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Pytorch CVE-2024-35199

HIGH
Exposure of Resource to Wrong Sphere (CWE-668)
2024-07-19 security-advisories@github.com
8.2
CVSS 3.1 · Vendor: github
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Severity by source

Vendor (github) PRIMARY
8.2 HIGH
AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:H

Primary rating from Vendor (github) · only source for this CVE.

CVSS VectorVendor: github

Attack Vector
Network
Attack Complexity
Low
Privileges Required
None
User Interaction
None
Scope
Unchanged
Confidentiality
Low
Integrity
None
Availability
High

Lifecycle Timeline

1
CVE Published
Jul 19, 2024 - 02:15 cve.org
HIGH 8.2

DescriptionCVE.org

TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. In affected versions the two gRPC ports 7070 and 7071, are not bound to localhost by default, so when TorchServe is launched, these two interfaces are bound to all interfaces. Customers using PyTorch inference Deep Learning Containers (DLC) through Amazon SageMaker and EKS are not affected. This issue in TorchServe has been fixed in PR #3083. TorchServe release 0.11.0 includes the fix to address this vulnerability. Users are advised to upgrade. There are no known workarounds for this vulnerability.

AnalysisAI

TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. Rated high severity (CVSS 8.2), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Exposure of Resource to Wrong Sphere vulnerability could allow attackers to access resources from an unintended security context.

Technical ContextAI

This vulnerability is classified as Exposure of Resource to Wrong Sphere (CWE-668), which allows attackers to access resources from an unintended security context. TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. In affected versions the two gRPC ports 7070 and 7071, are not bound to localhost by default, so when TorchServe is launched, these two interfaces are bound to all interfaces. Customers using PyTorch inference Deep Learning Containers (DLC) through Amazon SageMaker and EKS are not affected. This issue in TorchServe has been fixed in PR #3083. TorchServe release 0.11.0 includes the fix to address this vulnerability. Users are advised to upgrade. There are no known workarounds for this vulnerability. Affected products include: Pytorch Torchserve.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Implement proper access controls, validate resource access permissions, use security boundaries.

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CVE-2024-35199 vulnerability details – vuln.today

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