Windows DWM Core Library Elevation of Privilege Vulnerability
In avdt_scb_hdl_pkt_no_frag of avdt_scb_act.cc, 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.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-225879503
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
In buildCommand of bluetooth_ccc.cc, there is a possible out of bounds write due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-238420277References: N/A
Insufficient input validation in CpmDisplayFeatureSmm may allow an attacker to corrupt SMM memory by overwriting an arbitrary bit in an attacker-controlled pointer potentially leading to arbitrary code execution in SMM.
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.
In phNciNfc_MfCreateXchgDataHdr of phNxpExtns_MifareStd.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.Product: AndroidVersions: Android-10Android ID: A-246932269
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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 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 out of bound memory access due to improper boundary check while creating HSYNC fence in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables
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
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.
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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-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. 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. 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 buffer overflow due to lack of range check while processing a DIAG command for COEX management in Snapdragon Auto, Snapdragon Compute, 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
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. 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. 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.
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 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. 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. 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.
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
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
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.
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 BTA_GATTS_HandleValueIndication of bta_gatts_api.cc, 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.Product: AndroidVersions: Android-13Android ID: A-245915315
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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. 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.
In gt1x_debug_write_proc and gt1x_tool_write of drivers/input/touchscreen/mediatek/GT1151/gt1x_generic.c and gt1x_tools.c, 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.
Win32k Elevation of Privilege Vulnerability
An issue was discovered in the Linux kernel through 5.11.3. Certain iSCSI data structures do not have appropriate length constraints or checks, and can exceed the PAGE_SIZE value. An unprivileged user can send a Netlink message that is associated with iSCSI, and has a length up to the maximum length of a Netlink message.
In the Linux kernel, the following vulnerability has been resolved: crypto: virtio/akcipher - Fix stack overflow on memcpy sizeof(struct virtio_crypto_akcipher_session_para) is less than sizeof(struct virtio_crypto_op_ctrl_req::u), copying more bytes from stack variable leads stack overflow. Clang reports this issue by commands: make -j CC=clang-14 mrproper >/dev/null 2>&1 make -j O=/tmp/crypto-build CC=clang-14 allmodconfig >/dev/null 2>&1 make -j O=/tmp/crypto-build W=1 CC=clang-14 drivers/crypto/virtio/ virtio_crypto_akcipher_algs.o
Parsing malformed project files in Omron CX-One versions 4.42 and prior, including the following applications: CX-FLnet versions 1.00 and prior, CX-Protocol versions 1.992 and prior, CX-Programmer versions 9.65 and prior, CX-Server versions 5.0.22 and prior, Network Configurator versions 3.63 and prior, and Switch Box Utility versions 1.68 and prior, may cause a heap-based buffer overflow.
In the Linux kernel, the following vulnerability has been resolved: bpf: Fix DEVMAP_HASH overflow check on 32-bit arches The devmap code allocates a number hash buckets equal to the next power of two of the max_entries value provided when creating the map. When rounding up to the next power of two, the 32-bit variable storing the number of buckets can overflow, and the code checks for overflow by checking if the truncated 32-bit value is equal to 0. However, on 32-bit arches the rounding up itself can overflow mid-way through, because it ends up doing a left-shift of 32 bits on an unsigned long value. If the size of an unsigned long is four bytes, this is undefined behaviour, so there is no guarantee that we'll end up with a nice and tidy 0-value at the end. Syzbot managed to turn this into a crash on arm32 by creating a DEVMAP_HASH with max_entries > 0x80000000 and then trying to update it. Fix this by moving the overflow check to before the rounding up operation.
The interactive service in OpenVPN 2.6.9 and earlier allows an attacker to send data causing a stack overflow which can be used to execute arbitrary code with more privileges.
A vulnerability was found in Jianming Antivirus 16.2.2022.418. It has been declared as critical. This vulnerability affects unknown code in the library kvcore.sys of the component IoControlCode Handler. The manipulation leads to memory corruption. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-224008.
A flaw was found in the Linux Kernel in RDS (Reliable Datagram Sockets) protocol. The rds_rm_zerocopy_callback() uses list_entry() on the head of a list causing a type confusion. Local user can trigger this with rds_message_put(). Type confusion leads to `struct rds_msg_zcopy_info *info` actually points to something else that is potentially controlled by local user. It is known how to trigger this, which causes an out of bounds access, and a lock corruption.
In the Mediatek Preloader, there are out of bounds reads and writes due to an exposed interface that allows arbitrary peripheral memory mapping with insufficient blacklisting/whitelisting. This could lead to local elevation of privilege, given physical access to the device with no additional execution privileges needed. User interaction is needed for exploitation.
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_rx_range_done_ind(), there is no input validation check on rtt_id coming from userspace, which can lead to a heap overwrite.
An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_subscribe_get_nl_params(), there is no input validation check on hal_req->num_intf_addr_present coming from userspace, which can lead to a heap overwrite.
