sentence-transformers-2.2.2.tar.gz: 10 vulnerabilities (highest severity is: 10.0) reachable #13
Description
Vulnerable Library - sentence-transformers-2.2.2.tar.gz
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Vulnerabilities
| Vulnerability | Severity |  CVSS |
Exploit Maturity | EPSS | Dependency | Type | Fixed in (sentence-transformers version) | Remediation Possible** | Reachability |
|---|---|---|---|---|---|---|---|---|---|
| CVE-2026-24747 |  High |
8.8 | Not Defined | 0.0% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | 2.3.0 | ✅ |
|
| CVE-2025-55551 | High |
7.5 | Not Defined | 0.1% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | 2.3.0 | ✅ |
|
| CVE-2025-3001 |  Medium |
5.3 | Not Defined | 0.2% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | N/A* | ❌ |
|
| CVE-2025-2999 | Medium |
5.3 | Not Defined | 0.1% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | N/A* | ❌ |
|
| CVE-2025-2998 | Medium |
5.3 | Not Defined | 0.1% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | N/A* | ❌ |
|
| CVE-2025-63396 |  Low |
3.3 | Not Defined | 0.0% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | N/A* | ❌ |
|
| CVE-2025-14009 |  Critical |
10.0 | Not Defined | 0.4% | nltk-3.9.2-py3-none-any.whl | Transitive | N/A* | ❌ |
|
| CVE-2025-68146 | Medium |
6.3 | Not Defined | 0.0% | filelock-3.19.1-py3-none-any.whl | Transitive | N/A* | ❌ |
|
| CVE-2026-22701 | Medium |
5.3 | Not Defined | 0.0% | filelock-3.19.1-py3-none-any.whl | Transitive | 2.3.0 | ✅ |
|
| CVE-2025-55552 | Medium |
5.3 | Not Defined | 0.1% | torch-2.8.0-cp310-none-macosx_11_0_arm64.whl | Transitive | N/A* | ❌ |
|
*For some transitive vulnerabilities, there is no version of direct dependency with a fix. Check the "Details" section below to see if there is a version of transitive dependency where vulnerability is fixed.
**In some cases, Remediation PR cannot be created automatically for a vulnerability despite the availability of remediation
Details
CVE-2026-24747
Vulnerable Library - torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/__init__.py (Extension)
-> sentence-transformers-2.2.2/sentence_transformers/datasets/SentencesDataset.py (Extension)
-> ❌ torch-2.8.0/torch/__init__.py (Vulnerable Component)
Vulnerability Details
PyTorch is a Python package that provides tensor computation. Prior to version 2.10.0, a vulnerability in PyTorch's "weights_only" unpickler allows an attacker to craft a malicious checkpoint file (".pth") that, when loaded with "torch.load(..., weights_only=True)", can corrupt memory and potentially lead to arbitrary code execution. Version 2.10.0 fixes the issue.
Publish Date: 2026-01-27
URL: CVE-2026-24747
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.0%
CVSS 3 Score Details (8.8)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Network
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: Required
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: High
- Integrity Impact: High
- Availability Impact: High
Suggested Fix
Type: Upgrade version
Release Date: 2026-01-27
Fix Resolution (torch): 2.10.0
Direct dependency fix Resolution (sentence-transformers): 2.3.0
⛑️ Automatic Remediation will be attempted for this issue.
CVE-2025-55551
Vulnerable Library - torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/SentenceTransformer.py (Extension)
-> torch-2.8.0/torch/__init__.py (Extension)
-> ❌ torch-2.8.0/torch/types.py (Vulnerable Component)
Vulnerability Details
An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation.
Publish Date: 2025-09-25
URL: CVE-2025-55551
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.1%
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
Suggested Fix
Type: Upgrade version
Release Date: 2025-09-25
Fix Resolution (torch): 2.9.0
Direct dependency fix Resolution (sentence-transformers): 2.3.0
⛑️ Automatic Remediation will be attempted for this issue.
CVE-2025-3001
Vulnerable Library - torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/__init__.py (Extension)
-> sentence-transformers-2.2.2/sentence_transformers/datasets/SentencesDataset.py (Extension)
-> ❌ torch-2.8.0/torch/__init__.py (Vulnerable Component)
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
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.2%
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-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/SentenceTransformer.py (Extension)
-> torch-2.8.0/torch/__init__.py (Extension)
-> ❌ torch-2.8.0/torch/types.py (Vulnerable Component)
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
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.1%
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-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/SentenceTransformer.py (Extension)
-> torch-2.8.0/torch/__init__.py (Extension)
-> ❌ torch-2.8.0/torch/types.py (Vulnerable Component)
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
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.1%
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-2025-63396
Vulnerable Library - torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
This vulnerability is potentially reachable
AutoPrompt/utils/dedup.py (Application)
-> sentence-transformers-2.2.2/sentence_transformers/__init__.py (Extension)
-> sentence-transformers-2.2.2/sentence_transformers/datasets/SentencesDataset.py (Extension)
-> ❌ torch-2.8.0/torch/__init__.py (Vulnerable Component)
Vulnerability Details
An issue was discovered in PyTorch v2.5 and v2.7.1. Omission of profiler.stop() can cause torch.profiler.profile (PythonTracer) to crash or hang during finalization, leading to a Denial of Service (DoS).
