Search Results
There are 103 CVE Records that match your search.
| Name | Description |
|---|---|
| CVE-2024-26720 | In the Linux kernel, the following vulnerability has been resolved: mm/writeback: fix possible divide-by-zero in wb_dirty_limits(), again (struct dirty_throttle_control *)->thresh is an unsigned long, but is passed as the u32 divisor argument to div_u64(). On architectures where unsigned long is 64 bytes, the argument will be implicitly truncated. Use div64_u64() instead of div_u64() so that the value used in the "is this a safe division" check is the same as the divisor. Also, remove redundant cast of the numerator to u64, as that should happen implicitly. This would be difficult to exploit in memcg domain, given the ratio-based arithmetic domain_drity_limits() uses, but is much easier in global writeback domain with a BDI_CAP_STRICTLIMIT-backing device, using e.g. vm.dirty_bytes=(1<<32)*PAGE_SIZE so that dtc->thresh == (1<<32) |
| CVE-2024-26704 | In the Linux kernel, the following vulnerability has been resolved: ext4: fix double-free of blocks due to wrong extents moved_len In ext4_move_extents(), moved_len is only updated when all moves are successfully executed, and only discards orig_inode and donor_inode preallocations when moved_len is not zero. When the loop fails to exit after successfully moving some extents, moved_len is not updated and remains at 0, so it does not discard the preallocations. If the moved extents overlap with the preallocated extents, the overlapped extents are freed twice in ext4_mb_release_inode_pa() and ext4_process_freed_data() (as described in commit 94d7c16cbbbd ("ext4: Fix double-free of blocks with EXT4_IOC_MOVE_EXT")), and bb_free is incremented twice. Hence when trim is executed, a zero-division bug is triggered in mb_update_avg_fragment_size() because bb_free is not zero and bb_fragments is zero. Therefore, update move_len after each extent move to avoid the issue. |
| CVE-2023-42467 | QEMU through 8.0.0 could trigger a division by zero in scsi_disk_reset in hw/scsi/scsi-disk.c because scsi_disk_emulate_mode_select does not prevent s->qdev.blocksize from being 256. This stops QEMU and the guest immediately. |
| CVE-2023-25511 | NVIDIA CUDA Toolkit for Linux and Windows contains a vulnerability in cuobjdump, where a division-by-zero error may enable a user to cause a crash, which may lead to a limited denial of service. |
| CVE-2023-20588 | A division-by-zero error on some AMD processors can potentially return speculative data resulting in loss of confidentiality. |
| CVE-2022-39318 | FreeRDP is a free remote desktop protocol library and clients. Affected versions of FreeRDP are missing input validation in `urbdrc` channel. A malicious server can trick a FreeRDP based client to crash with division by zero. This issue has been addressed in version 2.9.0. All users are advised to upgrade. Users unable to upgrade should not use the `/usb` redirection switch. |
| CVE-2022-35996 | TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| CVE-2022-31169 | Wasmtime is a standalone runtime for WebAssembly. There is a bug in Wasmtime's code generator, Cranelift, for AArch64 targets where constant divisors can result in incorrect division results at runtime. This affects Wasmtime prior to version 0.38.2 and Cranelift prior to 0.85.2. This issue only affects the AArch64 platform. Other platforms are not affected. The translation rules for constants did not take into account whether sign or zero-extension should happen which resulted in an incorrect value being placed into a register when a division was encountered. The impact of this bug is that programs executing within the WebAssembly sandbox would not behave according to the WebAssembly specification. This means that it is hypothetically possible for execution within the sandbox to go awry and WebAssembly programs could produce unexpected results. This should not impact hosts executing WebAssembly but does affect the correctness of guest programs. This bug has been patched in Wasmtime version 0.38.2 and cranelift-codegen 0.85.2. There are no known workarounds. |
| CVE-2022-23557 | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would trigger a division by zero in `BiasAndClamp` implementation. There is no check that the `bias_size` is non zero. 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. |
| CVE-2022-21741 | Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. 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. |
| CVE-2022-21729 | Tensorflow is an Open Source Machine Learning Framework. The implementation of `UnravelIndex` is vulnerable to a division by zero caused by an integer overflow bug. 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. |
| CVE-2021-46906 | In the Linux kernel, the following vulnerability has been resolved: HID: usbhid: fix info leak in hid_submit_ctrl In hid_submit_ctrl(), the way of calculating the report length doesn't take into account that report->size can be zero. When running the syzkaller reproducer, a report of size 0 causes hid_submit_ctrl) to calculate transfer_buffer_length as 16384. When this urb is passed to the usb core layer, KMSAN reports an info leak of 16384 bytes. To fix this, first modify hid_report_len() to account for the zero report size case by using DIV_ROUND_UP for the division. Then, call it from hid_submit_ctrl(). |
