A use after free was found in igc_reloc_struct_ptr() of psi/igc.c of ghostscript-9.25. A local attacker could supply a specially crafted PDF file to cause a denial of service.

A divide by zero issue discovered in eps_print_page in gdevepsn.c in Artifex Software GhostScript 9.50 allows remote attackers to cause a denial of service via opening of crafted PDF file.

A Division by Zero vulnerability in bj10v_print_page() in contrib/japanese/gdev10v.c of Artifex Software GhostScript v9.50 allows a remote attacker to cause a denial of service via a crafted PDF file. This is fixed in v9.51.

A division by zero vulnerability in dot24_print_page() in devices/gdevdm24.c of Artifex Software GhostScript v9.50 allows a remote attacker to cause a denial of service via a crafted PDF file. This is fixed in v9.51.

A floating point exception (divide-by-zero) vulnerability was discovered in Artifex MuPDF 1.23.4 in function pnm_binary_read_image() of load-pnm.c when span equals zero.

A floating point exception (divide-by-zero) vulnerability was discovered in Artifex MuPDF 1.23.4 in the function fz_new_pixmap_from_float_data() of pixmap.c.

A floating point exception (divide-by-zero) vulnerability was discovered in Artifex MuPDF 1.23.4 in functon compute_color() of jquant2.c. NOTE: this is disputed by the supplier because there was not reasonable evidence to determine the existence of a vulnerability or identify the affected product.

A floating point exception (divide-by-zero) vulnerability was discovered in mupdf 1.23.4 in function pnm_binary_read_image() of load-pnm.c when fz_colorspace_n returns zero.

A floating point exception (divide-by-zero) vulnerability was discovered in Artifex MuPDF 1.23.4 in function bmp_decompress_rle4() of load-bmp.c.

The intersect function in base/gxfill.c in Artifex Software, Inc. Ghostscript 9.20 allows remote attackers to cause a denial of service (divide-by-zero error and application crash) via a crafted file.

In the Linux kernel, the following vulnerability has been resolved: drm/modes: Avoid divide by zero harder in drm_mode_vrefresh() drm_mode_vrefresh() is trying to avoid divide by zero by checking whether htotal or vtotal are zero. But we may still end up with a div-by-zero of vtotal*htotal*...

QEMU through 8.0.0 could trigger a division by zero in scsi_disk_reset in hw/scsi/scsi-disk.c because scsi_disk_emulate_mode_select does not prevent s->qdev.blocksize from being 256. This stops QEMU and the guest immediately.

In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Initialize denominators' default to 1 [WHAT & HOW] Variables used as denominators and maybe not assigned to other values, should not be 0. Change their default to 1 so they are never 0. This fixes 10 DIVIDE_BY_ZERO issues reported by Coverity.

In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Check denominator pbn_div before used [WHAT & HOW] A denominator cannot be 0, and is checked before used. This fixes 1 DIVIDE_BY_ZERO issue reported by Coverity.

