A vulnerability was found in Perl. This security issue occurs while Perl for Windows relies on the system path environment variable to find the shell (`cmd.exe`). When running an executable that uses the Windows Perl interpreter, Perl attempts to find and execute `cmd.exe` within the operating system. However, due to path search order issues, Perl initially looks for cmd.exe in the current working directory. This flaw allows an attacker with limited privileges to place`cmd.exe` in locations with weak permissions, such as `C:\ProgramData`. By doing so, arbitrary code can be executed when an administrator attempts to use this executable from these compromised locations.

An issue was discovered in Samsung Mobile Processor Exynos 980, Exynos 850, Exynos 1280, Exynos 1380, and Exynos 1330. In the function slsi_nan_get_security_info_nl(), there is no input validation check on sec_info->key_info.body.pmk_info.pmk_len coming from userspace, which can lead to a heap overwrite.

An out-of-bounds write flaw was found in grub2's NTFS filesystem driver. This issue may allow an attacker to present a specially crafted NTFS filesystem image, leading to grub's heap metadata corruption. In some circumstances, the attack may also corrupt the UEFI firmware heap metadata. As a result, arbitrary code execution and secure boot protection bypass may be achieved.

In vring_size of external/headers/include/virtio/virtio_ring.h, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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.

A stack-based buffer overflow in Fortinet FortiOS version 7.4.0 through 7.4.1 and 7.2.0 through 7.2.7 and 7.0.0 through 7.0.12 and 6.4.6 through 6.4.15 and 6.2.9 through 6.2.16 and 6.0.13 through 6.0.18 allows attacker to execute unauthorized code or commands via specially crafted CLI commands.

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.

In vring_init of external/headers/include/virtio/virtio_ring.h, 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.

Windows Kernel Elevation of Privilege Vulnerability

IBM AIX 7.2, 7.3, and VIOS 3.1 could allow a non-privileged local user to exploit a vulnerability in the invscout command to execute arbitrary commands. IBM X-Force ID: 267966.

In the Linux kernel, the following vulnerability has been resolved: drm/amdgpu: Validate TA binary size Add TA binary size validation to avoid OOB write. (cherry picked from commit c0a04e3570d72aaf090962156ad085e37c62e442)

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.

In CreateAudioBroadcast of broadcaster.cc, 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.

Exim 4 before 4.94.2 allows Out-of-bounds Write because the main function, while setuid root, copies the current working directory pathname into a buffer that is too small (on some common platforms).

In CreateAudioBroadcast of broadcaster.cc, 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.

Memory corruption may occur during IO configuration processing when the IO port count is invalid.

Memory corruption may occour while generating test pattern due to negative indexing of display ID.

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.

Memory corruption while invoking IOCTL calls from userspace to camera kernel driver to dump request information.

Improper buffer restrictions in Intel(R) Media SDK all versions may allow an authenticated user to potentially enable escalation of privilege via local access.

In multiple functions of btm_ble_gap.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

In ppmp_unprotect_buf of drm_fw.c, there is a possible compromise of protected memory due to a logic error in the code. This could lead to local escalation of privilege to TEE with no additional execution privileges needed. User interaction is not needed for exploitation.

