The sock_setsockopt function in net/core/sock.c in the Linux kernel before 3.5 mishandles negative values of sk_sndbuf and sk_rcvbuf, which allows local users to cause a denial of service (memory corruption and system crash) or possibly have unspecified other impact by leveraging the CAP_NET_ADMIN capability for a crafted setsockopt system call with the (1) SO_SNDBUF or (2) SO_RCVBUF option.
A flaw was found in xorg-x11-server in versions before 21.1.2 and before 1.20.14. An out-of-bounds access can occur in the SProcXFixesCreatePointerBarrier function. The highest threat from this vulnerability is to data confidentiality and integrity as well as system availability.
Possible buffer overflow in voice service due to lack of input validation of parameters in QMI Voice API in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables
A flaw was found in xorg-x11-server in versions before 21.1.2 and before 1.20.14. An out-of-bounds access can occur in the SProcRenderCompositeGlyphs function. The highest threat from this vulnerability is to data confidentiality and integrity as well as system availability.
Memory corruption while invoking IOCTL calls from user space to read WLAN target diagnostic information.
Out of bound write can occur in playready while processing command due to lack of input validation in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music
u'Possible buffer overflow in WIFI hal process due to usage of memcpy without checking length of destination buffer' in Snapdragon Auto, Snapdragon Compute, Snapdragon Industrial IOT, Snapdragon Mobile in QCM4290, QCS4290, QM215, QSM8350, SA6145P, SA6155, SA6155P, SA8155, SA8155P, SC8180X, SC8180XP, SDX55, SDX55M, SM4250, SM4250P, SM6115, SM6115P, SM6125, SM6250, SM6350, SM7125, SM7225, SM7250, SM7250P, SM8150, SM8150P, SM8250, SM8350, SM8350P, SXR2130, SXR2130P
IBM DB2 High Performance Unload load for LUW 6.1 and 6.5 is vulnerable to a buffer overflow, caused by improper bounds checking which could allow a local attacker to execute arbitrary code on the system with root privileges. IBM X-Force ID: 165481.
Memory corruption while processing camera use case IOCTL call.
NXP MCUXpresso SDK v2.7.0 was discovered to contain a buffer overflow in the function USB_HostProcessCallback().
rzpnk.sys in Razer Synapse 2.20.15.1104 allows local users to read and write to arbitrary memory locations, and consequently gain privileges, via a methodology involving a handle to \Device\PhysicalMemory, IOCTL 0x22A064, and ZwMapViewOfSection.
In drivers/char/virtio_console.c in the Linux kernel before 5.13.4, data corruption or loss can be triggered by an untrusted device that supplies a buf->len value exceeding the buffer size. NOTE: the vendor indicates that the cited data corruption is not a vulnerability in any existing use case; the length validation was added solely for robustness in the face of anomalous host OS behavior
In the Linux kernel, the following vulnerability has been resolved: kdb: Fix buffer overflow during tab-complete Currently, when the user attempts symbol completion with the Tab key, kdb will use strncpy() to insert the completed symbol into the command buffer. Unfortunately it passes the size of the source buffer rather than the destination to strncpy() with predictably horrible results. Most obviously if the command buffer is already full but cp, the cursor position, is in the middle of the buffer, then we will write past the end of the supplied buffer. Fix this by replacing the dubious strncpy() calls with memmove()/memcpy() calls plus explicit boundary checks to make sure we have enough space before we start moving characters around.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. 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.
Memory corruption in BT controller due to improper length check while processing vendor specific commands in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking
Qihoo 360 (https://www.360.cn/) Qihoo 360 Safeguard (https://www.360.cn/) Qihoo 360 Total Security (http://www.360totalsecurity.com/) is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). The component is: This is a set of vulnerabilities affecting popular software, "360 Safeguard(12.1.0.1004,12.1.0.1005,13.1.0.1001)" , "360 Total Security(10.8.0.1060,10.8.0.1213)", "360 Safe Browser & 360 Chrome(13.0.2170.0)". The attack vector is: On the browser vulnerability, just open a link to complete the vulnerability exploitation remotely; on the client software, you need to locally execute the vulnerability exploitation program, which of course can be achieved with the full chain of browser vulnerability. ¶¶ This is a set of the most serious vulnerabilities that exist on Qihoo 360's PC client a variety of popular software, remote vulnerabilities can be completed by opening a link to arbitrary code execution on both security browsers, with the use of local vulnerabilities, not only help the vulnerability code constitutes an escalation of privileges, er can make the spyware persistent without being scanned permanently resides on the target PC computer (because local vulnerability against Qihoo 360 company's antivirus kernel flaws); this group of remote and local vulnerability of the perfect match, to achieve an information security fallacy, in Qihoo 360's antivirus vulnerability, not only can not be scanned out of the virus, but will help the virus persistently control the target computer, while Qihoo 360 claims to be a safe browser, which exists in the kernel vulnerability but helped the composition of the remote vulnerability. (Security expert "Memory Corruptor" have reported this set of vulnerabilities to the corresponding vendor, all vulnerabilities have been fixed and the vendor rewarded thousands of dollars to the security experts)
Memory corruption while station LL statistic handling.