| CVE-2021-4216 | A Floating point exception (division-by-zero) flaw was found in Mupdf for zero width pages in muraster.c. It is fixed in Mupdf-1.20.0-rc1 upstream. |
| CVE-2021-40211 | An issue was discovered with ImageMagick 7.1.0-4 via Division by zero in function ReadEnhMetaFile of coders/emf.c. |
| CVE-2021-3941 | In ImfChromaticities.cpp routine RGBtoXYZ(), there are some division operations such as `float Z = (1 - chroma.white.x - chroma.white.y) * Y / chroma.white.y;` and `chroma.green.y * (X + Z))) / d;` but the divisor is not checked for a 0 value. A specially crafted file could trigger a divide-by-zero condition which could affect the availability of programs linked with OpenEXR. |
| CVE-2021-37691 | 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-2021-37683 | 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-2021-37680 | 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 | 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-2021-3432 | Invalid interval in CONNECT_IND leads to Division by Zero. Zephyr versions >= v1.14.0 Divide By Zero (CWE-369). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-7364-p4wc-8mj4 |
| CVE-2021-32494 | Radare2 has a division by zero vulnerability in Mach-O parser's rebase_buffer function. This allow attackers to create malicious inputs that can cause denial of service. |
| CVE-2021-29604 | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 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-2021-29602 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 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-2021-29600 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 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-2021-29599 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` would be 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-2021-29598 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SVDF` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/7f283ff806b2031f407db64c4d3edcda8fb9f9f5/tensorflow/lite/kernels/svdf.cc#L99-L102). An attacker can craft a model such that `params->rank` would be 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-2021-29597 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` 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-2021-29596 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input 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-2021-29595 | 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-2021-29594 | TensorFlow is an end-to-end open source platform for machine learning. TFLite's convolution code(https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-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-2021-29593 | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` 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-2021-29589 | 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-2021-29588 | TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. 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 | 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-2021-29586 | TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by 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-2021-29573 | 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 | 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 | 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-2021-29548 | 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/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). 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 | 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-2021-29538 | 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-2021-29522 | TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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 | 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-2021-27847 | 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-2021-20311 | 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-2021-20310 | 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 | 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-2021-20246 | A flaw was found in ImageMagick in MagickCore/resample.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 | 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-2021-20244 | A flaw was found in ImageMagick in MagickCore/visual-effects.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 | 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-2021-20241 | A flaw was found in ImageMagick in coders/jp2.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-2021-20176 | 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-2020-27773 | 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. |
| CVE-2020-27765 | 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-2020-27763 | 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-2020-27750 | 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-2020-27560 | ImageMagick 7.0.10-34 allows Division by Zero in OptimizeLayerFrames in MagickCore/layer.c, which may cause a denial of service. |
| CVE-2020-20892 | 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-2020-16310 | A division by zero vulnerability in dot24_print_page() in devices/gdevdm24.c of Artifex Software GhostScript v9.50 allows a remote attacker to cause a denial of service via a crafted PDF file. This is fixed in v9.51. |