In the Linux kernel, the following vulnerability has been resolved: bpf: Reject variable offset alu on PTR_TO_FLOW_KEYS For PTR_TO_FLOW_KEYS, check_flow_keys_access() only uses fixed off for validation. However, variable offset ptr alu is not prohibited for this ptr kind. So the variable offset is not checked. The following prog is accepted: func#0 @0 0: R1=ctx() R10=fp0 0: (bf) r6 = r1 ; R1=ctx() R6_w=ctx() 1: (79) r7 = *(u64 *)(r6 +144) ; R6_w=ctx() R7_w=flow_keys() 2: (b7) r8 = 1024 ; R8_w=1024 3: (37) r8 /= 1 ; R8_w=scalar() 4: (57) r8 &= 1024 ; R8_w=scalar(smin=smin32=0, smax=umax=smax32=umax32=1024,var_off=(0x0; 0x400)) 5: (0f) r7 += r8 mark_precise: frame0: last_idx 5 first_idx 0 subseq_idx -1 mark_precise: frame0: regs=r8 stack= before 4: (57) r8 &= 1024 mark_precise: frame0: regs=r8 stack= before 3: (37) r8 /= 1 mark_precise: frame0: regs=r8 stack= before 2: (b7) r8 = 1024 6: R7_w=flow_keys(smin=smin32=0,smax=umax=smax32=umax32=1024,var_off =(0x0; 0x400)) R8_w=scalar(smin=smin32=0,smax=umax=smax32=umax32=1024, var_off=(0x0; 0x400)) 6: (79) r0 = *(u64 *)(r7 +0) ; R0_w=scalar() 7: (95) exit This prog loads flow_keys to r7, and adds the variable offset r8 to r7, and finally causes out-of-bounds access: BUG: unable to handle page fault for address: ffffc90014c80038 [...] Call Trace: <TASK> bpf_dispatcher_nop_func include/linux/bpf.h:1231 [inline] __bpf_prog_run include/linux/filter.h:651 [inline] bpf_prog_run include/linux/filter.h:658 [inline] bpf_prog_run_pin_on_cpu include/linux/filter.h:675 [inline] bpf_flow_dissect+0x15f/0x350 net/core/flow_dissector.c:991 bpf_prog_test_run_flow_dissector+0x39d/0x620 net/bpf/test_run.c:1359 bpf_prog_test_run kernel/bpf/syscall.c:4107 [inline] __sys_bpf+0xf8f/0x4560 kernel/bpf/syscall.c:5475 __do_sys_bpf kernel/bpf/syscall.c:5561 [inline] __se_sys_bpf kernel/bpf/syscall.c:5559 [inline] __x64_sys_bpf+0x73/0xb0 kernel/bpf/syscall.c:5559 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0x3f/0x110 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x63/0x6b Fix this by rejecting ptr alu with variable offset on flow_keys. Applying the patch rejects the program with "R7 pointer arithmetic on flow_keys prohibited".

Memory corruption while sound model registration for voice activation with audio kernel driver.

A local privilege escalation vulnerability in Juniper Networks Junos OS and Junos OS Evolved allows a local, low-privileged user to cause the Juniper DHCP daemon (jdhcpd) process to crash, resulting in a Denial of Service (DoS), or execute arbitrary commands as root. Continued processing of malicious input will repeatedly crash the system and sustain the Denial of Service (DoS) condition. Systems are only vulnerable if jdhcpd is running, which can be confirmed via the 'show system processes' command. For example: root@host# run show system processes extensive | match dhcp 26537 root -16 0 97568K 13692K RUN 0 0:01 3.71% jdhcpd This issue affects: Juniper Networks Junos OS: All versions, including the following supported releases: 15.1 versions prior to 15.1R7-S10; 17.4 versions prior to 17.4R3-S5; 18.3 versions prior to 18.3R3-S5; 18.4 versions prior to 18.4R3-S9; 19.1 versions prior to 19.1R3-S6; 19.2 versions prior to 19.2R1-S7, 19.2R3-S3; 19.3 versions prior to 19.3R2-S6, 19.3R3-S3; 19.4 versions prior to 19.4R3-S6; 20.1 versions prior to 20.1R2-S2, 20.1R3-S1; 20.2 versions prior to 20.2R3-S2; 20.3 versions prior to 20.3R3; 20.4 versions prior to 20.4R2-S1, 20.4R3; 21.1 versions prior to 21.1R1-S1, 21.1R2. Juniper Networks Junos OS Evolved: All versions prior to 20.4R2-S3-EVO; All versions of 21.1-EVO.

Memory corruption can occur when a compat IOCTL call is followed by a normal IOCTL call from userspace.

Memory corruption can occur if an already verified IFS2 image is overwritten, bypassing boot verification. This allows unauthorized programs to be injected into security-sensitive images, enabling the booting of a tampered IFS2 system image.

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.

Memory corruption may occur when invoking IOCTL calls from userspace to the camera kernel driver to dump request information, due to a missing memory requirement check.

KiTTY versions 0.76.1.13 and before is vulnerable to a stack-based buffer overflow via the hostname, occurs due to insufficient bounds checking and input sanitization. This allows an attacker to overwrite adjacent memory, which leads to arbitrary code execution.

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 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. 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. 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 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.

Memory corruption when IOCTL call is invoked from user-space to write board data to WLAN driver.

An exploitable stack buffer overflow vulnerability vulnerability exists in the iocheckd service ‘I/O-Check’ functionality of WAGO PFC 200 Firmware version 03.02.02(14). An attacker can send a specially crafted packet to trigger the parsing of this cache file. The destination buffer sp+0x440 is overflowed with the call to sprintf() for any ip values that are greater than 1024-len(‘/etc/config-tools/config_interfaces interface=X1 state=enabled ip-address=‘) in length. A ip value of length 0x3da will cause the service to crash.

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

In ppmp_protect_mfcfw_buf of code/drm_fw.c, there is a possible memory corruption 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.

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.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.