torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl: 12 vulnerabilities (highest severity is: 9.8) #49
Description
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Vulnerabilities
| Vulnerability | Severity |  CVSS |
Dependency | Type | Fixed in (torch version) | Remediation Possible** |
|---|---|---|---|---|---|---|
| CVE-2025-32434 |  Critical |
9.8 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | https://github.com/pytorch/pytorch.git - v2.6.0 | ✅ |
| CVE-2025-3001 |  Medium |
5.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-3000 | Medium |
5.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-2999 | Medium |
5.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-2998 | Medium |
5.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-2148 | Medium |
5.0 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-4287 |  Low |
3.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-3730 | Low |
3.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-3136 | Low |
3.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-3121 | Low |
3.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-2953 | Low |
3.3 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
| CVE-2025-2149 | Low |
2.5 | torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl | Direct | N/A | ❌ |
**In some cases, Remediation PR cannot be created automatically for a vulnerability despite the availability of remediation
Details
CVE-2025-32434
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.
Publish Date: 2025-04-18
URL: CVE-2025-32434
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
Suggested Fix
Type: Upgrade version
Origin: pytorch/pytorch@8d4b8a9
Release Date: 2025-04-18
Fix Resolution: https://github.com/pytorch/pytorch.git - v2.6.0
⛑️ Automatic Remediation will be attempted for this issue.
CVE-2025-3001
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstm_cell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-03-31
URL: CVE-2025-3001
CVSS 3 Score Details (5.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: Low
- Integrity Impact: Low
- Availability Impact: Low
CVE-2025-3000
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability classified as critical has been found in PyTorch 2.6.0. This affects the function torch.jit.script. The manipulation leads to memory corruption. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-03-31
URL: CVE-2025-3000
CVSS 3 Score Details (5.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: Low
- Integrity Impact: Low
- Availability Impact: Low
CVE-2025-2999
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability was found in PyTorch 2.6.0. It has been rated as critical. Affected by this issue is the function torch.nn.utils.rnn.unpack_sequence. The manipulation leads to memory corruption. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-03-31
URL: CVE-2025-2999
CVSS 3 Score Details (5.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: Low
- Integrity Impact: Low
- Availability Impact: Low
CVE-2025-2998
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-03-31
URL: CVE-2025-2998
CVSS 3 Score Details (5.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: Low
- Integrity Impact: Low
- Availability Impact: Low
CVE-2025-2148
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability was found in PyTorch 2.6.0+cu124. It has been declared as critical. Affected by this vulnerability is the function torch.ops.profiler._call_end_callbacks_on_jit_fut of the component Tuple Handler. The manipulation of the argument None leads to memory corruption. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult.
Publish Date: 2025-03-10
URL: CVE-2025-2148
CVSS 3 Score Details (5.0)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Network
- Attack Complexity: High
- Privileges Required: None
- User Interaction: Required
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: Low
- Integrity Impact: Low
- Availability Impact: Low
CVE-2025-4287
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
Publish Date: 2025-05-05
URL: CVE-2025-4287
CVSS 3 Score Details (3.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: Low
CVE-2025-3730
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue. The security policy of the project warns to use unknown models which might establish malicious effects.
Publish Date: 2025-04-16
URL: CVE-2025-3730
CVSS 3 Score Details (3.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: Low
CVE-2025-3136
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue affects the function torch.cuda.memory.caching_allocator_delete of the file c10/cuda/CUDACachingAllocator.cpp. The manipulation leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-04-03
URL: CVE-2025-3136
CVSS 3 Score Details (3.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: Low
CVE-2025-3121
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability classified as problematic has been found in PyTorch 2.6.0. Affected is the function torch.jit.jit_module_from_flatbuffer. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-04-02
URL: CVE-2025-3121
CVSS 3 Score Details (3.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: Low
CVE-2025-2953
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The security policy of the project warns to use unknown models which might establish malicious effects.
Publish Date: 2025-03-30
URL: CVE-2025-2953
CVSS 3 Score Details (3.3)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: Low
CVE-2025-2149
Vulnerable Library - torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/68/6c/754b1b742258f9a76d8daf53ac55ce672228c988b5a1b59b16203dda6959/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Path to dependency file: /models/mhg_model
Path to vulnerable library: /tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl,/tmp/ws-ua_20250918082811_EVRJPN/python_EMJYWP/20250918082812/torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl
Dependency Hierarchy:
- ❌ torch-2.2.2-cp39-cp39-manylinux1_x86_64.whl (Vulnerable Library)
Found in HEAD commit: de615824db77a7030c9d4126994d28cbe005791b
Found in base branch: main
Vulnerability Details
A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnq_Sigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zero_point leads to improper initialization. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used.
Publish Date: 2025-03-10
URL: CVE-2025-2149
CVSS 3 Score Details (2.5)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: High
- Privileges Required: Low
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: Low
- Availability Impact: None
⛑️Automatic Remediation will be attempted for this issue.