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torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl: 4 vulnerabilities (highest severity is: 9.8) #176

Open mend-bolt-for-github[bot] opened 2 months ago

mend-bolt-for-github[bot] commented 2 months ago
Vulnerable Library - torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Library home page: https://files.pythonhosted.org/packages/00/86/77a9eddbf46f1bca2468d16a401911f58917f95b63402d6a7a4522521e5d/torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/PyDaw-0.4.6,/tmp/ws-scm/tes,/audiolm-pytorch-main

Found in HEAD commit: 2ecbce8beb9b9bd3cb2562e3a8543366c300c4c6

Vulnerabilities

CVE Severity CVSS Dependency Type Fixed in (torch version) Remediation Possible**
CVE-2024-5480 Critical 9.8 torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl Direct N/A ❌
CVE-2024-31584 High 7.5 torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl Direct 2.2.0 ❌
CVE-2024-31583 High 7.5 torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl Direct 2.2.0 ❌
CVE-2024-31580 High 7.5 torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl Direct 2.2.0 ❌

**In some cases, Remediation PR cannot be created automatically for a vulnerability despite the availability of remediation

Details

CVE-2024-5480 ### Vulnerable Library - torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Library home page: https://files.pythonhosted.org/packages/00/86/77a9eddbf46f1bca2468d16a401911f58917f95b63402d6a7a4522521e5d/torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/PyDaw-0.4.6,/tmp/ws-scm/tes,/audiolm-pytorch-main

Dependency Hierarchy: - :x: **torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl** (Vulnerable Library)

Found in HEAD commit: 2ecbce8beb9b9bd3cb2562e3a8543366c300c4c6

Found in base branch: main

### Vulnerability Details

A vulnerability in the PyTorch's torch.distributed.rpc framework, specifically in versions prior to 2.2.2, allows for remote code execution (RCE). The framework, which is used in distributed training scenarios, does not properly verify the functions being called during RPC (Remote Procedure Call) operations. This oversight permits attackers to execute arbitrary commands by leveraging built-in Python functions such as eval during multi-cpu RPC communication. The vulnerability arises from the lack of restriction on function calls when a worker node serializes and sends a PythonUDF (User Defined Function) to the master node, which then deserializes and executes the function without validation. This flaw can be exploited to compromise master nodes initiating distributed training, potentially leading to the theft of sensitive AI-related data.

Publish Date: 2024-06-06

URL: CVE-2024-5480

### CVSS 3 Score Details (9.8)

Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High

For more information on CVSS3 Scores, click here.

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CVE-2024-31584 ### Vulnerable Library - torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Library home page: https://files.pythonhosted.org/packages/00/86/77a9eddbf46f1bca2468d16a401911f58917f95b63402d6a7a4522521e5d/torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/PyDaw-0.4.6,/tmp/ws-scm/tes,/audiolm-pytorch-main

Dependency Hierarchy: - :x: **torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl** (Vulnerable Library)

Found in HEAD commit: 2ecbce8beb9b9bd3cb2562e3a8543366c300c4c6

Found in base branch: main

### Vulnerability Details

Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.

Publish Date: 2024-04-19

URL: CVE-2024-31584

### CVSS 3 Score Details (7.5)

Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High

For more information on CVSS3 Scores, click here.

### Suggested Fix

Type: Upgrade version

Origin: https://www.cve.org/CVERecord?id=CVE-2024-31584

Release Date: 2024-04-19

Fix Resolution: 2.2.0

Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
CVE-2024-31583 ### Vulnerable Library - torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Library home page: https://files.pythonhosted.org/packages/00/86/77a9eddbf46f1bca2468d16a401911f58917f95b63402d6a7a4522521e5d/torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/PyDaw-0.4.6,/tmp/ws-scm/tes,/audiolm-pytorch-main

Dependency Hierarchy: - :x: **torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl** (Vulnerable Library)

Found in HEAD commit: 2ecbce8beb9b9bd3cb2562e3a8543366c300c4c6

Found in base branch: main

### Vulnerability Details

Pytorch before version v2.2.0 was discovered to contain a use-after-free vulnerability in torch/csrc/jit/mobile/interpreter.cpp.

Publish Date: 2024-04-17

URL: CVE-2024-31583

### CVSS 3 Score Details (7.5)

Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High

For more information on CVSS3 Scores, click here.

### Suggested Fix

Type: Upgrade version

Origin: https://www.cve.org/CVERecord?id=CVE-2024-31583

Release Date: 2024-04-17

Fix Resolution: 2.2.0

Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
CVE-2024-31580 ### Vulnerable Library - torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Library home page: https://files.pythonhosted.org/packages/00/86/77a9eddbf46f1bca2468d16a401911f58917f95b63402d6a7a4522521e5d/torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/PyDaw-0.4.6,/tmp/ws-scm/tes,/audiolm-pytorch-main

Dependency Hierarchy: - :x: **torch-1.13.1-cp37-cp37m-manylinux1_x86_64.whl** (Vulnerable Library)

Found in HEAD commit: 2ecbce8beb9b9bd3cb2562e3a8543366c300c4c6

Found in base branch: main

### Vulnerability Details

PyTorch before v2.2.0 was discovered to contain a heap buffer overflow vulnerability in the component /runtime/vararg_functions.cpp. This vulnerability allows attackers to cause a Denial of Service (DoS) via a crafted input.

Publish Date: 2024-04-17

URL: CVE-2024-31580

### CVSS 3 Score Details (7.5)

Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High

For more information on CVSS3 Scores, click here.

### Suggested Fix

Type: Upgrade version

Origin: https://www.cve.org/CVERecord?id=CVE-2024-31580

Release Date: 2024-04-17

Fix Resolution: 2.2.0

Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
secure-code-warrior-for-github[bot] commented 2 months ago

Micro-Learning Topic: Buffer overflow (Detected by phrase)

Matched on "buffer overflow"

What is this? (2min video)

A buffer overflow condition exists when a program attempts to put more data in a buffer than it can hold or when a program attempts to put data in a memory area past a buffer.

Try a challenge in Secure Code Warrior

Micro-Learning Topic: Denial of service (Detected by phrase)

Matched on "Denial of Service"

The Denial of Service (DoS) attack is focused on making a resource (site, application, server) unavailable for the purpose it was designed. There are many ways to make a service unavailable for legitimate users by manipulating network packets, programming, logical, or resources handling vulnerabilities, among others. Source: https://www.owasp.org/index.php/Denial_of_Service

Try a challenge in Secure Code Warrior

Micro-Learning Topic: Use-after-free (Detected by phrase)

Matched on "use-after-free"

What is this? (2min video)

Dereferencing pointers to objects that have already been freed opens the door to execution of arbitrary code. Attackers may be able to insert instructions at the freed memory location in order to trigger the exploit when the pointer is used after the memory has been freed.

Try a challenge in Secure Code Warrior

Micro-Learning Topic: Vulnerable library (Detected by phrase)

Matched on "Vulnerable Library"

What is this? (2min video)

Use of vulnerable components will introduce weaknesses into the application. Components with published vulnerabilities will allow easy exploitation as resources will often be available to automate the process.

Try a challenge in Secure Code Warrior