In the Linux kernel, the following vulnerability has been resolved: netfilter: nft_limit: avoid possible divide error in nft_limit_init div_u64() divides u64 by u32. nft_limit_init() wants to divide u64 by u64, use the appropriate math function (div64_u64) divide error: 0000 [#1] PREEMPT SMP KASAN CPU: 1 PID: 8390 Comm: syz-executor188 Not tainted 5.12.0-rc4-syzkaller #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 01/01/2011 RIP: 0010:div_u64_rem include/linux/math64.h:28 [inline] RIP: 0010:div_u64 include/linux/math64.h:127 [inline] RIP: 0010:nft_limit_init+0x2a2/0x5e0 net/netfilter/nft_limit.c:85 Code: ef 4c 01 eb 41 0f 92 c7 48 89 de e8 38 a5 22 fa 4d 85 ff 0f 85 97 02 00 00 e8 ea 9e 22 fa 4c 0f af f3 45 89 ed 31 d2 4c 89 f0 <49> f7 f5 49 89 c6 e8 d3 9e 22 fa 48 8d 7d 48 48 b8 00 00 00 00 00 RSP: 0018:ffffc90009447198 EFLAGS: 00010246 RAX: 0000000000000000 RBX: 0000200000000000 RCX: 0000000000000000 RDX: 0000000000000000 RSI: ffffffff875152e6 RDI: 0000000000000003 RBP: ffff888020f80908 R08: 0000200000000000 R09: 0000000000000000 R10: ffffffff875152d8 R11: 0000000000000000 R12: ffffc90009447270 R13: 0000000000000000 R14: 0000000000000000 R15: 0000000000000000 FS: 000000000097a300(0000) GS:ffff8880b9d00000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00000000200001c4 CR3: 0000000026a52000 CR4: 00000000001506e0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: nf_tables_newexpr net/netfilter/nf_tables_api.c:2675 [inline] nft_expr_init+0x145/0x2d0 net/netfilter/nf_tables_api.c:2713 nft_set_elem_expr_alloc+0x27/0x280 net/netfilter/nf_tables_api.c:5160 nf_tables_newset+0x1997/0x3150 net/netfilter/nf_tables_api.c:4321 nfnetlink_rcv_batch+0x85a/0x21b0 net/netfilter/nfnetlink.c:456 nfnetlink_rcv_skb_batch net/netfilter/nfnetlink.c:580 [inline] nfnetlink_rcv+0x3af/0x420 net/netfilter/nfnetlink.c:598 netlink_unicast_kernel net/netlink/af_netlink.c:1312 [inline] netlink_unicast+0x533/0x7d0 net/netlink/af_netlink.c:1338 netlink_sendmsg+0x856/0xd90 net/netlink/af_netlink.c:1927 sock_sendmsg_nosec net/socket.c:654 [inline] sock_sendmsg+0xcf/0x120 net/socket.c:674 ____sys_sendmsg+0x6e8/0x810 net/socket.c:2350 ___sys_sendmsg+0xf3/0x170 net/socket.c:2404 __sys_sendmsg+0xe5/0x1b0 net/socket.c:2433 do_syscall_64+0x2d/0x70 arch/x86/entry/common.c:46 entry_SYSCALL_64_after_hwframe+0x44/0xae

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In the Linux kernel, the following vulnerability has been resolved: RDMA/core: Prevent divide-by-zero error triggered by the user The user_entry_size is supplied by the user and later used as a denominator to calculate number of entries. The zero supplied by the user will trigger the following divide-by-zero error: divide error: 0000 [#1] SMP KASAN PTI CPU: 4 PID: 497 Comm: c_repro Not tainted 5.13.0-rc1+ #281 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS rel-1.13.0-0-gf21b5a4aeb02-prebuilt.qemu.org 04/01/2014 RIP: 0010:ib_uverbs_handler_UVERBS_METHOD_QUERY_GID_TABLE+0x1b1/0x510 Code: 87 59 03 00 00 e8 9f ab 1e ff 48 8d bd a8 00 00 00 e8 d3 70 41 ff 44 0f b7 b5 a8 00 00 00 e8 86 ab 1e ff 31 d2 4c 89 f0 31 ff <49> f7 f5 48 89 d6 48 89 54 24 10 48 89 04 24 e8 1b ad 1e ff 48 8b RSP: 0018:ffff88810416f828 EFLAGS: 00010246 RAX: 0000000000000008 RBX: 1ffff1102082df09 RCX: ffffffff82183f3d RDX: 0000000000000000 RSI: ffff888105f2da00 RDI: 0000000000000000 RBP: ffff88810416fa98 R08: 0000000000000001 R09: ffffed102082df5f R10: ffff88810416faf7 R11: ffffed102082df5e R12: 0000000000000000 R13: 0000000000000000 R14: 0000000000000008 R15: ffff88810416faf0 FS: 00007f5715efa740(0000) GS:ffff88811a700000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000000020000840 CR3: 000000010c2e0001 CR4: 0000000000370ea0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 Call Trace: ? ib_uverbs_handler_UVERBS_METHOD_INFO_HANDLES+0x4b0/0x4b0 ib_uverbs_cmd_verbs+0x1546/0x1940 ib_uverbs_ioctl+0x186/0x240 __x64_sys_ioctl+0x38a/0x1220 do_syscall_64+0x3f/0x80 entry_SYSCALL_64_after_hwframe+0x44/0xae

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. 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.

TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. 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. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.UnravelIndex` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by `dims` is not empty. Hence, if one element of `dims` is 0, the implementation does a division by 0. We have patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. 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.

