Out-of-bounds write in handling of macro blocks for MPEG4 codec in libsavsvc.so prior to Android 15 allows local attackers to write out-of-bounds memory.
In wlan AP driver, 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. Patch ID: WCNCR00397141; Issue ID: MSV-2187.
In display, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS10196993; Issue ID: MSV-4807.
Out-of-bounds write under specific condition in the pre-processing of JPEG decoding in libpadm.so prior to SMR Oct-2025 Release 1 allows local attackers to cause memory corruption.
Out-of-bounds write in parsing jpeg image in Samsung Notes prior to version 4.4.26.71 allows local attackers to execute arbitrary code.
In scp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS09625562; Issue ID: MSV-3027.
Out-of-bounds write in parsing bmp image in Samsung Notes prior to version 4.4.26.71 allows local attackers to execute arbitrary code.
In imgsensor, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS10089545; Issue ID: MSV-4279.
Out-of-bounds write in libsavsvc.so prior to SMR Sep-2025 Release 1 allows local attackers to potentially execute arbitrary code.
Bootloader contains a vulnerability in NVIDIA MB2 where potential heap overflow might cause corruption of the heap metadata, which might lead to arbitrary code execution, denial of service, and information disclosure during secure boot.
A vulnerability was detected in floooh sokol up to 16cbcc864012898793cd2bc57f802499a264ea40. The impacted element is the function _sg_pipeline_desc_defaults in the library sokol_gfx.h. The manipulation results in stack-based buffer overflow. The attack requires a local approach. The exploit is now public and may be used. This product does not use versioning. This is why information about affected and unaffected releases are unavailable. The patch is identified as 5d11344150973f15e16d3ec4ee7550a73fb995e0. It is advisable to implement a patch to correct this issue.
In Eclipse OMR versions 0.2.0 to 0.4.0, some of the z/OS atoe print functions use a constant length buffer for string conversion. If the input format string and arguments are larger than the buffer size then buffer overflow occurs. Beginning in version 0.5.0, the conversion buffers are sized correctly and checked appropriately to prevent buffer overflows.
IBM CICS TX Standard 11.1 and IBM CICS TX Advanced 10.1 and 11.1 could allow a local user to execute arbitrary code on the system due to failure to handle DNS return requests by the gethostbyname function.
Stack based buffer overflow in le_ecred_conn_req(). Zephyr versions >= v2.5.0 Stack-based Buffer Overflow (CWE-121). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-8w87-6rfp-cfrm
IBM CICS TX Standard 11.1 and IBM CICS TX Advanced 10.1 and 11.1 could allow a local user to execute arbitrary code on the system due to failure to handle DNS return requests by the gethostbyaddr function.
Possible out of bound write due to improper validation of number of timer values received from firmware while syncing timers in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
Bootloader contains a vulnerability in NVIDIA MB2 where a potential heap overflow could cause memory corruption, which might lead to denial of service or code execution.
In NTFS-3G versions < 2021.8.22, when a specially crafted NTFS inode is loaded in the function ntfs_inode_real_open, a heap buffer overflow can occur allowing for code execution and escalation of privileges.
Bootloader contains a vulnerability in NVIDIA TegraBoot where a potential heap overflow might allow an attacker to control all the RAM after the heap block, leading to denial of service or code execution.
A vulnerability classified as critical was found in code-projects Police FIR Record Management System 1.0. Affected by this vulnerability is an unknown functionality of the component Delete Record Handler. The manipulation leads to stack-based buffer overflow. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used.
fs/seq_file.c in the Linux kernel 3.16 through 5.13.x before 5.13.4 does not properly restrict seq buffer allocations, leading to an integer overflow, an Out-of-bounds Write, and escalation to root by an unprivileged user, aka CID-8cae8cd89f05.
Out-of-bounds write in the BIOS firmware for some Intel(R) Processors may allow an authenticated user to potentially enable escalation of privilege via local access.