Publish Date: 2025-11-12
URL: CVE-2025-63396
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.0%
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-2025-14009
Vulnerable Library - nltk-3.9.2-py3-none-any.whl
Natural Language Toolkit
Library home page: https://files.pythonhosted.org/packages/60/90/81ac364ef94209c100e12579629dc92bf7a709a84af32f8c551b02c07e94/nltk-3.9.2-py3-none-any.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/nltk-3.9.2-py3-none-any.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ nltk-3.9.2-py3-none-any.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
The vulnerable code is unreachable
Vulnerability Details
A critical vulnerability exists in the NLTK downloader component of nltk/nltk, affecting all versions. The _unzip_iter function in nltk/downloader.py uses zipfile.extractall() without performing path validation or security checks. This allows attackers to craft malicious zip packages that, when downloaded and extracted by NLTK, can execute arbitrary code. The vulnerability arises because NLTK assumes all downloaded packages are trusted and extracts them without validation. If a malicious package contains Python files, such as init.py, these files are executed automatically upon import, leading to remote code execution. This issue can result in full system compromise, including file system access, network access, and potential persistence mechanisms.
Mend Note: The description of this vulnerability differs from MITRE.
Publish Date: 2026-02-18
URL: CVE-2025-14009
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.4%
CVSS 3 Score Details (10.0)
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Network
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: None
- Scope: Changed
- Impact Metrics:
- Confidentiality Impact: High
- Integrity Impact: High
- Availability Impact: High
CVE-2025-68146
Vulnerable Library - filelock-3.19.1-py3-none-any.whl
A platform independent file lock.
Library home page: https://files.pythonhosted.org/packages/42/14/42b2651a2f46b022ccd948bca9f2d5af0fd8929c4eec235b8d6d844fbe67/filelock-3.19.1-py3-none-any.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/filelock-3.19.1-py3-none-any.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
- ❌ filelock-3.19.1-py3-none-any.whl (Vulnerable Library)
- torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
The vulnerable code is unreachable
Vulnerability Details
filelock is a platform-independent file lock for Python. In versions prior to 3.20.1, a Time-of-Check-Time-of-Use (TOCTOU) race condition allows local attackers to corrupt or truncate arbitrary user files through symlink attacks. The vulnerability exists in both Unix and Windows lock file creation where filelock checks if a file exists before opening it with O_TRUNC. An attacker can create a symlink pointing to a victim file in the time gap between the check and open, causing os.open() to follow the symlink and truncate the target file. All users of filelock on Unix, Linux, macOS, and Windows systems are impacted. The vulnerability cascades to dependent libraries. The attack requires local filesystem access and ability to create symlinks (standard user permissions on Unix; Developer Mode on Windows 10+). Exploitation succeeds within 1-3 attempts when lock file paths are predictable. The issue is fixed in version 3.20.1. If immediate upgrade is not possible, use SoftFileLock instead of UnixFileLock/WindowsFileLock (note: different locking semantics, may not be suitable for all use cases); ensure lock file directories have restrictive permissions (chmod 0700) to prevent untrusted users from creating symlinks; and/or monitor lock file directories for suspicious symlinks before running trusted applications. These workarounds provide only partial mitigation. The race condition remains exploitable. Upgrading to version 3.20.1 is strongly recommended.
Publish Date: 2025-12-16
URL: CVE-2025-68146
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.0%
CVSS 3 Score Details (6.3)
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: High
- Availability Impact: High
Suggested Fix
Type: Upgrade version
Release Date: 2025-12-16
Fix Resolution: filelock - 3.20.1
CVE-2026-22701
Vulnerable Library - filelock-3.19.1-py3-none-any.whl
A platform independent file lock.
Library home page: https://files.pythonhosted.org/packages/42/14/42b2651a2f46b022ccd948bca9f2d5af0fd8929c4eec235b8d6d844fbe67/filelock-3.19.1-py3-none-any.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/filelock-3.19.1-py3-none-any.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
- ❌ filelock-3.19.1-py3-none-any.whl (Vulnerable Library)
- torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
The vulnerable code is unreachable
Vulnerability Details
filelock is a platform-independent file lock for Python. Prior to version 3.20.3, a TOCTOU race condition vulnerability exists in the SoftFileLock implementation of the filelock package. An attacker with local filesystem access and permission to create symlinks can exploit a race condition between the permission validation and file creation to cause lock operations to fail or behave unexpectedly. The vulnerability occurs in the _acquire() method between raise_on_not_writable_file() (permission check) and os.open() (file creation). During this race window, an attacker can create a symlink at the lock file path, potentially causing the lock to operate on an unintended target file or leading to denial of service. This issue has been patched in version 3.20.3.
Publish Date: 2026-01-10
URL: CVE-2026-22701
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.0%
CVSS 3 Score Details (5.3)
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: High
Suggested Fix
Type: Upgrade version
Release Date: 2026-01-10
Fix Resolution (filelock): 3.20.3
Direct dependency fix Resolution (sentence-transformers): 2.3.0
⛑️ Automatic Remediation will be attempted for this issue.
CVE-2025-55552
Vulnerable Library - torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Library home page: https://files.pythonhosted.org/packages/ef/d6/e6d4c57e61c2b2175d3aafbfb779926a2cfd7c32eeda7c543925dceec923/torch-2.8.0-cp310-none-macosx_11_0_arm64.whl
Path to dependency file: /requirements.txt
Path to vulnerable library: /tmp/ws-ua_20260219120409_AEEMWL/python_IKDEBG/20260219120410/torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl
Dependency Hierarchy:
- sentence-transformers-2.2.2.tar.gz (Root Library)
- ❌ torch-2.8.0-cp310-none-macosx_11_0_arm64.whl (Vulnerable Library)
Found in HEAD commit: c2a0de4212c13487918a46a2935ca84031dd91aa
Found in base branch: main
Reachability Analysis
The vulnerable code is unreachable
Vulnerability Details
pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
Publish Date: 2025-09-25
URL: CVE-2025-55552
Threat Assessment
Exploit Maturity: Not Defined
EPSS: 0.1%
CVSS 3 Score Details (5.3)
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: Low
⛑️Automatic Remediation will be attempted for this issue.