In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Fix division by zero in setup_dsc_config When slice_height is 0, the division by slice_height in the calculation of the number of slices will cause a division by zero driver crash. This leaves the kernel in a state that requires a reboot. This patch adds a check to avoid the division by zero. The stack trace below is for the 6.8.4 Kernel. I reproduced the issue on a Z16 Gen 2 Lenovo Thinkpad with a Apple Studio Display monitor connected via Thunderbolt. The amdgpu driver crashed with this exception when I rebooted the system with the monitor connected. kernel: ? die (arch/x86/kernel/dumpstack.c:421 arch/x86/kernel/dumpstack.c:434 arch/x86/kernel/dumpstack.c:447) kernel: ? do_trap (arch/x86/kernel/traps.c:113 arch/x86/kernel/traps.c:154) kernel: ? setup_dsc_config (drivers/gpu/drm/amd/amdgpu/../display/dc/dsc/dc_dsc.c:1053) amdgpu kernel: ? do_error_trap (./arch/x86/include/asm/traps.h:58 arch/x86/kernel/traps.c:175) kernel: ? setup_dsc_config (drivers/gpu/drm/amd/amdgpu/../display/dc/dsc/dc_dsc.c:1053) amdgpu kernel: ? exc_divide_error (arch/x86/kernel/traps.c:194 (discriminator 2)) kernel: ? setup_dsc_config (drivers/gpu/drm/amd/amdgpu/../display/dc/dsc/dc_dsc.c:1053) amdgpu kernel: ? asm_exc_divide_error (./arch/x86/include/asm/idtentry.h:548) kernel: ? setup_dsc_config (drivers/gpu/drm/amd/amdgpu/../display/dc/dsc/dc_dsc.c:1053) amdgpu kernel: dc_dsc_compute_config (drivers/gpu/drm/amd/amdgpu/../display/dc/dsc/dc_dsc.c:1109) amdgpu After applying this patch, the driver no longer crashes when the monitor is connected and the system is rebooted. I believe this is the same issue reported for 3113.

In the Linux kernel, the following vulnerability has been resolved: block: prevent division by zero in blk_rq_stat_sum() The expression dst->nr_samples + src->nr_samples may have zero value on overflow. It is necessary to add a check to avoid division by zero. Found by Linux Verification Center (linuxtesting.org) with Svace.

In the Linux kernel, the following vulnerability has been resolved: fbmon: prevent division by zero in fb_videomode_from_videomode() The expression htotal * vtotal can have a zero value on overflow. It is necessary to prevent division by zero like in fb_var_to_videomode(). Found by Linux Verification Center (linuxtesting.org) with Svace.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`. The division by 0 occurs deep in Eigen code because the `b` tensor is empty. 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 division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. 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 division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller. 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 the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 0. 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 division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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 denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger a denial of service. 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 runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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 division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller. 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.

A vulnerability has been identified in SIMATIC S7-PLCSIM V5.4 (All versions). An attacker with local access to the system could cause a Denial-of-Service condition in the application when it is used to open a specially crafted file. As a consequence, a divide by zero operation could occur and cause the application to terminate unexpectedly and must be restarted to restore the service.

In the Linux kernel, the following vulnerability has been resolved: fbdev: sis: Error out if pixclock equals zero The userspace program could pass any values to the driver through ioctl() interface. If the driver doesn't check the value of pixclock, it may cause divide-by-zero error. In sisfb_check_var(), var->pixclock is used as a divisor to caculate drate before it is checked against zero. Fix this by checking it at the beginning. This is similar to CVE-2022-3061 in i740fb which was fixed by commit 15cf0b8.

In the Linux kernel, the following vulnerability has been resolved: USB: usb-storage: Prevent divide-by-0 error in isd200_ata_command The isd200 sub-driver in usb-storage uses the HEADS and SECTORS values in the ATA ID information to calculate cylinder and head values when creating a CDB for READ or WRITE commands. The calculation involves division and modulus operations, which will cause a crash if either of these values is 0. While this never happens with a genuine device, it could happen with a flawed or subversive emulation, as reported by the syzbot fuzzer. Protect against this possibility by refusing to bind to the device if either the ATA_ID_HEADS or ATA_ID_SECTORS value in the device's ID information is 0. This requires isd200_Initialization() to return a negative error code when initialization fails; currently it always returns 0 (even when there is an error).