| CVE-2020-16299 | A Division by Zero vulnerability in bj10v_print_page() in contrib/japanese/gdev10v.c of Artifex Software GhostScript v9.50 allows a remote attacker to cause a denial of service via a crafted PDF file. This is fixed in v9.51. |
| CVE-2020-16161 | GoPro gpmf-parser 1.5 has a division-by-zero vulnerability in GPMF_ScaledData(). Parsing malicious input can result in a crash. |
| CVE-2020-16160 | GoPro gpmf-parser 1.5 has a division-by-zero vulnerability in GPMF_Decompress(). Parsing malicious input can result in a crash. |
| CVE-2019-9084 | In Hoteldruid before 2.3.1, a division by zero was discovered in $num_tabelle in tab_tariffe.php (aka the numtariffa1 parameter) due to the mishandling of non-numeric values, as demonstrated by the /tab_tariffe.php?anno=[YEAR]&numtariffa1=1a URI. It could allow an administrator to conduct remote denial of service (disrupting certain business functions of the product). |
| CVE-2019-7156 | In libdoc through 2019-01-28, calcFileBlockOffset in ole.c allows division by zero. |
| CVE-2019-16168 | 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-2019-14443 | An issue was discovered in Libav 12.3. Division by zero in range_decode_culshift in libavcodec/apedec.c allows remote attackers to cause a denial of service (application crash), as demonstrated by avconv. |
| CVE-2019-14284 | In the Linux kernel before 5.2.3, drivers/block/floppy.c allows a denial of service by setup_format_params division-by-zero. Two consecutive ioctls can trigger the bug: the first one should set the drive geometry with .sect and .rate values that make F_SECT_PER_TRACK be zero. Next, the floppy format operation should be called. It can be triggered by an unprivileged local user even when a floppy disk has not been inserted. NOTE: QEMU creates the floppy device by default. |
| CVE-2019-14249 | dwarf_elf_load_headers.c in libdwarf before 2019-07-05 allows attackers to cause a denial of service (division by zero) via an ELF file with a zero-size section group (SHT_GROUP), as demonstrated by dwarfdump. |
| CVE-2019-13454 | ImageMagick 7.0.8-54 Q16 allows Division by Zero in RemoveDuplicateLayers in MagickCore/layer.c. |
| CVE-2019-13390 | In FFmpeg 4.1.3, there is a division by zero at adx_write_trailer in libavformat/rawenc.c. |
| CVE-2019-13218 | Division by zero in the predict_point function in stb_vorbis through 2019-03-04 allows an attacker to cause a denial of service by opening a crafted Ogg Vorbis file. |
| CVE-2018-5816 | An integer overflow error within the "identify()" function (internal/dcraw_common.cpp) in LibRaw versions prior to 0.18.12 can be exploited to trigger a division by zero via specially crafted NOKIARAW file (Note: This vulnerability is caused due to an incomplete fix of CVE-2018-5804). |
| CVE-2018-5804 | A type confusion error within the "identify()" function (internal/dcraw_common.cpp) in LibRaw versions prior to 0.18.8 can be exploited to trigger a division by zero. |
| CVE-2018-20845 | Division-by-zero vulnerabilities in the functions pi_next_pcrl, pi_next_cprl, and pi_next_rpcl in openmj2/pi.c in OpenJPEG through 2.3.0 allow remote attackers to cause a denial of service (application crash). |
| CVE-2018-19872 | An issue was discovered in Qt 5.11. A malformed PPM image causes a division by zero and a crash in qppmhandler.cpp. |
| CVE-2018-18058 | An issue was discovered in Bitdefender Engines before 7.76662. A vulnerability has been discovered in the iso.xmd parser that results from a lack of proper validation of user-supplied data, which can result in a division-by-zero circumstance. Paired with other vulnerabilities, this can result in denial-of-service. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. |
| CVE-2018-17438 | A SIGFPE signal is raised in the function H5D__select_io() of H5Dselect.c in the HDF HDF5 through 1.10.3 library during an attempted parse of a crafted HDF file, because of incorrect protection against division by zero. It could allow a remote denial of service attack. |
| CVE-2018-17434 | A SIGFPE signal is raised in the function apply_filters() of h5repack_filters.c in the HDF HDF5 through 1.10.3 library during an attempted parse of a crafted HDF file, because of incorrect protection against division by zero. It could allow a remote denial of service attack. |
| CVE-2018-17237 | A SIGFPE signal is raised in the function H5D__chunk_set_info_real() of H5Dchunk.c in the HDF HDF5 1.10.3 library during an attempted parse of a crafted HDF file, because of incorrect protection against division by zero. This issue is different from CVE-2018-11207. |
| CVE-2018-17233 | A SIGFPE signal is raised in the function H5D__create_chunk_file_map_hyper() of H5Dchunk.c in the HDF HDF5 through 1.10.3 library during an attempted parse of a crafted HDF file, because of incorrect protection against division by zero. It could allow a remote denial of service attack. |
| CVE-2018-16523 | Amazon Web Services (AWS) FreeRTOS through 1.3.1, FreeRTOS up to V10.0.1 (with FreeRTOS+TCP), and WITTENSTEIN WHIS Connect middleware TCP/IP component allow division by zero in prvCheckOptions. |
| CVE-2018-14423 | Division-by-zero vulnerabilities in the functions pi_next_pcrl, pi_next_cprl, and pi_next_rpcl in lib/openjp3d/pi.c in OpenJPEG through 2.3.0 allow remote attackers to cause a denial of service (application crash). |
