In TBD of TBD, there is a possible out of bounds read due to TBD. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-206039140References: N/A

In extract of MediaMetricsItem.h, there is a possible out of bounds read due to improper input validation. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12Android ID: A-204445255

In ffu_flash_pack of ffu.c, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.

Out of bounds memory access in V8 in Google Chrome prior to 144.0.7559.59 allowed a remote attacker to potentially exploit object corruption via a crafted HTML page. (Chromium security severity: High)

A flaw was found in the vhost library in DPDK. Function vhost_user_set_inflight_fd() does not validate `msg->payload.inflight.num_queues`, possibly causing out-of-bounds memory read/write. Any software using DPDK vhost library may crash as a result of this vulnerability.

Wasmtime is an open source runtime for WebAssembly & WASI. In Wasmtime from version 0.26.0 and before version 0.30.0 is affected by a memory unsoundness vulnerability. There was an invalid free and out-of-bounds read and write bug when running Wasm that uses `externref`s in Wasmtime. To trigger this bug, Wasmtime needs to be running Wasm that uses `externref`s, the host creates non-null `externrefs`, Wasmtime performs a garbage collection (GC), and there has to be a Wasm frame on the stack that is at a GC safepoint where there are no live references at this safepoint, and there is a safepoint with live references earlier in this frame's function. Under this scenario, Wasmtime would incorrectly use the GC stack map for the safepoint from earlier in the function instead of the empty safepoint. This would result in Wasmtime treating arbitrary stack slots as `externref`s that needed to be rooted for GC. At the *next* GC, it would be determined that nothing was referencing these bogus `externref`s (because nothing could ever reference them, because they are not really `externref`s) and then Wasmtime would deallocate them and run `<ExternRef as Drop>::drop` on them. This results in a free of memory that is not necessarily on the heap (and shouldn't be freed at this moment even if it was), as well as potential out-of-bounds reads and writes. Even though support for `externref`s (via the reference types proposal) is enabled by default, unless you are creating non-null `externref`s in your host code or explicitly triggering GCs, you cannot be affected by this bug. We have reason to believe that the effective impact of this bug is relatively small because usage of `externref` is currently quite rare. This bug has been patched and users should upgrade to Wasmtime version 0.30.0. If you cannot upgrade Wasmtime at this time, you can avoid this bug by disabling the reference types proposal by passing `false` to `wasmtime::Config::wasm_reference_types`.

In startWpsPbcInternal of sta_iface.cpp, there is a possible out of bounds read due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-262246082

A use after free in printing in Google Chrome prior to 57.0.2987.133 for Linux and Windows allowed a remote attacker to perform an out of bounds memory read via a crafted HTML page.

In multiple locations of p2p_iface.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-262236313

In MessageQueueBase of MessageQueueBase.h, there is a possible out of bounds read 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.Product: AndroidVersions: Android-13Android ID: A-247092734

In EUTRAN_LCS_DecodeFacilityInformationElement of LPP_LcsManagement.c, there is a possible out of bounds read due to a missing bounds check. This could lead to remote information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-247564044References: N/A

A crafted NTFS image can cause an out-of-bounds read in ntfs_ie_lookup in NTFS-3G < 2021.8.22.

In wlan, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07573552; Issue ID: ALPS07573552.

A crafted NTFS image can cause an out-of-bounds read in ntfs_runlists_merge_i in NTFS-3G < 2021.8.22.

Out of bounds read in audio in Google Chrome prior to 86.0.4240.75 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page.

An out-of-bounds read in V8 in Google Chrome prior to 57.0.2987.133 for Linux, Windows, and Mac, and 57.0.2987.132 for Android, allowed a remote attacker to obtain heap memory contents via a crafted HTML page.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. 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.

An out-of-bounds (OOB) memory read flaw was found in the Qualcomm IPC router protocol in the Linux kernel. A missing sanity check allows a local attacker to gain access to out-of-bounds memory, leading to a system crash or a leak of internal kernel information. The highest threat from this vulnerability is to system availability.

Out of bounds read in ANGLE allowed a remote attacker to obtain sensitive data via a crafted HTML page.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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.

Out of bounds read in WebAudio in Google Chrome prior to 95.0.4638.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

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.

Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to print the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service, if they can trick the victim into running Exiv2 on a crafted image file. Note that this bug is only triggered when printing the image ICC profile, which is a less frequently used Exiv2 operation that requires an extra command line option (`-p C`). The bug is fixed in version v0.27.5.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. 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.

Out of bounds read in networking in Google Chrome prior to 87.0.4280.88 allowed a remote attacker who had compromised the renderer process to obtain potentially sensitive information from process memory via a crafted HTML page.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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.

Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to write metadata into a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service by crashing Exiv2, if they can trick the victim into running Exiv2 on a crafted image file. Note that this bug is only triggered when writing the metadata, which is a less frequently used Exiv2 operation than reading the metadata. For example, to trigger the bug in the Exiv2 command-line application, you need to add an extra command-line argument such as insert. The bug is fixed in version v0.27.5.

Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to read the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service, if they can trick the victim into running Exiv2 on a crafted image file. The bug is fixed in version v0.27.5.

An issue was discovered in Adobe Flash Player 27.0.0.183 and earlier versions. This vulnerability occurs as a result of a computation that reads data that is past the end of the target buffer; the computation is part of providing language- and region- or country- specific functionality. The use of an invalid (out-of-range) pointer offset during access of internal data structure fields causes the vulnerability. A successful attack can lead to sensitive data exposure.

Out of bounds read in libjpeg-turbo in Google Chrome prior to 94.0.4606.54 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page.

In crus_afe_get_param of msm-cirrus-playback.c, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation.Product: Android. Versions: Android kernel. Android ID: A-139354541

TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

TensorFlow is an open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.

A flaw was found in the ptp4l program of the linuxptp package. When ptp4l is operating on a little-endian architecture as a PTP transparent clock, a remote attacker could send a crafted one-step sync message to cause an information leak or crash. The highest threat from this vulnerability is to data confidentiality and system availability. This flaw affects linuxptp versions before 3.1.1 and before 2.0.1.

The CIL compiler in SELinux 3.2 has a heap-based buffer over-read in ebitmap_match_any (called indirectly from cil_check_neverallow). This occurs because there is sometimes a lack of checks for invalid statements in an optional block.

In MediaInfoLib in MediaArea MediaInfo 20.03, there is a stack-based buffer over-read in Streams_Fill_PerStream in Multiple/File_MpegPs.cpp (aka an off-by-one during MpegPs parsing).

In heap of spaces.h, there is a possible out of bounds read due to improper input validation. This could lead to remote information disclosure when processing a proxy auto config file 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-117555811

TensorFlow is an open source platform for machine learning. Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad. A fix is included in TensorFlow 2.12.0 and 2.11.1.

A heap-based buffer overflow vulnerability was found in ImageMagick in versions prior to 7.0.11-14 in ReadTIFFImage() in coders/tiff.c. This issue is due to an incorrect setting of the pixel array size, which can lead to a crash and segmentation fault.

An issue was discovered in Adobe Flash Player 27.0.0.183 and earlier versions. This vulnerability occurs as a result of a computation that reads data that is past the end of the target buffer; the computation is part of AdobePSDK metadata. The use of an invalid (out-of-range) pointer offset during access of internal data structure fields causes the vulnerability. A successful attack can lead to sensitive data exposure.

It was discovered that the eBPF implementation in the Linux kernel did not properly track bounds information for 32 bit registers when performing div and mod operations. A local attacker could use this to possibly execute arbitrary code.

In wlan service, there is a possible out of bounds read due to improper input validation. This could lead to local information disclosure with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07588360; Issue ID: ALPS07588360.

A carefully crafted request uri-path can cause mod_proxy_uwsgi to read above the allocated memory and crash (DoS). This issue affects Apache HTTP Server versions 2.4.30 to 2.4.48 (inclusive).

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.

A flaw was found in the hivex library in versions before 1.3.20. It is caused due to a lack of bounds check within the hivex_open function. An attacker could input a specially crafted Windows Registry (hive) file which would cause hivex to read memory beyond its normal bounds or cause the program to crash. The highest threat from this vulnerability is to system availability.

Ming 0.4.8 has an out-of-bounds buffer access issue in the function decompileINCR_DECR() in decompiler.c file that causes a direct segmentation fault and leads to denial of service.