In all Android releases from CAF using the Linux kernel, an untrusted pointer dereference vulnerability exists in WideVine DRM.

In all Qualcomm products with Android releases from CAF using the Linux kernel, an untrusted pointer dereference can occur in a TrustZone syscall.

A remote denial of service vulnerability in HevcUtils.cpp in libstagefright in Mediaserver could enable an attacker to use a specially crafted file to cause a device hang or reboot. This issue is rated as Low due to details specific to the vulnerability. Product: Android. Versions: 7.0, 7.1.1, 7.1.2. Android ID: A-35467107.

An elevation of privilege vulnerability in SurfaceFlinger could enable a local malicious application to execute arbitrary code within the context of a privileged process. This issue is rated as High because it could be used to gain local access to elevated capabilities, which are not normally accessible to a third-party application. Product: Android. Versions: 4.4.4, 5.0.2, 5.1.1, 6.0, 6.0.1, 7.0, 7.1.1. Android ID: A-32628763.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where a local user with basic capabilities can cause a null-pointer dereference, which may lead to denial of service.

All versions of the NVIDIA Windows GPU Display Driver contain a vulnerability in the kernel mode layer (nvlddmkm.sys) handler where a NULL pointer dereference may lead to denial of service or potential escalation of privileges.

In line libavcodec/h264dec.c:500 in libav(v13_dev0), ffmpeg(n3.4), chromium(56 prior Feb 13, 2017), the return value of init_get_bits is ignored and get_ue_golomb(&gb) is called on an uninitialized get_bits context, which causes a NULL deref exception.

A denial of service vulnerability in the Android media framework. Product: Android. Versions: 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2. Android ID: A-34231231.

All versions of the NVIDIA GPU Display Driver contain a vulnerability in the kernel mode layer handler where a NULL pointer dereference caused by invalid user input may lead to denial of service or potential escalation of privileges.

The updateMessageStatus function in Android 5.1.1 and earlier allows local users to cause a denial of service (NULL pointer exception and process crash).

All versions of NVIDIA Windows GPU Display Driver contain a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape where an attempt to access an invalid object pointer may lead to denial of service or potential escalation of privileges.

NVIDIA GPU Display Driver for Linux contains a vulnerability in the kernel mode layer handler, where an unhandled return value can lead to a null-pointer dereference, which may lead to denial of service.

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.

All versions of NVIDIA Windows GPU Display Driver contain a vulnerability in the kernel mode layer handler where a NULL pointer dereference caused by invalid user input may lead to denial of service or potential escalation of privileges.

All versions of NVIDIA Windows GPU Display Driver contain a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgDdiEscape where multiple pointers are used without checking for NULL, leading to denial of service or potential escalation of privileges.

there is a possible way to crash the modem due to a missing null check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

NVIDIA Display Driver for Linux contains a vulnerability in the Virtual GPU Manager, where it does not check the return value from a null-pointer dereference, which may lead to denial of service.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged user can cause a null-pointer dereference, which may lead to denial of service.

TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. 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.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where a null-pointer dereference occurs, which may lead to denial of service.

TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where a local user with basic capabilities can cause a null-pointer dereference, which may lead to denial of service.

For the NVIDIA Quadro, NVS, and GeForce products, there is a Remote Desktop denial of service. A successful exploit of a vulnerable system will result in a kernel null pointer dereference, causing a blue screen crash.

In all Android releases from CAF using the Linux kernel, an untrusted pointer dereference vulnerability exists in WideVine DRM.

In Core Kernel in all Android releases from CAF using the Linux kernel, a Null Pointer Dereference vulnerability could potentially exist.

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. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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 TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. 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.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. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. 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 by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.

libmedia in mediaserver in Android 4.x before 4.4.4, 5.0.x before 5.0.2, 5.1.x before 5.1.1, and 6.x before 2016-08-01 has certain incorrect declarations, which allows remote attackers to execute arbitrary code or cause a denial of service (NULL pointer dereference or memory corruption) via a crafted media file, aka internal bug 28166152.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in `ngrams_data`(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. 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.

In cd_SsParseMsg of cd_SsCodec.c, there is a possible crash due to a missing null check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-244448906References: N/A

there is a possible Null Pointer Dereference (modem crash) due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. 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.

b/libs/gui/ISurfaceComposer.cpp in Android allows attackers to trigger a denial of service (null pointer dereference and process crash).

TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape. 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.

cmds/servicemanager/service_manager.c in Android before commit 7d42a3c31ba78a418f9bdde0e0ab951469f321b5 allows attackers to cause a denial of service (NULL pointer dereference, or out-of-bounds write) via vectors related to binder passed lengths.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it can dereference a null pointer, which may lead to denial of service.

NVIDIA GPU Display Driver for Linux contains a vulnerability in the kernel mode layer, where a local user with basic capabilities can cause a null-pointer dereference, which may lead to denial of service.

In onNullBinding of TileLifecycleManager.java, there is a possible way to launch an activity from the background due to a missing null check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA vGPU software for Linux contains a vulnerability where the software can dereference a NULL pointer. A successful exploit of this vulnerability might lead to denial of service and undefined behavior in the vGPU plugin.

NVIDIA CUDA toolkit for all platforms contains a vulnerability in cuobjdump and nvdisasm where an attacker may cause a crash by tricking a user into reading a malformed ELF file. A successful exploit of this vulnerability may lead to a partial denial of service.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer, where any local user can cause a null-pointer dereference, which may lead to a kernel panic.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where a user in a guest can cause a NULL-pointer dereference in the host, which may lead to denial of service.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

NVIDIA CUDA Toolkit for Windows and Linux contains a vulnerability in the nvdisam command line tool, where a user can cause a NULL pointer dereference by running nvdisasm on a malformed ELF file. A successful exploit of this vulnerability might lead to a limited denial of service.