Memory corruption while triggering commands in the PlayReady Trusted application.
In the Linux kernel, the following vulnerability has been resolved: wifi: ath11k: update channel list in reg notifier instead reg worker Currently when ath11k gets a new channel list, it will be processed according to the following steps: 1. update new channel list to cfg80211 and queue reg_work. 2. cfg80211 handles new channel list during reg_work. 3. update cfg80211's handled channel list to firmware by ath11k_reg_update_chan_list(). But ath11k will immediately execute step 3 after reg_work is just queued. Since step 2 is asynchronous, cfg80211 may not have completed handling the new channel list, which may leading to an out-of-bounds write error: BUG: KASAN: slab-out-of-bounds in ath11k_reg_update_chan_list Call Trace: ath11k_reg_update_chan_list+0xbfe/0xfe0 [ath11k] kfree+0x109/0x3a0 ath11k_regd_update+0x1cf/0x350 [ath11k] ath11k_regd_update_work+0x14/0x20 [ath11k] process_one_work+0xe35/0x14c0 Should ensure step 2 is completely done before executing step 3. Thus Wen raised patch[1]. When flag NL80211_REGDOM_SET_BY_DRIVER is set, cfg80211 will notify ath11k after step 2 is done. So enable the flag NL80211_REGDOM_SET_BY_DRIVER then cfg80211 will notify ath11k after step 2 is done. At this time, there will be no KASAN bug during the execution of the step 3. [1] https://patchwork.kernel.org/project/linux-wireless/patch/20230201065313.27203-1-quic_wgong@quicinc.com/ Tested-on: WCN6855 hw2.0 PCI WLAN.HSP.1.1-03125-QCAHSPSWPL_V1_V2_SILICONZ_LITE-3
An issue was discovered in the Linux kernel before 6.0.11. Missing validation of IEEE80211_P2P_ATTR_OPER_CHANNEL in drivers/net/wireless/microchip/wilc1000/cfg80211.c in the WILC1000 wireless driver can trigger an out-of-bounds write when parsing the channel list attribute from Wi-Fi management frames.
ImageMagick is free and open-source software used for editing and manipulating digital images. Prior to versions 7.1.2-16 and 6.9.13-41, MagnifyImage uses a fixed-size stack buffer. When using a specific image it is possible to overflow this buffer and corrupt the stack. This vulnerability is fixed in 7.1.2-16 and 6.9.13-41.
An out-of-bounds write flaw was found in the X.Org X server and Xwayland in DRIGetBuffers/DRIGetBuffersWithFormat. A client that requests multiple DRI2BufferBackLeft attachments and one DRI2BufferFrontLeft can trigger an out-of-bounds heap write. This may be used to crash the server, or for privilege escalation if the X server runs as root.
PassFab Excel Password Recovery 8.3.1 contains a structured exception handling buffer overflow vulnerability that allows local attackers to execute arbitrary code by supplying a malicious payload in the registration code field. Attackers can craft a buffer overflow payload with a pop-pop-ret gadget and shellcode that triggers code execution when pasted into the Licensed E-mail and Registration Code field during the registration process.
This CVE ID has been rejected or withdrawn by its CVE Numbering Authority. Filesystem bugs due to corrupt images are not considered a CVE for any filesystem that is only mountable by CAP_SYS_ADMIN in the initial user namespace. That includes delegated mounting.
In the Linux kernel, the following vulnerability has been resolved: bpf: Fix out-of-bounds write in trie_get_next_key() trie_get_next_key() allocates a node stack with size trie->max_prefixlen, while it writes (trie->max_prefixlen + 1) nodes to the stack when it has full paths from the root to leaves. For example, consider a trie with max_prefixlen is 8, and the nodes with key 0x00/0, 0x00/1, 0x00/2, ... 0x00/8 inserted. Subsequent calls to trie_get_next_key with _key with .prefixlen = 8 make 9 nodes be written on the node stack with size 8.
LanSpy 2.0.1.159 contains a local buffer overflow vulnerability that allows attackers to overwrite the instruction pointer by supplying oversized input to the scan field. Attackers can craft a payload with 688 bytes of padding followed by 4 bytes of controlled data to crash the application or potentially achieve code execution.
