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Vulnerabilities

With the aim of informing, warning and helping professionals with the latest security vulnerabilities in technology systems, we have made a database available for users interested in this information, which is in Spanish and includes all of the latest documented and recognised vulnerabilities.

This repository, with over 75,000 registers, is based on the information from the NVD (National Vulnerability Database) – by virtue of a partnership agreement – through which INCIBE translates the included information into Spanish.

On occasions this list will show vulnerabilities that have still not been translated, as they are added while the INCIBE team is still carrying out the translation process. The CVE  (Common Vulnerabilities and Exposures) Standard for Information Security Vulnerability Names is used with the aim to support the exchange of information between different tools and databases.

All vulnerabilities collected are linked to different information sources, as well as available patches or solutions provided by manufacturers and developers. It is possible to carry out advanced searches, as there is the option to select different criteria to narrow down the results, some examples being vulnerability types, manufacturers and impact levels, among others.

Through RSS feeds or Newsletters we can be informed daily about the latest vulnerabilities added to the repository. Below there is a list, updated daily, where you can discover the latest vulnerabilities.

CVE-2022-21731
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be used to trigger a denial of service attack via a segfault caused by a type confusion. The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank. However, `WithRankAtLeast` receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a negative value, so the error check is bypassed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-21732
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
09/02/2022

CVE-2022-21733
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `StringNGrams` can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. We are missing a validation on `pad_witdh` and that result in computing a negative value for `ngram_width` which is later used to allocate parts of the output. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-21728
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` does not fully validate the value of `batch_dim` and can result in a heap OOB read. There is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of `Dim` would access elements before the start of an array. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-21730
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-21726
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-21727
Publication date:
03/02/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Severity CVSS v4.0: Pending analysis
Last modification:
05/05/2025

CVE-2022-24121
Publication date:
03/02/2022
SQL Injection vulnerability discovered in Unified Office Total Connect Now that would allow an attacker to extract sensitive information through a cookie parameter.
Severity CVSS v4.0: Pending analysis
Last modification:
08/02/2022

CVE-2022-23871
Publication date:
03/02/2022
Multiple cross-site scripting (XSS) vulnerabilities in the component outcomes_addProcess.php of Gibbon CMS v22.0.01 allow attackers to execute arbitrary web scripts or HTML via a crafted payload insterted into the name, category, description parameters.
Severity CVSS v4.0: Pending analysis
Last modification:
07/02/2022

CVE-2022-23873
Publication date:
03/02/2022
Victor CMS v1.0 was discovered to contain a SQL injection vulnerability that allows attackers to inject arbitrary commands via 'user_firstname' parameter.
Severity CVSS v4.0: Pending analysis
Last modification:
08/02/2022

CVE-2022-23357
Publication date:
03/02/2022
mozilo2.0 was discovered to be vulnerable to directory traversal attacks via the parameter curent_dir.
Severity CVSS v4.0: Pending analysis
Last modification:
30/09/2022

CVE-2022-22818
Publication date:
03/02/2022
The {% debug %} template tag in Django 2.2 before 2.2.27, 3.2 before 3.2.12, and 4.0 before 4.0.2 does not properly encode the current context. This may lead to XSS.
Severity CVSS v4.0: Pending analysis
Last modification:
07/11/2023
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