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Total
265 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-20243 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2022-01-01 | 4.3 MEDIUM | 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. | |||||
| CVE-2021-20245 | 4 Debian, Fedoraproject, Imagemagick and 1 more | 4 Debian Linux, Fedora, Imagemagick and 1 more | 2022-01-01 | 7.1 HIGH | 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-2015-6855 | 6 Arista, Canonical, Debian and 3 more | 7 Eos, Ubuntu Linux, Debian Linux and 4 more | 2021-12-15 | 5.0 MEDIUM | 7.5 HIGH |
| hw/ide/core.c in QEMU does not properly restrict the commands accepted by an ATAPI device, which allows guest users to cause a denial of service or possibly have unspecified other impact via certain IDE commands, as demonstrated by a WIN_READ_NATIVE_MAX command to an empty drive, which triggers a divide-by-zero error and instance crash. | |||||
| CVE-2020-23903 | 2 Fedoraproject, Xiph | 2 Fedora, Speex | 2021-12-15 | 4.3 MEDIUM | 5.5 MEDIUM |
| A Divide by Zero vulnerability in the function static int read_samples of Speex v1.2 allows attackers to cause a denial of service (DoS) via a crafted WAV file. | |||||
| CVE-2015-7513 | 4 Canonical, Debian, Fedoraproject and 1 more | 4 Ubuntu Linux, Debian Linux, Fedora and 1 more | 2021-12-10 | 4.9 MEDIUM | 6.5 MEDIUM |
| arch/x86/kvm/x86.c in the Linux kernel before 4.4 does not reset the PIT counter values during state restoration, which allows guest OS users to cause a denial of service (divide-by-zero error and host OS crash) via a zero value, related to the kvm_vm_ioctl_set_pit and kvm_vm_ioctl_set_pit2 functions. | |||||
| CVE-2021-20309 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2021-12-03 | 7.8 HIGH | 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-2020-20453 | 2 Debian, Ffmpeg | 2 Debian Linux, Ffmpeg | 2021-11-30 | 4.0 MEDIUM | 6.5 MEDIUM |
| FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/aaccoder, which allows a remote malicious user to cause a Denial of Service | |||||
| CVE-2020-20445 | 2 Debian, Ffmpeg | 2 Debian Linux, Ffmpeg | 2021-11-30 | 4.0 MEDIUM | 6.5 MEDIUM |
| FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/lpc.h, which allows a remote malicious user to cause a Denial of Service. | |||||
| CVE-2020-20446 | 2 Debian, Ffmpeg | 2 Debian Linux, Ffmpeg | 2021-11-30 | 4.0 MEDIUM | 6.5 MEDIUM |
| FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/aacpsy.c, which allows a remote malicious user to cause a Denial of Service. | |||||
| CVE-2018-18521 | 5 Canonical, Debian, Elfutils Project and 2 more | 7 Ubuntu Linux, Debian Linux, Elfutils and 4 more | 2021-11-30 | 4.3 MEDIUM | 5.5 MEDIUM |
| Divide-by-zero vulnerabilities in the function arlib_add_symbols() in arlib.c in elfutils 0.174 allow remote attackers to cause a denial of service (application crash) with a crafted ELF file, as demonstrated by eu-ranlib, because a zero sh_entsize is mishandled. | |||||
| CVE-2019-15939 | 3 Debian, Opencv, Opensuse | 3 Debian Linux, Opencv, Leap | 2021-11-30 | 4.3 MEDIUM | 5.9 MEDIUM |
| An issue was discovered in OpenCV 4.1.0. There is a divide-by-zero error in cv::HOGDescriptor::getDescriptorSize in modules/objdetect/src/hog.cpp. | |||||
| CVE-2021-41218 | 1 Google | 1 Tensorflow | 2021-11-09 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | |||||
| CVE-2021-41209 | 1 Google | 1 Tensorflow | 2021-11-09 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | |||||
| CVE-2021-41207 | 1 Google | 1 Tensorflow | 2021-11-09 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | |||||
| CVE-2020-23567 | 1 Irfanview | 1 Irfanview | 2021-11-08 | 4.3 MEDIUM | 5.5 MEDIUM |
| Irfanview v4.53 allows attackers to to cause a denial of service (DoS) via a crafted JPEG 2000 file. Related to "Integer Divide By Zero starting at JPEG2000!ShowPlugInSaveOptions_W+0x00000000000082ea" | |||||
| CVE-2020-20892 | 1 Ffmpeg | 1 Ffmpeg | 2021-09-24 | 6.8 MEDIUM | 8.8 HIGH |
| An issue was discovered in function filter_frame in libavfilter/vf_lenscorrection.c in Ffmpeg 4.2.1, allows attackers to cause a Denial of Service or other unspecified impacts due to a division by zero. | |||||
