Total
354 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2021-29573 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29550 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29524 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29517 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29527 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29549 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29546 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29525 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-29538 | 1 Google | 1 Tensorflow | 2024-08-03 | 2.5 Low |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | ||||
| CVE-2021-28856 | 1 Entropymine | 1 Deark | 2024-08-03 | 5.5 Medium |
| In Deark before v1.5.8, a specially crafted input file can cause a division by zero in (src/fmtutil.c) because of the value of pixelsize. | ||||
| CVE-2021-27845 | 1 Jasper Project | 1 Jasper | 2024-08-03 | 5.5 Medium |
| A Divide-by-zero vulnerability exists in JasPer Image Coding Toolkit 2.0 in jasper/src/libjasper/jpc/jpc_enc.c | ||||
| CVE-2021-27847 | 1 Libvips | 1 Libvips | 2024-08-03 | 6.5 Medium |
| Division-By-Zero vulnerability in Libvips 8.10.5 in the function vips_eye_point, eye.c#L83, and function vips_mask_point, mask.c#L85. | ||||
| CVE-2021-27550 | 1 Polarisoffice | 1 Polaris Office | 2024-08-03 | 5.5 Medium |
| Polaris Office v9.102.66 is affected by a divide-by-zero error in PolarisOffice.exe and EngineDLL.dll that may cause a local denial of service. To exploit the vulnerability, someone must open a crafted PDF file. | ||||
| CVE-2021-25675 | 1 Siemens | 1 Simatic S7-plcsim | 2024-08-03 | 5.5 Medium |
| A vulnerability has been identified in SIMATIC S7-PLCSIM V5.4 (All versions). An attacker with local access to the system could cause a Denial-of-Service condition in the application when it is used to open a specially crafted file. As a consequence, a divide by zero operation could occur and cause the application to terminate unexpectedly and must be restarted to restore the service. | ||||
| CVE-2021-23210 | 1 Sox Project | 1 Sox | 2024-08-03 | 5.5 Medium |
| A floating point exception (divide-by-zero) issue was discovered in SoX in functon read_samples() of voc.c file. An attacker with a crafted file, could cause an application to crash. | ||||
| CVE-2021-20310 | 1 Imagemagick | 1 Imagemagick | 2024-08-03 | 7.5 High |
| A flaw was found in ImageMagick in versions before 7.0.11, where a division by zero ConvertXYZToJzazbz() of MagickCore/colorspace.c may trigger undefined behavior via a crafted image file that is submitted by an attacker and processed by an application using ImageMagick. The highest threat from this vulnerability is to system availability. | ||||
| CVE-2021-20309 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2024-08-03 | 7.5 High |
| A flaw was found in ImageMagick in versions before 7.0.11 and before 6.9.12, where a division by zero in WaveImage() of MagickCore/visual-effects.c may trigger undefined behavior via a crafted image file submitted to an application using ImageMagick. The highest threat from this vulnerability is to system availability. | ||||
| CVE-2021-20311 | 1 Imagemagick | 1 Imagemagick | 2024-08-03 | 7.5 High |
| A flaw was found in ImageMagick in versions before 7.0.11, where a division by zero in sRGBTransformImage() in the MagickCore/colorspace.c may trigger undefined behavior via a crafted image file that is submitted by an attacker processed by an application using ImageMagick. The highest threat from this vulnerability is to system availability. | ||||
| CVE-2021-20245 | 4 Debian, Fedoraproject, Imagemagick and 1 more | 4 Debian Linux, Fedora, Imagemagick and 1 more | 2024-08-03 | 5.5 Medium |
| A flaw was found in ImageMagick in coders/webp.c. An attacker who submits a crafted file that is processed by ImageMagick could trigger undefined behavior in the form of math division by zero. The highest threat from this vulnerability is to system availability. | ||||
| CVE-2021-20243 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2024-08-03 | 5.5 Medium |
| A flaw was found in ImageMagick in MagickCore/resize.c. An attacker who submits a crafted file that is processed by ImageMagick could trigger undefined behavior in the form of math division by zero. The highest threat from this vulnerability is to system availability. | ||||