TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
NXP LPC55S69 devices before A3 have a buffer over-read via a crafted wlength value in a GET Descriptor Configuration request during use of USB In-System Programming (ISP) mode. This discloses protected flash memory.
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 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.
In the PatternMatch function in fontfile/fontdir.c in libXfont through 1.5.2 and 2.x before 2.0.2, an attacker with access to an X connection can cause a buffer over-read during pattern matching of fonts, leading to information disclosure or a crash (denial of service). This occurs because '\0' characters are incorrectly skipped in situations involving ? characters.
An out-of-bounds array read in the apr_time_exp*() functions was fixed in the Apache Portable Runtime 1.6.3 release (CVE-2017-12613). The fix for this issue was not carried forward to the APR 1.7.x branch, and hence version 1.7.0 regressed compared to 1.6.3 and is vulnerable to the same issue.
Possible out of bound read due to lack of length check of data length for a DIAG event in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music
Information disclosure while processing information on firmware image during core initialization.
Possible buffer overflow due to lack of buffer length check during management frame Rx handling in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile
Information disclosure while processing IO control commands.
Possible buffer over read due to improper buffer allocation for file length passed from user space in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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 `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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 implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. 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` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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 heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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 access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. 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.
A information disclosure vulnerability in the Upstream kernel encrypted-keys. Product: Android. Versions: Android kernel. Android ID: A-70526974.
In the pcfGetProperties function in bitmap/pcfread.c in libXfont through 1.5.2 and 2.x before 2.0.2, a missing boundary check (for PCF files) could be used by local attackers authenticated to an Xserver for a buffer over-read, for information disclosure or a crash of the X server.
When apr_time_exp*() or apr_os_exp_time*() functions are invoked with an invalid month field value in Apache Portable Runtime APR 1.6.2 and prior, out of bounds memory may be accessed in converting this value to an apr_time_exp_t value, potentially revealing the contents of a different static heap value or resulting in program termination, and may represent an information disclosure or denial of service vulnerability to applications which call these APR functions with unvalidated external input.
Information disclosure when the ADSP payload size received in HLOS in response to Audio Stream Manager matrix session is less than this expected size.
NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape where an out of bounds array access may lead to denial of service or information disclosure.
information disclosure while invoking the mailbox read API.
An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The Wi-Fi kernel drivers have an out-of-bounds Read. The Samsung IDs are SVE-2019-15692, SVE-2019-15693 (December 2019).
In the Linux kernel 5.0.0-rc7 (as distributed in ubuntu/linux.git on kernel.ubuntu.com), mounting a crafted f2fs filesystem image and performing some operations can lead to slab-out-of-bounds read access in ttm_put_pages in drivers/gpu/drm/ttm/ttm_page_alloc.c. This is related to the vmwgfx or ttm module.
An out-of-bounds read in the vrend_blit_need_swizzle function in vrend_renderer.c in virglrenderer through 0.8.0 allows guest OS users to cause a denial of service via VIRGL_CCMD_BLIT commands.
A flaw was found in the Netfilter subsystem in the Linux kernel. The sctp_mt_check did not validate the flag_count field. This flaw allows a local privileged (CAP_NET_ADMIN) attacker to trigger an out-of-bounds read, leading to a crash or information disclosure.
When attempting to create a new XFRM policy, a stack out-of-bounds read will occur if the user provides a template where the mode is set to a value that does not resolve to a valid XFRM mode in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8053, APQ8096AU, APQ8098, IPQ4019, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, MSM8996AU, QCA4531, QCN7605, QCS605, QM215, SA415M, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM845, SDX20, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR2130
Out of bounds read can happen in diag event set mask command handler when user provided length in the command request is less than expected length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096, APQ8096AU, APQ8098, Kamorta, MDM9150, MDM9205, MDM9206, MDM9607, MDM9625, MDM9635M, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, Nicobar, QCM2150, QCN7605, QCS404, QCS405, QCS605, QM215, Rennell, SA415M, Saipan, SC7180, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SXR1130
Information disclosure in Audio while accessing AVCS services from ADSP payload.
Lack of boundary checks for data offsets received from HLOS can lead to out-of-bound read in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8016, APQ8017, APQ8053, APQ8076, APQ8096, APQ8096AU, APQ8098, MDM9150, MDM9205, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, QCM2150, QCS605, QM215, Rennell, SC7180, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SM6150, SM7150, SM8150, SXR1130, SXR2130
Out of bound access in diag services when DCI command buffer reallocation is not done properly with required capacity in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables in APQ8009, APQ8096AU, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCS605, Rennell, SC8180X, SDM429W, SDM710, SDX55, SM7150, SM8150
Information disclosure in WLAN HOST while processing the WLAN scan descriptor list during roaming scan.
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.
Buffer over-read in ADSP parse function due to lack of check for availability of sufficient data payload received in command response in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8053, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, QCS605, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM670, SDM710, SDM845, SDX20, SDX24
Out of bound read in in fingerprint application due to requested data assigned to a local buffer without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9205, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130
Out of bound read in Fingerprint application due to requested data is being used without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9150, MDM9205, MDM9650, MSM8998, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDA660, SDM630, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130
Out of bound read in adm call back function due to incorrect boundary check for payload in command response in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8053, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, QCS605, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM670, SDM710, SDM845, SDX20, SDX24
Out of bound read access in hypervisor due to an invalid read access attempt by passing invalid addresses in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking
Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read
A vulnerability in an Trend Micro Apex One, Worry-Free Business Security 10.0 SP1 and Worry-Free Business Security Services dll may allow an attacker to manipulate it to cause an out-of-bounds read that crashes multiple processes in the product. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.
The vgacon subsystem in the Linux kernel before 5.8.10 mishandles software scrollback. There is a vgacon_scrolldelta out-of-bounds read, aka CID-973c096f6a85.
Out-of-bounds Read in GitHub repository radareorg/radare2 prior to 5.7.0. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.
Out of bound memory access can happen while parsing ADSP message due to lack of check of size of payload received from userspace in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096AU, IPQ4019, IPQ6018, IPQ8064, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCN7605, QCS605, SC8180X, SDM710, SDX24, SDX55, SM8150, SM8250, SXR2130
NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged regular user can cause an out-of-bounds read, which may lead to denial of service and information disclosure.
Out of bound read can happen in Widevine TA while copying data to buffer from user data due to lack of check of buffer length received in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
Possible out of bound read in DRM due to improper buffer length check. in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking
Improper boundary check in sflvd_rdbuf_bits of libsflvextractor prior to SMR Apr-2022 Release 1 allows attackers to read out of bounds memory.