| CVE-2021-36692 | 1 Libjxl Project | 1 Libjxl | 2021-09-07 | 4.3 MEDIUM | 6.5 MEDIUM |
| libjxl v0.3.7 is affected by a Divide By Zero in issue in lib/extras/codec_apng.cc jxl::DecodeImageAPNG(). When encoding a malicous APNG file using cjxl, an attacker can trigger a denial of service. | |||||
| CVE-2021-27845 | 1 Jasper Project | 1 Jasper | 2021-09-07 | 4.3 MEDIUM | 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-2021-37668 | 1 Google | 1 Tensorflow | 2021-08-19 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.UnravelIndex` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by `dims` is not empty. Hence, if one element of `dims` is 0, the implementation does a division by 0. We have patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37684 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37683 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37691 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37675 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37680 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37640 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version. | |||||
| CVE-2021-37636 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37642 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37660 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2021-37653 | 1 Google | 1 Tensorflow | 2021-08-18 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | |||||
| CVE-2016-9921 | 3 Debian, Qemu, Redhat | 5 Debian Linux, Qemu, Enterprise Linux and 2 more | 2021-08-04 | 2.1 LOW | 6.5 MEDIUM |
| Quick emulator (Qemu) built with the Cirrus CLGD 54xx VGA Emulator support is vulnerable to a divide by zero issue. It could occur while copying VGA data when cirrus graphics mode was set to be VGA. A privileged user inside guest could use this flaw to crash the Qemu process instance on the host, resulting in DoS. | |||||
| CVE-2016-8669 | 4 Debian, Opensuse, Qemu and 1 more | 6 Debian Linux, Leap, Qemu and 3 more | 2021-08-04 | 2.1 LOW | 6.0 MEDIUM |
| The serial_update_parameters function in hw/char/serial.c in QEMU (aka Quick Emulator) allows local guest OS administrators to cause a denial of service (divide-by-zero error and QEMU process crash) via vectors involving a value of divider greater than baud base. | |||||
| CVE-2019-16168 | 7 Canonical, Debian, Fedoraproject and 4 more | 19 Ubuntu Linux, Debian Linux, Fedora and 16 more | 2021-07-31 | 4.3 MEDIUM | 6.5 MEDIUM |
| In SQLite through 3.29.0, whereLoopAddBtreeIndex in sqlite3.c can crash a browser or other application because of missing validation of a sqlite_stat1 sz field, aka a "severe division by zero in the query planner." | |||||
| CVE-2021-27847 | 1 Libvips Project | 1 Libvips | 2021-07-28 | 4.3 MEDIUM | 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-2021-29538 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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-2021-29550 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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-2021-29549 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| 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-2021-29556 | 1 Google | 1 Tensorflow | 2021-07-27 | 2.1 LOW | 5.5 MEDIUM |
| TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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-2021-29595 | 1 Google | 1 Tensorflow | 2021-07-26 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` 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-2021-29585 | 1 Google | 1 Tensorflow | 2021-07-26 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The TFLite computation for size of output after padding, `ComputeOutSize`(https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division. Users can craft special models such that `ComputeOutSize` is called with `stride` set to 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-2021-29589 | 1 Google | 1 Tensorflow | 2021-07-26 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension. 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-2021-29587 | 1 Google | 1 Tensorflow | 2021-07-26 | 4.6 MEDIUM | 7.8 HIGH |
| TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be 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-2021-29546 | 1 Google | 1 Tensorflow | 2021-07-26 | 4.6 MEDIUM | 7.8 HIGH |
| 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-2021-34069 | 1 Tsmuxer Project | 1 Tsmuxer | 2021-06-28 | 4.3 MEDIUM | 5.5 MEDIUM |
| Divide-by-zero bug in tsMuxer 2.6.16 allows attackers to cause a Denial of Service (DoS) by running the application with a crafted file. | |||||
| CVE-2020-27756 | 1 Imagemagick | 1 Imagemagick | 2021-06-02 | 4.3 MEDIUM | 5.5 MEDIUM |
| In ParseMetaGeometry() of MagickCore/geometry.c, image height and width calculations can lead to divide-by-zero conditions which also lead to undefined behavior. This flaw can be triggered by a crafted input file processed by ImageMagick and could impact application availability. The patch uses multiplication in addition to the function `PerceptibleReciprocal()` in order to prevent such divide-by-zero conditions. This flaw affects ImageMagick versions prior to 7.0.9-0. | |||||
| CVE-2020-27750 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2021-06-02 | 4.3 MEDIUM | 5.5 MEDIUM |
| A flaw was found in ImageMagick in MagickCore/colorspace-private.h and MagickCore/quantum.h. An attacker who submits a crafted file that is processed by ImageMagick could trigger undefined behavior in the form of values outside the range of type `unsigned char` and math division by zero. This would most likely lead to an impact to application availability, but could potentially cause other problems related to undefined behavior. This flaw affects ImageMagick versions prior to 7.0.8-68. | |||||
| CVE-2020-27760 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2021-06-02 | 4.3 MEDIUM | 5.5 MEDIUM |
| In `GammaImage()` of /MagickCore/enhance.c, depending on the `gamma` value, it's possible to trigger a divide-by-zero condition when a crafted input file is processed by ImageMagick. This could lead to an impact to application availability. The patch uses the `PerceptibleReciprocal()` to prevent the divide-by-zero from occurring. This flaw affects ImageMagick versions prior to ImageMagick 7.0.8-68. | |||||
| CVE-2021-20176 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2021-06-02 | 4.3 MEDIUM | 5.5 MEDIUM |
| A divide-by-zero flaw was found in ImageMagick 6.9.11-57 and 7.0.10-57 in gem.c. This flaw allows an attacker who submits a crafted file that is processed by ImageMagick to trigger undefined behavior through a division by zero. The highest threat from this vulnerability is to system availability. | |||||
| CVE-2020-27763 | 2 Debian, Imagemagick | 2 Debian Linux, Imagemagick | 2021-06-02 | 4.3 MEDIUM | 3.3 LOW |
| 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. This would most likely lead to an impact to application availability, but could potentially cause other problems related to undefined behavior. This flaw affects ImageMagick versions prior to 7.0.8-68. | |||||
| CVE-2020-27765 | 3 Debian, Imagemagick, Redhat | 3 Debian Linux, Imagemagick, Enterprise Linux | 2021-06-02 | 4.3 MEDIUM | 3.3 LOW |
| A flaw was found in ImageMagick in MagickCore/segment.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. This would most likely lead to an impact to application availability, but could potentially cause other problems related to undefined behavior. This flaw affects ImageMagick versions prior to 7.0.9-0. | |||||
| CVE-2020-27773 | 3 Debian, Imagemagick, Redhat | 3 Debian Linux, Imagemagick, Enterprise Linux | 2021-06-02 | 4.3 MEDIUM | 3.3 LOW |
| A flaw was found in ImageMagick in MagickCore/gem-private.h. An attacker who submits a crafted file that is processed by ImageMagick could trigger undefined behavior in the form of values outside the range of type `unsigned char` or division by zero. This would most likely lead to an impact to application availability, but could potentially cause other problems related to undefined behavior. This flaw affects ImageMagick versions prior to 7.0.9-0. | |||||