ensure that all checkpoint files originate from trusted environments to prevent the deserialization of malicious Python objects and subsequent arbitrary code execution.
Затронутые пакеты
| Платформа | Пакет | Состояние | Рекомендация | Релиз |
|---|---|---|---|---|
| Red Hat AI Inference Server | rhaiis/vllm-cpu-rhel9 | Will not fix | ||
| Red Hat AI Inference Server | rhaiis/vllm-cuda-rhel9 | Affected | ||
| Red Hat AI Inference Server | rhaiis/vllm-rocm-rhel9 | Affected | ||
| Red Hat AI Inference Server | rhaiis/vllm-tpu-rhel9 | Will not fix | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-aws-cuda-rhel9 | Affected | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-azure-cuda-rhel9 | Affected | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-azure-rocm-rhel9 | Affected | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-cuda-rhel9 | Affected | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-gcp-cuda-rhel9 | Affected | ||
| Red Hat Enterprise Linux AI (RHEL AI) 3 | rhelai3/bootc-rocm-rhel9 | Affected |
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Дополнительная информация
Статус:
EPSS
8 High
CVSS3
Связанные уязвимости
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.
PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.
PyTorch Lightning load_from_checkpoint has an insecure checkpoint deserialization
EPSS
8 High
CVSS3