Memory corruption in WLAN handler while processing PhyID in Tx status handler.
Memory corruption in BT controller while parsing debug commands with specific sub-opcodes at HCI interface level.
A flaw was found in the Linux kernel's ext4 filesystem. A local user can cause an out-of-bounds write and a denial of service or unspecified other impact is possible by mounting and operating a crafted ext4 filesystem image.
Memory corruption in core services when Diag handler receives a command to configure event listeners.
Memory corruption in Core Services while executing the command for removing a single event listener.
Memory corruption vulnerability in the driver file component in McAfee GetSusp prior to 4.0.0 could allow a program being investigated on the local machine to trigger a buffer overflow in GetSusp, leading to the execution of arbitrary code, potentially triggering a BSOD.
Memory corruption in WLAN Host while setting the PMK length in PMK length in internal cache.
vim is vulnerable to Heap-based Buffer Overflow
Memory corruption while processing audio effects.
Memory corruption in WLAN FW while processing command parameters from untrusted WMI payload.
Memory corruption in WLAN HAL while processing devIndex from untrusted WMI payload.
ncurses before 6.4 20230408, when used by a setuid application, allows local users to trigger security-relevant memory corruption via malformed data in a terminfo database file that is found in $HOME/.terminfo or reached via the TERMINFO or TERM environment variable.
Memory corruption in WLAN HAL while handling command streams through WMI interfaces.
Memory corruption in WLAN HAL while handling command through WMI interfaces.
Buffer overflow vulnerability in the signelf library used by Zscaler Client Connector on Linux allows Code Injection. This issue affects Zscaler Client Connector for Linux: before 1.3.1.6.
Multiple out-of-bounds write issues were addressed with improved bounds checking. This issue is fixed in macOS Big Sur 11.6.1. A malicious application may be able to execute arbitrary code with kernel privileges.
zsh through version 5.4.2 is vulnerable to a stack-based buffer overflow in the utils.c:checkmailpath function. A local attacker could exploit this to execute arbitrary code in the context of another user.
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
Memory corruption in MPP performance while accessing DSM watermark using external memory address.
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.
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
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.
Possible stack overflow due to improper validation of camera name length before copying the name in VR Service in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT
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.
There is an improper memory access permission configuration on ACPU.Successful exploitation of this vulnerability may cause out-of-bounds access.
Improper Input Validation vulnerability in synaTEE.signed.dll of Synaptics Fingerprint Driver allows a local authorized attacker to overwrite a heap tag, with potential loss of confidentiality. This issue affects: Synaptics Synaptics Fingerprint Driver 5.1.xxx.26 versions prior to xxx=340 on x86/64; 5.2.xxxx.26 versions prior to xxxx=3541 on x86/64; 5.2.2xx.26 versions prior to xx=29 on x86/64; 5.2.3xx.26 versions prior to xx=25 on x86/64; 5.3.xxxx.26 versions prior to xxxx=3543 on x86/64; 5.5.xx.1058 versions prior to xx=44 on x86/64; 5.5.xx.1102 versions prior to xx=34 on x86/64; 5.5.xx.1116 versions prior to xx=14 on x86/64; 6.0.xx.1104 versions prior to xx=50 on x86/64; 6.0.xx.1108 versions prior to xx=31 on x86/64; 6.0.xx.1111 versions prior to xx=58 on x86/64.
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. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->data`. 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. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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. 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-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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 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. 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.
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
Memory corruption in UTILS when modem processes memory specific Diag commands having arbitrary address values as input arguments.
Out-of-bounds write in some Intel(R) Arc(TM) & Iris(R) Xe Graphics - WHQL - Windows drivers before version 31.0.101.4255 may allow authenticated user to potentially enable escalation of privilege via local access.
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.
ATMFD.DLL in the Adobe Type Manager Font Driver in Microsoft Windows Server 2003 SP2, Windows Vista SP2, Windows Server 2008 SP2 and R2 SP1, Windows 7 SP1, Windows 8, Windows 8.1, Windows Server 2012 Gold and R2, and Windows RT Gold and 8.1 allows local users to gain privileges via a crafted application, aka "ATMFD.DLL Memory Corruption Vulnerability."
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/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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 Linux kernel, the following vulnerability has been resolved: usb: gadget: f_uac1_legacy: validate control request size f_audio_complete() copies req->length bytes into a 4-byte stack variable: u32 data = 0; memcpy(&data, req->buf, req->length); req->length is derived from the host-controlled USB request path, which can lead to a stack out-of-bounds write. Validate req->actual against the expected payload size for the supported control selectors and decode only the expected amount of data. This avoids copying a host-influenced length into a fixed-size stack object.