The drivers in the tool packages use RTL_QUERY_REGISTRY_DIRECT flag to read a registry value to which an untrusted user-mode application may be able to cause a buffer overflow.
Out-of-bounds write in the Intel(R) Kernelflinger project may allow an authenticated user to potentially enable escalation of privilege via local access.
Stack-based buffer overflow in the gps_tracker function in airodump-ng.c in Aircrack-ng before 1.2 RC 1 allows local users to execute arbitrary code or gain privileges via unspecified vectors.
The drivers in the tool packages use RTL_QUERY_REGISTRY_DIRECT flag to read a registry value to which an untrusted user-mode application may be able to cause a buffer overflow.
The drivers in the tool packages use RTL_QUERY_REGISTRY_DIRECT flag to read a registry value to which an untrusted user-mode application may be able to cause a buffer overflow.
Memory corruption while processing the IOCTL FM HCI WRITE request.
In NTFS-3G versions < 2021.8.22, when a specially crafted unicode string is supplied in an NTFS image a heap buffer overflow can occur and allow for code execution.
A weakness has been identified in mruby 3.4.0. This vulnerability affects the function ary_fill_exec of the file mrbgems/mruby-array-ext/src/array.c. Executing a manipulation of the argument start/length can lead to out-of-bounds write. The attack needs to be launched locally. The exploit has been made available to the public and could be used for attacks. This patch is called 93619f06dd378db6766666b30c08978311c7ec94. It is best practice to apply a patch to resolve this issue.
Trend Micro Home Network Security version 6.6.604 and earlier is vulnerable to an iotcl stack-based buffer overflow vulnerability which could allow an attacker to issue a specially crafted iotcl which could lead to code execution on affected devices. An attacker must first obtain the ability to execute low-privileged code on the target device in order to exploit this vulnerability.
The drivers in the tool packages use RTL_QUERY_REGISTRY_DIRECT flag to read a registry value to which an untrusted user-mode application may be able to cause a buffer overflow.
A security vulnerability has been detected in Kamailio 5.5. Impacted is the function rve_destroy of the file src/core/rvalue.c of the component Configuration File Handler. The manipulation leads to heap-based buffer overflow. The attack must be carried out locally. The exploit has been disclosed publicly and may be used. There is ongoing doubt regarding the real existence of this vulnerability. This attack requires manipulating config files which might not be a realistic scenario in many cases. The vendor was contacted early about this disclosure but did not respond in any way.
Possible out of bounds write due to improper validation of number of GPIOs configured in an internal parameters array in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile
Possible memory corruption due to lack of validation of client data used for memory allocation in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables
A security flaw has been discovered in OGRECave Ogre up to 14.4.1. This issue affects the function STBIImageCodec::encode of the file /ogre/PlugIns/STBICodec/src/OgreSTBICodec.cpp of the component Image Handler. The manipulation results in heap-based buffer overflow. The attack is only possible with local access. The exploit has been released to the public and may be exploited.
Possible stack overflow due to improper length check of TLV while copying the TLV to a local stack variable 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 Wired Infrastructure and Networking
A memory corruption issue was addressed with improved state management. This issue is fixed in Security Update 2021-005 Catalina, macOS Big Sur 11.6. A local attacker may be able to elevate their privileges.
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.
IBM Security Verify Access 20.07 is vulnerable to a stack based buffer overflow, caused by improper bounds checking which could allow a local attacker to execute arbitrary code on the system with elevated privileges.
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 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 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.
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. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the 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.
IBM Spectrum Protect Client 8.1.0.0-8 through 1.11.0 is vulnerable to a stack-based buffer overflow, caused by improper bounds checking when processing the current locale settings. A local attacker could overflow a buffer and execute arbitrary code on the system with elevated privileges or cause the application to crash. IBM X-Force ID: 199479
TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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 `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.
Win32k Elevation of Privilege Vulnerability