Memory corruption while reading secure file.
In FuseDaemon.cpp, there is a possible out of bounds write due to memory corruption. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
A vulnerability was found in HDF5 1.14.6 and classified as critical. This issue affects the function H5MM_strndup of the component Metadata Attribute Decoder. The manipulation leads to heap-based buffer overflow. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. The vendor plans to fix this issue in an upcoming release.
In the Linux kernel, the following vulnerability has been resolved: net: pse-pd: Fix out of bound for loop Adjust the loop limit to prevent out-of-bounds access when iterating over PI structures. The loop should not reach the index pcdev->nr_lines since we allocate exactly pcdev->nr_lines number of PI structures. This fix ensures proper bounds are maintained during iterations.
Out-of-bounds write in the Intel(R) Kernelflinger project may allow an authenticated user to potentially enable escalation of privilege via local access.
An ErrorMessage driver stack-based buffer overflow vulnerability in BIOS of some ThinkPad models could allow an attacker with local access to elevate their privileges and execute arbitrary code.
A vulnerability, which was classified as critical, was found in HDF5 1.14.6. This affects the function H5Z__scaleoffset_decompress_one_byte of the component Scale-Offset Filter. The manipulation leads to heap-based buffer overflow. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The vendor plans to fix this issue in an upcoming release.
A memory corruption vulnerability exists in the Shared String Table Record Parser implementation in xls2csv utility version 0.95. A specially crafted malformed file can lead to a heap buffer overflow. An attacker can provide a malicious file to trigger this vulnerability.
In the Linux kernel, the following vulnerability has been resolved: RDMA/rtrs-clt: Reset cid to con_num - 1 to stay in bounds In the function init_conns(), after the create_con() and create_cm() for loop if something fails. In the cleanup for loop after the destroy tag, we access out of bound memory because cid is set to clt_path->s.con_num. This commits resets the cid to clt_path->s.con_num - 1, to stay in bounds in the cleanup loop later.
APTIOV contains a vulnerability in BIOS where an attacker may cause an Out-of-bounds Write by local. Successful exploitation of this vulnerability may lead to data corruption and loss of availability.
Memory corruption while processing image encoding, when configuration is NULL in IOCTL parameter.
A malicious or compromised UApp or ABL may be used by an attacker to issue a malformed system call to the Stage 2 Bootloader potentially leading to corrupt memory and code execution.
There are multiple out-of-bounds vulnerabilities in some processes of D-Link AC2600(DIR-2640) 1.01B04. Ordinary permissions can be elevated to administrator permissions, resulting in local arbitrary code execution. An attacker can combine other vulnerabilities to further achieve the purpose of remote code execution.
In the Linux kernel, the following vulnerability has been resolved: drivers: media: dvb-frontends/rtl2830: fix an out-of-bounds write error Ensure index in rtl2830_pid_filter does not exceed 31 to prevent out-of-bounds access. dev->filters is a 32-bit value, so set_bit and clear_bit functions should only operate on indices from 0 to 31. If index is 32, it will attempt to access a non-existent 33rd bit, leading to out-of-bounds access. Change the boundary check from index > 32 to index >= 32 to resolve this issue.
Memory corruption while reading response from FW, when buffer size is changed by FW while driver is using this size to write null character at the end of buffer.
The MsIo64.sys component in Asus Aura Sync through v1.07.79 does not properly validate input to IOCTL 0x80102040, 0x80102044, 0x80102050, and 0x80102054, allowing attackers to trigger a memory corruption and cause a Denial of Service (DoS) or escalate privileges via crafted IOCTL requests.
