TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation(https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer). 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.

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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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.

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.

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.

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.

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.

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.

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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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.

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.

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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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.

Heap buffer overflow in Tab Groups in Google Chrome prior to 126.0.6478.54 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Type Confusion in V8 in Google Chrome prior to 126.0.6478.54 allowed a remote attacker to perform an out of bounds memory write via a crafted HTML page. (Chromium security severity: High)

Out of bounds access in SwiftShader in Google Chrome prior to 73.0.3683.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In ppcfw_deny_sec_dram_access of ppcfw.c, there is a possible arbitrary read from TEE memory due to a logic error in the code. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.

Out of bounds write in Streams API in Google Chrome prior to 125.0.6422.141 allowed a remote attacker to execute arbitrary code inside a sandbox via a crafted HTML page. (Chromium security severity: High)

In closeChannel of secureelementimpl.cpp, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

In static long dev_send of tipc_dev_ql, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

In all Android releases from CAF using the Linux kernel, a kernel driver has an off-by-one buffer over-read vulnerability.

In a camera driver function in all Android releases from CAF using the Linux kernel, a bounds check is missing when writing into an array potentially leading to an out-of-bounds heap write.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, while processing a specially crafted cfg80211 vendor command, a buffer over-read can occur.

Out of bounds access in SwiftShader in Google Chrome prior to 73.0.3683.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Heap buffer overflow in ANGLE in Google Chrome on Windows prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient input validation in WebGL in Google Chrome prior to 72.0.3626.81 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page.

In all Qualcomm products with Android releases from CAF using the Linux kernel, in a driver function, a value from userspace is not properly validated potentially leading to an out of bounds heap write.

In all Qualcomm products with Android releases from CAF using the Linux kernel, due to a type downcast, a value may improperly pass validation and cause an out of bounds write later.

Out of bounds read in Skia in Google Chrome prior to 75.0.3770.80 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page.

Integer overflow in PDFium in Google Chrome prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

Integer overflow in PDFium in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

In Android before the 2018-06-05 security patch level, NVIDIA TLZ TrustZone contains a possible out of bounds write due to integer overflow which could lead to local escalation of privilege in the TrustZone with no additional execution privileges needed. User interaction is not needed for exploitation. This issue is rated as high. Version: N/A. Android: A-69480285. Reference: N-CVE-2017-6292.

Insufficient checks of pointer validity in WebRTC in Google Chrome prior to 72.0.3626.81 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

NVIDIA libnvrm contains a possible out of bounds read due to a missing bounds check which could lead to local information disclosure. This issue is rated as moderate. Product: Android. Version: N/A. Android: A-64893156. Reference: N-CVE-2017-6285.

NVIDIA driver contains a possible out-of-bounds read vulnerability due to a leak which may lead to information disclosure. This issue is rated as moderate. Android: A-63851980.

Integer overflow in ANGLE in Google Chrome on Windows prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In Android before the 2018-06-05 security patch level, NVIDIA Tegra X1 TZ contains a possible out of bounds write due to missing bounds check which could lead to escalation of privilege from the kernel to the TZ. User interaction is not needed for exploitation. This issue is rated as high. Version: N/A. Android: A-69316825. Reference: N-CVE-2017-6294.

NVIDIA libnvmmlite_audio.so contains an elevation of privilege vulnerability when running in media server which may cause an out of bounds write and could lead to local code execution in a privileged process. This issue is rated as high. Product: Android. Version: N/A. Android: A-65023166. Reference: N-CVE-2017-6279.

Integer overflow in PDFium in Google Chrome prior to 76.0.3809.87 allowed a remote attacker to potentially exploit heap corruption via a crafted PDF file.

Out of bounds memory access in JavaScript in Google Chrome prior to 77.0.3865.75 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

NVIDIA libnvomx contains a possible out of bounds write due to a missing bounds check which could lead to local escalation of privilege. This issue is rated as high. Product: Android. Version: N/A. Android: A-64893247. Reference: N-CVE-2017-6286.

Heap buffer overflow in Dawn in Google Chrome prior to 125.0.6422.76 allowed a remote attacker to perform an out of bounds memory write via a crafted HTML page. (Chromium security severity: High)

NVIDIA libnvrm contains a possible out of bounds read due to a missing bounds check which could lead to local information disclosure. This issue is rated as moderate. Product: Android. Version: N/A. Android: A-65482562. Reference: N-CVE-2017-6288.

Heap buffer overflow in ANGLE in Google Chrome prior to 125.0.6422.76 allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page. (Chromium security severity: High)

Use after free in V8 in Google Chrome prior to 74.0.3729.108 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In Android before the 2018-05-05 security patch level, NVIDIA Tegra X1 TZ contains a vulnerability in Widevine TA where the software writes data past the end, or before the beginning, of the intended buffer, which may lead to escalation of Privileges. This issue is rated as high. Android: A-69377364. Reference: N-CVE-2017-6293.

Heap buffer overflow in V8 Internationalization in Google Chrome prior to 101.0.4951.64 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

Insufficient validation of untrusted input in Skia in Google Chrome prior to 59.0.3071.86 for Linux, Windows, and Mac, and 59.0.3071.92 for Android, allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page.