In the Linux kernel, the following vulnerability has been resolved: drm/dp: Fix divide-by-zero regression on DP MST unplug with nouveau Fix a regression when using nouveau and unplugging a StarTech MSTDP122DP DisplayPort 1.2 MST hub (the same regression does not appear when using a Cable Matters DisplayPort 1.4 MST hub). Trace: divide error: 0000 [#1] PREEMPT SMP PTI CPU: 7 PID: 2962 Comm: Xorg Not tainted 6.8.0-rc3+ #744 Hardware name: Razer Blade/DANA_MB, BIOS 01.01 08/31/2018 RIP: 0010:drm_dp_bw_overhead+0xb4/0x110 [drm_display_helper] Code: c6 b8 01 00 00 00 75 61 01 c6 41 0f af f3 41 0f af f1 c1 e1 04 48 63 c7 31 d2 89 ff 48 8b 5d f8 c9 48 0f af f1 48 8d 44 06 ff <48> f7 f7 31 d2 31 c9 31 f6 31 ff 45 31 c0 45 31 c9 45 31 d2 45 31 RSP: 0018:ffffb2c5c211fa30 EFLAGS: 00010206 RAX: ffffffffffffffff RBX: 0000000000000000 RCX: 0000000000f59b00 RDX: 0000000000000000 RSI: 0000000000000000 RDI: 0000000000000000 RBP: ffffb2c5c211fa48 R08: 0000000000000001 R09: 0000000000000020 R10: 0000000000000004 R11: 0000000000000000 R12: 0000000000023b4a R13: ffff91d37d165800 R14: ffff91d36fac6d80 R15: ffff91d34a764010 FS: 00007f4a1ca3fa80(0000) GS:ffff91d6edbc0000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000559491d49000 CR3: 000000011d180002 CR4: 00000000003706f0 Call Trace: <TASK> ? show_regs+0x6d/0x80 ? die+0x37/0xa0 ? do_trap+0xd4/0xf0 ? do_error_trap+0x71/0xb0 ? drm_dp_bw_overhead+0xb4/0x110 [drm_display_helper] ? exc_divide_error+0x3a/0x70 ? drm_dp_bw_overhead+0xb4/0x110 [drm_display_helper] ? asm_exc_divide_error+0x1b/0x20 ? drm_dp_bw_overhead+0xb4/0x110 [drm_display_helper] ? drm_dp_calc_pbn_mode+0x2e/0x70 [drm_display_helper] nv50_msto_atomic_check+0xda/0x120 [nouveau] drm_atomic_helper_check_modeset+0xa87/0xdf0 [drm_kms_helper] drm_atomic_helper_check+0x19/0xa0 [drm_kms_helper] nv50_disp_atomic_check+0x13f/0x2f0 [nouveau] drm_atomic_check_only+0x668/0xb20 [drm] ? drm_connector_list_iter_next+0x86/0xc0 [drm] drm_atomic_commit+0x58/0xd0 [drm] ? __pfx___drm_printfn_info+0x10/0x10 [drm] drm_atomic_connector_commit_dpms+0xd7/0x100 [drm] drm_mode_obj_set_property_ioctl+0x1c5/0x450 [drm] ? __pfx_drm_connector_property_set_ioctl+0x10/0x10 [drm] drm_connector_property_set_ioctl+0x3b/0x60 [drm] drm_ioctl_kernel+0xb9/0x120 [drm] drm_ioctl+0x2d0/0x550 [drm] ? __pfx_drm_connector_property_set_ioctl+0x10/0x10 [drm] nouveau_drm_ioctl+0x61/0xc0 [nouveau] __x64_sys_ioctl+0xa0/0xf0 do_syscall_64+0x76/0x140 ? do_syscall_64+0x85/0x140 ? do_syscall_64+0x85/0x140 entry_SYSCALL_64_after_hwframe+0x6e/0x76 RIP: 0033:0x7f4a1cd1a94f Code: 00 48 89 44 24 18 31 c0 48 8d 44 24 60 c7 04 24 10 00 00 00 48 89 44 24 08 48 8d 44 24 20 48 89 44 24 10 b8 10 00 00 00 0f 05 <41> 89 c0 3d 00 f0 ff ff 77 1f 48 8b 44 24 18 64 48 2b 04 25 28 00 RSP: 002b:00007ffd2f1df520 EFLAGS: 00000246 ORIG_RAX: 0000000000000010 RAX: ffffffffffffffda RBX: 00007ffd2f1df5b0 RCX: 00007f4a1cd1a94f RDX: 00007ffd2f1df5b0 RSI: 00000000c01064ab RDI: 000000000000000f RBP: 00000000c01064ab R08: 000056347932deb8 R09: 000056347a7d99c0 R10: 0000000000000000 R11: 0000000000000246 R12: 000056347938a220 R13: 000000000000000f R14: 0000563479d9f3f0 R15: 0000000000000000 </TASK> Modules linked in: rfcomm xt_conntrack nft_chain_nat xt_MASQUERADE nf_nat nf_conntrack_netlink nf_conntrack nf_defrag_ipv6 nf_defrag_ipv4 xfrm_user xfrm_algo xt_addrtype nft_compat nf_tables nfnetlink br_netfilter bridge stp llc ccm cmac algif_hash overlay algif_skcipher af_alg bnep binfmt_misc snd_sof_pci_intel_cnl snd_sof_intel_hda_common snd_soc_hdac_hda snd_sof_pci snd_sof_xtensa_dsp snd_sof_intel_hda snd_sof snd_sof_utils snd_soc_acpi_intel_match snd_soc_acpi snd_soc_core snd_compress snd_sof_intel_hda_mlink snd_hda_ext_core iwlmvm intel_rapl_msr intel_rapl_common intel_tcc_cooling x86_pkg_temp_thermal intel_powerclamp mac80211 coretemp kvm_intel snd_hda_codec_hdmi kvm snd_hda_ ---truncated---