| CVE-2018-11207 | A division by zero was discovered in H5D__chunk_init in H5Dchunk.c in the HDF HDF5 1.10.2 library. It could allow a remote denial of service attack. |
| CVE-2018-11203 | A division by zero was discovered in H5D__btree_decode_key in H5Dbtree.c in the HDF HDF5 1.10.2 library. It could allow a remote denial of service attack. |
| CVE-2018-10016 | Netwide Assembler (NASM) 2.14rc0 has a division-by-zero vulnerability in the expr5 function in asm/eval.c via a malformed input file. |
| CVE-2017-18360 | In change_port_settings in drivers/usb/serial/io_ti.c in the Linux kernel before 4.11.3, local users could cause a denial of service by division-by-zero in the serial device layer by trying to set very high baud rates. |
| CVE-2017-14249 | ImageMagick 7.0.6-8 Q16 mishandles EOF checks in ReadMPCImage in coders/mpc.c, leading to division by zero in GetPixelCacheTileSize in MagickCore/cache.c, allowing remote attackers to cause a denial of service via a crafted file. |
| CVE-2017-11720 | There is a division-by-zero vulnerability in LAME 3.99.5, caused by a malformed input file. |
| CVE-2017-11464 | A SIGFPE is raised in the function box_blur_line of rsvg-filter.c in GNOME librsvg 2.40.17 during an attempted parse of a crafted SVG file, because of incorrect protection against division by zero. |
| CVE-2017-1000414 | ImpulseAdventure JPEGsnoop version 1.7.5 is vulnerable to a division by zero in the JFIF decode handling resulting denial of service. |
| CVE-2016-2070 | The tcp_cwnd_reduction function in net/ipv4/tcp_input.c in the Linux kernel before 4.3.5 allows remote attackers to cause a denial of service (divide-by-zero error and system crash) via crafted TCP traffic. |
| CVE-2016-10506 | Division-by-zero vulnerabilities in the functions opj_pi_next_cprl, opj_pi_next_pcrl, and opj_pi_next_rpcl in pi.c in OpenJPEG before 2.2.0 allow remote attackers to cause a denial of service (application crash) via crafted j2k files. |
| CVE-2014-0158 | Heap-based buffer overflow in the JPEG2000 image tile decoder in OpenJPEG before 1.5.2 allows remote attackers to cause a denial of service (application crash) or possibly have unspecified other impact via a crafted file because of incorrect j2k_decode, j2k_read_eoc, and tcd_decode_tile interaction, a related issue to CVE-2013-6045. NOTE: this is not a duplicate of CVE-2013-1447, because the scope of CVE-2013-1447 was specifically defined in http://openwall.com/lists/oss-security/2013/12/04/6 as only "null pointer dereferences, division by zero, and anything that would just fit as DoS." |
| CVE-2013-6887 | OpenJPEG 1.5.1 allows remote attackers to cause a denial of service via unspecified vectors that trigger NULL pointer dereferences, division-by-zero, and other errors. |
| CVE-2013-4207 | Buffer overflow in sshbn.c in PuTTY before 0.63 allows remote SSH servers to cause a denial of service (crash) via an invalid DSA signature that is not properly handled during computation of a modular inverse and triggers the overflow during a division by zero by the bignum functionality, a different vulnerability than CVE-2013-4206. |
| CVE-2013-1447 | OpenJPEG 1.3 and earlier allows remote attackers to cause a denial of service (memory consumption or crash) via unspecified vectors related to NULL pointer dereferences, division-by-zero, and other errors. |
| CVE-2009-4835 | The (1) htk_read_header, (2) alaw_init, (3) ulaw_init, (4) pcm_init, (5) float32_init, and (6) sds_read_header functions in libsndfile 1.0.20 allow context-dependent attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted audio file. |
| CVE-2009-4307 | The ext4_fill_flex_info function in fs/ext4/super.c in the Linux kernel before 2.6.32-git6 allows user-assisted remote attackers to cause a denial of service (divide-by-zero error and panic) via a malformed ext4 filesystem containing a super block with a large FLEX_BG group size (aka s_log_groups_per_flex value). |
| CVE-2007-3126 | Gimp before 2.8.22 allows context-dependent attackers to cause a denial of service (crash) via an ICO file with an InfoHeader containing a Height of zero, a similar issue to CVE-2007-2237. |
| CVE-2007-2237 | Microsoft Windows Graphics Device Interface (GDI+, GdiPlus.dll) allows context-dependent attackers to cause a denial of service (crash) via an ICO file with an InfoHeader containing a Height of zero, which triggers a divide-by-zero error. |
| CVE-2006-4066 | The Graphical Device Interface Plus library (gdiplus.dll) in Microsoft Windows XP SP2 allows context-dependent attackers to cause a denial of service (application crash) via certain images that trigger a divide-by-zero error, as demonstrated by a (1) .ico file, (2) .png file that crashes MSN Messenger, and (3) .jpg file that crashes Internet Explorer. NOTE: another researcher has not been able to reproduce this issue. |
| CVE-2005-0998 | The Web_Links module for PHP-Nuke 7.6 allows remote attackers to obtain sensitive information via an invalid show parameter, which triggers a division by zero PHP error that leaks the full pathname of the server. |
|
You can also search by reference using the CVE Reference Maps.
For More Information: CVE Request Web Form (select “Other” from dropdown)
|
||
