In FT_ACDK_CCT_V2_OP_ISP_SET_TUNING_PARAS of Meta_CCAP_Para.cpp, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In CryptoPlugin::decrypt of CryptoPlugin.cpp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In store_upgrade and store_cmd of drivers/input/touchscreen/stm/ftm4_pdc.c, there are out of bound writes due to missing bounds checks or integer underflows. These could lead to escalation of privilege.

In IMSA_Recv_Thread and VT_IMCB_Thread of ImsaClient.cpp and VideoTelephony.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In gattServerSendResponseNative of com_android_bluetooth_gatt.cpp, there is a possible out of bounds stack write due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

In HID_DevAddRecord of hidd_api.cc, there is a possible out-of-bounds write due to a missing bounds check. This could lead to local escalation of privilege in the Bluetooth service with User execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-9. Android ID: A-79946737.

In ResStringPool::setTo of ResourceTypes.cpp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In persist_set_key and other functions of cryptfs.cpp, there is a possible out-of-bounds write due to an uncaught error. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android-7.0 Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9. Android ID: A-112731440.

In ip6_append_data of ip6_output.c, there is a possible way to achieve code execution due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In task_get_unused_fd_flags of binder.c, there is a possible memory corruption due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android kernel Android ID: A-69164715 References: Upstream kernel.

In cmd_flash_mmc_sparse_img of dl_commands.c, there is a possible out of bounds write due to a missing bounds check. This could lead to a local escalation of privilege in the bootloader with no additional execution privileges needed. User interaction is not needed for exploitation.

In several native functions called by AdvertiseManager.java, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege in the Bluetooth server with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-171400004

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, in the kernel IPA driver, a Use After Free condition can occur.

A stack-based buffer overflow can occur in fastboot from all Android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the Linux kernel.

An issue was discovered on Samsung mobile devices with JBP(4.2) and KK(4.4) (Marvell chipsets) software. The ACIPC-MSOCKET driver allows local privilege escalation via a stack-based buffer overflow. The Samsung ID is SVE-2016-5393 (April 2016).

In the WCD CPE codec, a Use After Free condition can occur in all Android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the Linux kernel.

In all android releases(Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, while list traversal in LPM status driver for clean up, use after free vulnerability may occur.

In the video driver function set_output_buffers(), binfo can be accessed after being freed in a failure scenario in all Android releases from CAF (Android for MSM, Firefox OS for MSM, QRD Android) using the Linux Kernel.

Early or late retirement of rotation requests can result in a Use After Free condition in all Android releases from CAF (Android for MSM, Firefox OS for MSM, QRD Android) using the Linux Kernel.

In the KGSL driver in all Android releases from CAF (Android for MSM, Firefox OS for MSM, QRD Android) using the Linux Kernel, a Use After Free condition can occur when printing information about sparse memory allocations

In RGXFWChangeOSidPriority of rgxfwutils.c, there is a possible arbitrary code execution due to a missing bounds check. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05, whenever TDLS connection is setup, we are freeing the netbuf in ol_tx_completion_handler and after that, we are accessing it in NBUF_UPDATE_TX_PKT_COUNT causing a use after free.

In RGXCreateZSBufferKM of rgxta3d.c, there is a possible arbitrary code execution due to a use after free. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In RGXCreateHWRTData_aux of rgxta3d.c, there is a possible arbitrary code execution due to a use after free. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In the FastRPC driver in Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05, a Use After Free condition can occur when mapping on the remote processor fails.

In PMRWritePMPageList of pmr.c, there is a possible out of bounds write due to a logic error in the code. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Out-of-bound Write vulnerability in chunk parsing implementation of libsdffextractor prior to SMR Apr-2023 Release 1 allows local attackers to execute arbitrary code.

In vdec, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08932916; Issue ID: MSV-1551.

In vdec, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08932916; Issue ID: MSV-1550.

In vdec, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09028313; Issue ID: MSV-1700.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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.

In wlan service, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08572601; Issue ID: MSV-1229.

In onQueueFilled of SoftMPEG4.cpp, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In convertYUV420Planar16ToY410 of ColorConverter.cpp, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In getConfig of SoftVideoDecoderOMXComponent.cpp, there is a possible out of bounds write due to a missing validation check. This could lead to a local non-security issue with no additional execution privileges needed. User interaction is not needed for exploitation.

In ConvertRGBToPlanarYUV of Codec2BufferUtils.cpp, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple locations, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA Linux distributions contain a vulnerability in nvmap ioctl, which allows any user with a local account to exploit a use-after-free condition, leading to code privilege escalation, loss of confidentiality and integrity, or denial of service.

NVIDIA Tegra kernel driver contains a vulnerability in NVIDIA NVDEC, where a user with high privileges might be able to read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service, Information disclosure, loss of Integrity, or possible escalation of privileges.

In DevmemIntUnmapPMR of devicemem_server.c, there is a possible arbitrary code execution due to a use after free. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In bta_av_rc_disc_done of bta_av_act.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-226927612

TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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 heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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 CCCI, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06641673; Issue ID: ALPS06641653.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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 validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.

Use after free in Browser UI in Google Chrome on Chrome prior to 92.0.4515.131 allowed a remote attacker to potentially exploit heap corruption via physical access to the device.