In the Linux kernel, the following vulnerability has been resolved: staging: iio: frequency: ad9834: Validate frequency parameter value In ad9834_write_frequency() clk_get_rate() can return 0. In such case ad9834_calc_freqreg() call will lead to division by zero. Checking 'if (fout > (clk_freq / 2))' doesn't protect in case of 'fout' is 0. ad9834_write_frequency() is called from ad9834_write(), where fout is taken from text buffer, which can contain any value. Modify parameters checking. Found by Linux Verification Center (linuxtesting.org) with SVACE.

In the Linux kernel, the following vulnerability has been resolved: ext4: avoid dividing by 0 in mb_update_avg_fragment_size() when block bitmap corrupt Determine if bb_fragments is 0 instead of determining bb_free to eliminate the risk of dividing by zero when the block bitmap is corrupted.

In the Linux kernel, the following vulnerability has been resolved: spi: hisi-kunpeng: Add verification for the max_frequency provided by the firmware If the value of max_speed_hz is 0, it may cause a division by zero error in hisi_calc_effective_speed(). The value of max_speed_hz is provided by firmware. Firmware is generally considered as a trusted domain. However, as division by zero errors can cause system failure, for defense measure, the value of max_speed is validated here. So 0 is regarded as invalid and an error code is returned.

In the Linux kernel, the following vulnerability has been resolved: mm/mglru: fix div-by-zero in vmpressure_calc_level() evict_folios() uses a second pass to reclaim folios that have gone through page writeback and become clean before it finishes the first pass, since folio_rotate_reclaimable() cannot handle those folios due to the isolation. The second pass tries to avoid potential double counting by deducting scan_control->nr_scanned. However, this can result in underflow of nr_scanned, under a condition where shrink_folio_list() does not increment nr_scanned, i.e., when folio_trylock() fails. The underflow can cause the divisor, i.e., scale=scanned+reclaimed in vmpressure_calc_level(), to become zero, resulting in the following crash: [exception RIP: vmpressure_work_fn+101] process_one_work at ffffffffa3313f2b Since scan_control->nr_scanned has no established semantics, the potential double counting has minimal risks. Therefore, fix the problem by not deducting scan_control->nr_scanned in evict_folios().