Improper buffer restrictions in firmware for some Intel(R) Wireless Bluetooth(R) and Killer(TM) Bluetooth(R) products before version 22.120 may allow an authenticated user to potentially enable escalation of privilege via local access.
Microsoft Edge (HTML-based) Memory Corruption Vulnerability
Possible buffer overflow due to lack of input IB amount validation while processing the user command in Snapdragon Auto
Memory corruption while processing GPU page table switch.
Possible buffer overflow due to lack of validation for the length of NAI string read from EFS in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile
A potential buffer overflow in the software drivers for certain HP LaserJet products and Samsung product printers could lead to an escalation of privilege.
IBM DB2 for Linux, UNIX and Windows (includes DB2 Connect Server) 9.7, 10.1, 10.5, and 11.1 is vulnerable to a buffer overflow, which could allow an authenticated local attacker to execute arbitrary code on the system as root. IBM X-ForceID: 155894.
Improper buffer restrictions in Intel(R) Media SDK all versions may allow an authenticated user to potentially enable escalation of privilege via local access.
A vulnerability, which was classified as critical, has been found in bwoodsend rockhopper up to 0.1.2. Affected by this issue is the function count_rows of the file rockhopper/src/ragged_array.c of the component Binary Parser. The manipulation of the argument raw leads to buffer overflow. Local access is required to approach this attack. Upgrading to version 0.2.0 is able to address this issue. The name of the patch is 1a15fad5e06ae693eb9b8908363d2c8ef455104e. It is recommended to upgrade the affected component. The identifier of this vulnerability is VDB-266312.
The tpacket_rcv function in net/packet/af_packet.c in the Linux kernel before 4.13 mishandles vnet headers, which might allow local users to cause a denial of service (buffer overflow, and disk and memory corruption) or possibly have unspecified other impact via crafted system calls.
In construct_transaction_from_cmd of lwis_ioctl.c, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.
Windows Kernel Elevation of Privilege Vulnerability
Windows Kernel-Mode Driver Elevation of Privilege Vulnerability
Possible heap overflow due to improper validation of local variable while storing current task information locally in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables
Possible heap Memory Corruption Issue due to lack of input validation when sending HWTC IQ Capture command in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables
Improper validation of maximum size of data write to EFS file can lead to memory corruption in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables
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
Improper validation of input when provisioning the HDCP key can lead to memory corruption in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Voice & Music, Snapdragon Wearables
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 bound access due to improper validation of item size and DIAG memory pools data while switching between USB and PCIE interface in Snapdragon Auto, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking
Improper size validation of QXDM commands can lead to memory corruption in Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile
Possible buffer overflow while printing the HARQ memory partition detail due to improper validation of buffer size in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile
Possible buffer overflow due to lack of buffer length check when segmented WMI command is received in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking
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. 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.
Memory corruption when user provides data for FM HCI command control operations.
Memory corruption during the handshake between the Primary Virtual Machine and Trusted Virtual Machine.
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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.
An issue was discovered in the Linux kernel through 5.11.8. The sound/soc/qcom/sdm845.c soundwire device driver has a buffer overflow when an unexpected port ID number is encountered, aka CID-1c668e1c0a0f. (This has been fixed in 5.12-rc4.)
Memory corruption when Alternative Frequency offset value is set to 255.
Buffer overflow in the clusterip_proc_write function in net/ipv4/netfilter/ipt_CLUSTERIP.c in the Linux kernel before 2.6.39 might allow local users to cause a denial of service or have unspecified other impact via a crafted write operation, related to string data that lacks a terminating '\0' character.
IBM DB2 for Linux, UNIX and Windows (includes DB2 Connect Server) 9.7, 10.1, 10.5, and 11.1 is vulnerable to a buffer overflow, which could allow an authenticated local attacker to execute arbitrary code on the system as root. IBM X-Force ID: 161202.
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.