In the Linux kernel, the following vulnerability has been resolved: net: hns3: fixed hclge_fetch_pf_reg accesses bar space out of bounds issue The TQP BAR space is divided into two segments. TQPs 0-1023 and TQPs 1024-1279 are in different BAR space addresses. However, hclge_fetch_pf_reg does not distinguish the tqp space information when reading the tqp space information. When the number of TQPs is greater than 1024, access bar space overwriting occurs. The problem of different segments has been considered during the initialization of tqp.io_base. Therefore, tqp.io_base is directly used when the queue is read in hclge_fetch_pf_reg. The error message: Unable to handle kernel paging request at virtual address ffff800037200000 pc : hclge_fetch_pf_reg+0x138/0x250 [hclge] lr : hclge_get_regs+0x84/0x1d0 [hclge] Call trace: hclge_fetch_pf_reg+0x138/0x250 [hclge] hclge_get_regs+0x84/0x1d0 [hclge] hns3_get_regs+0x2c/0x50 [hns3] ethtool_get_regs+0xf4/0x270 dev_ethtool+0x674/0x8a0 dev_ioctl+0x270/0x36c sock_do_ioctl+0x110/0x2a0 sock_ioctl+0x2ac/0x530 __arm64_sys_ioctl+0xa8/0x100 invoke_syscall+0x4c/0x124 el0_svc_common.constprop.0+0x140/0x15c do_el0_svc+0x30/0xd0 el0_svc+0x1c/0x2c el0_sync_handler+0xb0/0xb4 el0_sync+0x168/0x180
Memory corruption while processing IOCTL command when device is in power-save state.
in OpenHarmony v4.1.0 and prior versions allow a local attacker cause the common permission is upgraded to root and sensitive information leak through out-of-bounds write.
Buffer overflow vulnerability in function json_parse_value in sheredom json.h before commit 0825301a07cbf51653882bf2b153cc81fdadf41 (November 14, 2022) allows attackers to code arbitrary code and gain escalated privileges.
MUNGE is an authentication service for creating and validating user credentials. From 0.5 to 0.5.17, local attacker can exploit a buffer overflow vulnerability in munged (the MUNGE authentication daemon) to leak cryptographic key material from process memory. With the leaked key material, the attacker could forge arbitrary MUNGE credentials to impersonate any user (including root) to services that rely on MUNGE for authentication. The vulnerability allows a buffer overflow by sending a crafted message with an oversized address length field, corrupting munged's internal state and enabling extraction of the MAC subkey used for credential verification. This vulnerability is fixed in 0.5.18.
An improper length check in APAService prior to SMR Sep-2021 Release 1 results in stack based Buffer Overflow.
Buffer overflow vulnerability in function json_parse_string in sheredom json.h before commit 0825301a07cbf51653882bf2b153cc81fdadf41 (November 14, 2022) allows attackers to code arbitrary code and gain escalated privileges.
A possible heap buffer overflow vulnerability in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows arbitrary code execution.
In the Linux kernel, the following vulnerability has been resolved: drivers: media: dvb-frontends/rtl2832: fix an out-of-bounds write error Ensure index in rtl2832_pid_filter does not exceed 31 to prevent out-of-bounds access. dev->filters is a 32-bit value, so set_bit and clear_bit functions should only operate on indices from 0 to 31. If index is 32, it will attempt to access a non-existent 33rd bit, leading to out-of-bounds access. Change the boundary check from index > 32 to index >= 32 to resolve this issue. [hverkuil: added fixes tag, rtl2830_pid_filter -> rtl2832_pid_filter in logmsg]
Memory corruption while processing multiple IOCTL command for escape operations.
An out-of-bounds access vulnerability in the Unauthorized Change Prevention service of Trend Micro Apex One and Apex One as a Service could allow a local attacker to elevate privileges on affected installations. Please note: an attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.
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.
An OOB heap buffer r/w access issue was found in the NVM Express Controller emulation in QEMU. It could occur in nvme_cmb_ops routines in nvme device. A guest user/process could use this flaw to crash the QEMU process resulting in DoS or potentially run arbitrary code with privileges of the QEMU process.
procps-ng before version 3.3.15 is vulnerable to multiple integer overflows leading to a heap corruption in file2strvec function. This allows a privilege escalation for a local attacker who can create entries in procfs by starting processes, which could result in crashes or arbitrary code execution in proc utilities run by other users.
A possible out of bounds write vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write.
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.
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. 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.
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 possible buffer overflow vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write and code execution.
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.
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 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.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. 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.