PyTorch Quantized Sigmoid Module nnq_Sigmoid initialization (CVE-2025-2149)
A vulnerability in PyTorch's Quantized Sigmoid Module (nnq_Sigmoid) affects versions 2.6.0 up to but not including 2.7.0. The issue involves improper initialization caused by manipulation of the scale and zero_point arguments. Exploitation requires local access and is considered difficult due to high attack complexity. The vulnerability has been publicly disclosed, but no known exploits are reported in the wild. A patch is available to address this issue.
AI Analysis
Technical Summary
The PyTorch Quantized Sigmoid Module's nnq_Sigmoid function in versions >=2.6.0 <2.7.0 contains a vulnerability where improper initialization occurs due to manipulation of the scale and zero_point arguments. This flaw requires local access to exploit and has a high complexity, making exploitation difficult. The vulnerability has been publicly disclosed, and a patch is available to remediate the issue.
Potential Impact
Improper initialization in the nnq_Sigmoid function could potentially lead to unexpected behavior in the quantized sigmoid computations within PyTorch. However, exploitation requires local access and is difficult to perform, limiting the practical impact. There are no known exploits in the wild.
Mitigation Recommendations
A patch is available for this vulnerability. Users should update PyTorch to version 2.7.0 or later where the issue is fixed. Applying the official patch will remediate the improper initialization flaw.
PyTorch Quantized Sigmoid Module nnq_Sigmoid initialization (CVE-2025-2149)
Description
A vulnerability in PyTorch's Quantized Sigmoid Module (nnq_Sigmoid) affects versions 2.6.0 up to but not including 2.7.0. The issue involves improper initialization caused by manipulation of the scale and zero_point arguments. Exploitation requires local access and is considered difficult due to high attack complexity. The vulnerability has been publicly disclosed, but no known exploits are reported in the wild. A patch is available to address this issue.
Affected software
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The PyTorch Quantized Sigmoid Module's nnq_Sigmoid function in versions >=2.6.0 <2.7.0 contains a vulnerability where improper initialization occurs due to manipulation of the scale and zero_point arguments. This flaw requires local access to exploit and has a high complexity, making exploitation difficult. The vulnerability has been publicly disclosed, and a patch is available to remediate the issue.
Potential Impact
Improper initialization in the nnq_Sigmoid function could potentially lead to unexpected behavior in the quantized sigmoid computations within PyTorch. However, exploitation requires local access and is difficult to perform, limiting the practical impact. There are no known exploits in the wild.
Mitigation Recommendations
A patch is available for this vulnerability. Users should update PyTorch to version 2.7.0 or later where the issue is fixed. Applying the official patch will remediate the improper initialization flaw.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- BIT-pytorch-2025-2149
- Osv Schema Version
- 1.6.2
- Aliases
- ["CVE-2025-2149"]
- Ecosystems
- ["Bitnami"]
- Database Specific Severity
- Low
Threat ID: 6aa005ceacd9273b49ab5e1e
Added to database: 09/08/2026, 12:55:42 UTC
Last enriched: 09/08/2026, 13:24:27 UTC
Last updated: 09/10/2026, 19:36:49 UTC
Views: 4
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