In the Linux kernel, the following vulnerability has been resolved: firmware: thead: Fix buffer overflow and use standard endian macros Addresses two issues in the TH1520 AON firmware protocol driver: 1. Fix a potential buffer overflow where the code used unsafe pointer arithmetic to access the 'mode' field through the 'resource' pointer with an offset. This was flagged by Smatch static checker as: "buffer overflow 'data' 2 <= 3" 2. Replace custom RPC_SET_BE* and RPC_GET_BE* macros with standard kernel endianness conversion macros (cpu_to_be16, etc.) for better portability and maintainability. The functionality was re-tested with the GPU power-up sequence, confirming the GPU powers up correctly and the driver probes successfully. [ 12.702370] powervr ffef400000.gpu: [drm] loaded firmware powervr/rogue_36.52.104.182_v1.fw [ 12.711043] powervr ffef400000.gpu: [drm] FW version v1.0 (build 6645434 OS) [ 12.719787] [drm] Initialized powervr 1.0.0 for ffef400000.gpu on minor 0
In the Linux kernel, the following vulnerability has been resolved: Buffer overflow in drivers/xen/sys-hypervisor.c The build id returned by HYPERVISOR_xen_version(XENVER_build_id) is neither NUL terminated nor a string. The first causes a buffer overflow as sprintf in buildid_show will read and copy till it finds a NUL. 00000000 f4 91 51 f4 dd 38 9e 9d 65 47 52 eb 10 71 db 50 |..Q..8..eGR..q.P| 00000010 b9 a8 01 42 6f 2e 32 |...Bo.2| 00000017 So use a memcpy instead of sprintf to have the correct value: 00000000 f4 91 51 f4 dd 00 9e 9d 65 47 52 eb 10 71 db 50 |..Q.....eGR..q.P| 00000010 b9 a8 01 42 |...B| 00000014 (the above have a hack to embed a zero inside and check it's returned correctly). This is XSA-485 / CVE-2026-31786
Improper validation of buffer size input to the EFS file can lead to memory corruption in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables
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
Possible out of bound read or write in VR service due to lack of validation of DSP selection values in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT
Possible buffer overflow due to improper size calculation of payload received in VR service in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables
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.
arch/powerpc/kvm/book3s_rtas.c in the Linux kernel through 5.13.5 on the powerpc platform allows KVM guest OS users to cause host OS memory corruption via rtas_args.nargs, aka CID-f62f3c20647e.
In the Linux kernel, the following vulnerability has been resolved: media: intel/ipu6: remove cpu latency qos request on error Fix cpu latency qos list corruption like below. It happens when we do not remove cpu latency request on error path and free corresponding memory. [ 30.634378] l7 kernel: list_add corruption. prev->next should be next (ffffffff9645e960), but was 0000000100100001. (prev=ffff8e9e877e20a8). [ 30.634388] l7 kernel: WARNING: CPU: 2 PID: 2008 at lib/list_debug.c:32 __list_add_valid_or_report+0x83/0xa0 <snip> [ 30.634640] l7 kernel: Call Trace: [ 30.634650] l7 kernel: <TASK> [ 30.634659] l7 kernel: ? __list_add_valid_or_report+0x83/0xa0 [ 30.634669] l7 kernel: ? __warn.cold+0x93/0xf6 [ 30.634678] l7 kernel: ? __list_add_valid_or_report+0x83/0xa0 [ 30.634690] l7 kernel: ? report_bug+0xff/0x140 [ 30.634702] l7 kernel: ? handle_bug+0x58/0x90 [ 30.634712] l7 kernel: ? exc_invalid_op+0x17/0x70 [ 30.634723] l7 kernel: ? asm_exc_invalid_op+0x1a/0x20 [ 30.634733] l7 kernel: ? __list_add_valid_or_report+0x83/0xa0 [ 30.634742] l7 kernel: plist_add+0xdd/0x140 [ 30.634754] l7 kernel: pm_qos_update_target+0xa0/0x1f0 [ 30.634764] l7 kernel: cpu_latency_qos_update_request+0x61/0xc0 [ 30.634773] l7 kernel: intel_dp_aux_xfer+0x4c7/0x6e0 [i915 1f824655ed04687c2b0d23dbce759fa785f6d033]
Memory corruption in WIN Product while invoking WinAcpi update driver in the UEFI region.