In all Qualcomm products with Android releases from CAF using the Linux kernel, a NULL pointer can be dereferenced during GAL decoding.

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

In all Android releases from CAF using the Linux kernel, an untrusted pointer dereference vulnerability exists in the unlocking of memory.

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. 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 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.

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 in the implementation of `tf.raw_ops.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. 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. 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.

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.

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.

In the cpuidle driver in all Android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the Linux kernel, the list_for_each macro was not used correctly which could lead to an untrusted pointer dereference.

A vulnerability in mfc driver prior to SMR Oct-2021 Release 1 allows memory corruption via NULL-pointer dereference.

NULL pointer dereference vulnerability in ION driver prior to SMR Sep-2021 Release 1 allows attackers to cause memory corruption.

NULL pointer dereference vulnerability in NPU driver prior to SMR Sep-2021 Release 1 allows attackers to cause memory corruption.

In soter service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

The WebSockets implementation in Google Chrome before 6.0.472.53 allows remote attackers to cause a denial of service (NULL pointer dereference and application crash) via unspecified vectors.

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.

In DRM service, there is a possible system crash due to null pointer dereference. This could lead to local denial of service with System execution privileges needed.

Google TensorFlow 1.6.x and earlier is affected by: Null Pointer Dereference. The type of exploitation is: context-dependent.

In multiple locations, there is a possible permissions bypass 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.

A nullptr dereference in WebAssembly in Google Chrome prior to 66.0.3359.117 allowed a remote attacker to potentially perform out of bounds memory access via a crafted HTML page.

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).

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

In TrustZone in all Android releases from CAF using the Linux kernel, an Untrusted Pointer Dereference vulnerability could potentially exist.

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, disabling asserts can potentially cause a NULL pointer dereference during an out-of-memory condition.

In ss_SendCallBarringPwdRequiredIndMsg of ss_CallBarring.c, there is a possible null pointer deref 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.

In Qualcomm Android for MSM, Firefox OS for MSM, and QRD Android with all Android releases from CAF using the Linux kernel before security patch level 2018-04-05, untrusted pointer dereference in apr_cb_func can lead to an arbitrary code execution.

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 open source platform for machine learning. When `mlir::tfg::TFOp::nameAttr` receives null type list attributes, it crashes. We have patched the issue in GitHub commits 3a754740d5414e362512ee981eefba41561a63a6 and a0f0b9a21c9270930457095092f558fbad4c03e5. 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.

TensorFlow is an open source platform for machine learning. When `mlir::tfg::ConvertGenericFunctionToFunctionDef` is given empty function attributes, it gives a null dereference. We have patched the issue in GitHub commit 1cf45b831eeb0cab8655c9c7c5d06ec6f45fc41b. 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.

TensorFlow is an open source platform for machine learning. When `mlir::tfg::GraphDefImporter::ConvertNodeDef` tries to convert NodeDefs without an op name, it crashes. We have patched the issue in GitHub commit a0f0b9a21c9270930457095092f558fbad4c03e5. 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.

TensorFlow is an open source platform for machine learning. If `LowerBound` or `UpperBound` is given an empty`sorted_inputs` input, it results in a `nullptr` dereference, leading to a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea. 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.

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. 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.

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.

NVIDIA SHIELD TV, all versions prior to 8.2.2, contains a vulnerability in the NVHost function, which may lead to abnormal reboot due to a null pointer reference, causing data loss.

In btif_in_hf_client_generic_evt of btif_hf_client.cc, there is a possible Bluetooth service 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-12Android ID: A-180420059

`NSC_DeriveKey` inadvertently assumed that the `phKey` parameter is always non-NULL. When it was passed as NULL, a segmentation fault (SEGV) occurred, leading to crashes. This behavior conflicted with the PKCS#11 v3.0 specification, which allows `phKey` to be NULL for certain mechanisms. This vulnerability affects Firefox < 133 and Thunderbird < 133.

Possible null-pointer dereference can occur while parsing mp4 clip with corrupted sample table atoms in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8017, APQ8053, APQ8096AU, APQ8098, Kamorta, MDM9206, MDM9207C, MDM9607, MSM8905, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, QCA6574AU, QCS405, QCS605, QM215, Rennell, Saipan, SDA660, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDX20, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

In strongSwan before 5.9.5, a malicious responder can send an EAP-Success message too early without actually authenticating the client and (in the case of EAP methods with mutual authentication and EAP-only authentication for IKEv2) even without server authentication.

Joda Time v2.12.5 was discovered to contain a NullPointerException via the component org.joda.time.format.PeriodFormat::wordBased(Locale). NOTE: this is disputed by multiple third parties who believe there was not reasonable evidence to determine the existence of a vulnerability. The submission may have been based on a tool that is not sufficiently robust for vulnerability identification.

An issue in coap_msg.c in Keith Cullen's FreeCoAP v.0.7 allows remote attackers to cause a Denial of Service or potentially disclose information via a specially crafted packet.

In the Linux kernel, the following vulnerability has been resolved: wifi: wilc1000: fix RCU usage in connect path With lockdep enabled, calls to the connect function from cfg802.11 layer lead to the following warning: ============================= WARNING: suspicious RCU usage 6.7.0-rc1-wt+ #333 Not tainted ----------------------------- drivers/net/wireless/microchip/wilc1000/hif.c:386 suspicious rcu_dereference_check() usage! [...] stack backtrace: CPU: 0 PID: 100 Comm: wpa_supplicant Not tainted 6.7.0-rc1-wt+ #333 Hardware name: Atmel SAMA5 unwind_backtrace from show_stack+0x18/0x1c show_stack from dump_stack_lvl+0x34/0x48 dump_stack_lvl from wilc_parse_join_bss_param+0x7dc/0x7f4 wilc_parse_join_bss_param from connect+0x2c4/0x648 connect from cfg80211_connect+0x30c/0xb74 cfg80211_connect from nl80211_connect+0x860/0xa94 nl80211_connect from genl_rcv_msg+0x3fc/0x59c genl_rcv_msg from netlink_rcv_skb+0xd0/0x1f8 netlink_rcv_skb from genl_rcv+0x2c/0x3c genl_rcv from netlink_unicast+0x3b0/0x550 netlink_unicast from netlink_sendmsg+0x368/0x688 netlink_sendmsg from ____sys_sendmsg+0x190/0x430 ____sys_sendmsg from ___sys_sendmsg+0x110/0x158 ___sys_sendmsg from sys_sendmsg+0xe8/0x150 sys_sendmsg from ret_fast_syscall+0x0/0x1c This warning is emitted because in the connect path, when trying to parse target BSS parameters, we dereference a RCU pointer whithout being in RCU critical section. Fix RCU dereference usage by moving it to a RCU read critical section. To avoid wrapping the whole wilc_parse_join_bss_param under the critical section, just use the critical section to copy ies data

JGraphT Core v1.5.2 was discovered to contain a NullPointerException via the component org.jgrapht.alg.util.ToleranceDoubleComparator::compare(Double, Double). NOTE: this is disputed by multiple third parties who believe there was not reasonable evidence to determine the existence of a vulnerability. The submission may have been based on a tool that is not sufficiently robust for vulnerability identification.