In the Linux kernel, the following vulnerability has been resolved: LoongArch: BPF: Prevent out-of-bounds memory access The test_tag test triggers an unhandled page fault: # ./test_tag [ 130.640218] CPU 0 Unable to handle kernel paging request at virtual address ffff80001b898004, era == 9000000003137f7c, ra == 9000000003139e70 [ 130.640501] Oops[#3]: [ 130.640553] CPU: 0 PID: 1326 Comm: test_tag Tainted: G D O 6.7.0-rc4-loong-devel-gb62ab1a397cf #47 61985c1d94084daa2432f771daa45b56b10d8d2a [ 130.640764] Hardware name: QEMU QEMU Virtual Machine, BIOS unknown 2/2/2022 [ 130.640874] pc 9000000003137f7c ra 9000000003139e70 tp 9000000104cb4000 sp 9000000104cb7a40 [ 130.641001] a0 ffff80001b894000 a1 ffff80001b897ff8 a2 000000006ba210be a3 0000000000000000 [ 130.641128] a4 000000006ba210be a5 00000000000000f1 a6 00000000000000b3 a7 0000000000000000 [ 130.641256] t0 0000000000000000 t1 00000000000007f6 t2 0000000000000000 t3 9000000004091b70 [ 130.641387] t4 000000006ba210be t5 0000000000000004 t6 fffffffffffffff0 t7 90000000040913e0 [ 130.641512] t8 0000000000000005 u0 0000000000000dc0 s9 0000000000000009 s0 9000000104cb7ae0 [ 130.641641] s1 00000000000007f6 s2 0000000000000009 s3 0000000000000095 s4 0000000000000000 [ 130.641771] s5 ffff80001b894000 s6 ffff80001b897fb0 s7 9000000004090c50 s8 0000000000000000 [ 130.641900] ra: 9000000003139e70 build_body+0x1fcc/0x4988 [ 130.642007] ERA: 9000000003137f7c build_body+0xd8/0x4988 [ 130.642112] CRMD: 000000b0 (PLV0 -IE -DA +PG DACF=CC DACM=CC -WE) [ 130.642261] PRMD: 00000004 (PPLV0 +PIE -PWE) [ 130.642353] EUEN: 00000003 (+FPE +SXE -ASXE -BTE) [ 130.642458] ECFG: 00071c1c (LIE=2-4,10-12 VS=7) [ 130.642554] ESTAT: 00010000 [PIL] (IS= ECode=1 EsubCode=0) [ 130.642658] BADV: ffff80001b898004 [ 130.642719] PRID: 0014c010 (Loongson-64bit, Loongson-3A5000) [ 130.642815] Modules linked in: [last unloaded: bpf_testmod(O)] [ 130.642924] Process test_tag (pid: 1326, threadinfo=00000000f7f4015f, task=000000006499f9fd) [ 130.643062] Stack : 0000000000000000 9000000003380724 0000000000000000 0000000104cb7be8 [ 130.643213] 0000000000000000 25af8d9b6e600558 9000000106250ea0 9000000104cb7ae0 [ 130.643378] 0000000000000000 0000000000000000 9000000104cb7be8 90000000049f6000 [ 130.643538] 0000000000000090 9000000106250ea0 ffff80001b894000 ffff80001b894000 [ 130.643685] 00007ffffb917790 900000000313ca94 0000000000000000 0000000000000000 [ 130.643831] ffff80001b894000 0000000000000ff7 0000000000000000 9000000100468000 [ 130.643983] 0000000000000000 0000000000000000 0000000000000040 25af8d9b6e600558 [ 130.644131] 0000000000000bb7 ffff80001b894048 0000000000000000 0000000000000000 [ 130.644276] 9000000104cb7be8 90000000049f6000 0000000000000090 9000000104cb7bdc [ 130.644423] ffff80001b894000 0000000000000000 00007ffffb917790 90000000032acfb0 [ 130.644572] ... [ 130.644629] Call Trace: [ 130.644641] [<9000000003137f7c>] build_body+0xd8/0x4988 [ 130.644785] [<900000000313ca94>] bpf_int_jit_compile+0x228/0x4ec [ 130.644891] [<90000000032acfb0>] bpf_prog_select_runtime+0x158/0x1b0 [ 130.645003] [<90000000032b3504>] bpf_prog_load+0x760/0xb44 [ 130.645089] [<90000000032b6744>] __sys_bpf+0xbb8/0x2588 [ 130.645175] [<90000000032b8388>] sys_bpf+0x20/0x2c [ 130.645259] [<9000000003f6ab38>] do_syscall+0x7c/0x94 [ 130.645369] [<9000000003121c5c>] handle_syscall+0xbc/0x158 [ 130.645507] [ 130.645539] Code: 380839f6 380831f9 28412bae <24000ca6> 004081ad 0014cb50 004083e8 02bff34c 58008e91 [ 130.645729] [ 130.646418] ---[ end trace 0000000000000000 ]--- On my machine, which has CONFIG_PAGE_SIZE_16KB=y, the test failed at loading a BPF prog with 2039 instructions: prog = (struct bpf_prog *)ffff80001b894000 insn = (struct bpf_insn *)(prog->insnsi)fff ---truncated---
A vulnerability was found in DriverGenius 9.70.0.346. It has been declared as critical. Affected by this vulnerability is the function 0x9C402088 in the library mydrivers64.sys of the component IOCTL Handler. The manipulation leads to memory corruption. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. The identifier VDB-224233 was assigned to this vulnerability.
A vulnerability was found in IObit Malware Fighter 9.4.0.776. It has been declared as critical. This vulnerability affects the function 0x8018E000/0x8018E004 in the library IMFCameraProtect.sys of the component IOCTL Handler. The manipulation leads to stack-based buffer overflow. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. VDB-224026 is the identifier assigned to this vulnerability.
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 procfile_write of drivers/misc/mediatek/connectivity/wlan/gen2/os/linux/gl_proc.c, there is a possible OOB 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.