In the Linux kernel, the following vulnerability has been resolved: ad7780: fix division by zero in ad7780_write_raw() In the ad7780_write_raw() , val2 can be zero, which might lead to a division by zero error in DIV_ROUND_CLOSEST(). The ad7780_write_raw() is based on iio_info's write_raw. While val is explicitly declared that can be zero (in read mode), val2 is not specified to be non-zero.

The cirrus_do_copy function in hw/display/cirrus_vga.c in QEMU (aka Quick Emulator), when cirrus graphics mode is VGA, allows local guest OS privileged users to cause a denial of service (divide-by-zero error and QEMU process crash) via vectors involving blit pitch values.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. 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 the Linux kernel, the following vulnerability has been resolved: padata: Fix possible divide-by-0 panic in padata_mt_helper() We are hit with a not easily reproducible divide-by-0 panic in padata.c at bootup time. [ 10.017908] Oops: divide error: 0000 1 PREEMPT SMP NOPTI [ 10.017908] CPU: 26 PID: 2627 Comm: kworker/u1666:1 Not tainted 6.10.0-15.el10.x86_64 #1 [ 10.017908] Hardware name: Lenovo ThinkSystem SR950 [7X12CTO1WW]/[7X12CTO1WW], BIOS [PSE140J-2.30] 07/20/2021 [ 10.017908] Workqueue: events_unbound padata_mt_helper [ 10.017908] RIP: 0010:padata_mt_helper+0x39/0xb0 : [ 10.017963] Call Trace: [ 10.017968] <TASK> [ 10.018004] ? padata_mt_helper+0x39/0xb0 [ 10.018084] process_one_work+0x174/0x330 [ 10.018093] worker_thread+0x266/0x3a0 [ 10.018111] kthread+0xcf/0x100 [ 10.018124] ret_from_fork+0x31/0x50 [ 10.018138] ret_from_fork_asm+0x1a/0x30 [ 10.018147] </TASK> Looking at the padata_mt_helper() function, the only way a divide-by-0 panic can happen is when ps->chunk_size is 0. The way that chunk_size is initialized in padata_do_multithreaded(), chunk_size can be 0 when the min_chunk in the passed-in padata_mt_job structure is 0. Fix this divide-by-0 panic by making sure that chunk_size will be at least 1 no matter what the input parameters are.

In the Linux kernel, the following vulnerability has been resolved: serial: core: check uartclk for zero to avoid divide by zero Calling ioctl TIOCSSERIAL with an invalid baud_base can result in uartclk being zero, which will result in a divide by zero error in uart_get_divisor(). The check for uartclk being zero in uart_set_info() needs to be done before other settings are made as subsequent calls to ioctl TIOCSSERIAL for the same port would be impacted if the uartclk check was done where uartclk gets set. Oops: divide error: 0000 PREEMPT SMP KASAN PTI RIP: 0010:uart_get_divisor (drivers/tty/serial/serial_core.c:580) Call Trace: <TASK> serial8250_get_divisor (drivers/tty/serial/8250/8250_port.c:2576 drivers/tty/serial/8250/8250_port.c:2589) serial8250_do_set_termios (drivers/tty/serial/8250/8250_port.c:502 drivers/tty/serial/8250/8250_port.c:2741) serial8250_set_termios (drivers/tty/serial/8250/8250_port.c:2862) uart_change_line_settings (./include/linux/spinlock.h:376 ./include/linux/serial_core.h:608 drivers/tty/serial/serial_core.c:222) uart_port_startup (drivers/tty/serial/serial_core.c:342) uart_startup (drivers/tty/serial/serial_core.c:368) uart_set_info (drivers/tty/serial/serial_core.c:1034) uart_set_info_user (drivers/tty/serial/serial_core.c:1059) tty_set_serial (drivers/tty/tty_io.c:2637) tty_ioctl (drivers/tty/tty_io.c:2647 drivers/tty/tty_io.c:2791) __x64_sys_ioctl (fs/ioctl.c:52 fs/ioctl.c:907 fs/ioctl.c:893 fs/ioctl.c:893) do_syscall_64 (arch/x86/entry/common.c:52 (discriminator 1) arch/x86/entry/common.c:83 (discriminator 1)) entry_SYSCALL_64_after_hwframe (arch/x86/entry/entry_64.S:130) Rule: add