In the Linux kernel, the following vulnerability has been resolved: fbdev: savage: Handle err return when savagefb_check_var failed The commit 04e5eac8f3ab("fbdev: savage: Error out if pixclock equals zero") checks the value of pixclock to avoid divide-by-zero error. However the function savagefb_probe doesn't handle the error return of savagefb_check_var. When pixclock is 0, it will cause divide-by-zero error.

In ImfChromaticities.cpp routine RGBtoXYZ(), there are some division operations such as `float Z = (1 - chroma.white.x - chroma.white.y) * Y / chroma.white.y;` and `chroma.green.y * (X + Z))) / d;` but the divisor is not checked for a 0 value. A specially crafted file could trigger a divide-by-zero condition which could affect the availability of programs linked with OpenEXR.

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: 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. 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 `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9. 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 `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. 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 fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. 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 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.

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 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.

oss_write in audio/ossaudio.c in QEMU before 5.0.0 mishandles a buffer position.

A memory leak flaw and potential divide by zero and Integer overflow was found in the Linux kernel V4L2 and vivid test code functionality. This issue occurs when a user triggers ioctls, such as VIDIOC_S_DV_TIMINGS ioctl. This could allow a local user to crash the system if vivid test code enabled.

A divide-by-zero in VirtIO network device emulation in BitVisor from commit 108df6 (2020-05-20) to commit 480907 (2025-07-06) allows local attackers to cause a denial of service (host hypervisor crash) via a crafted PCI configuration space access.

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.

In the Linux kernel, the following vulnerability has been resolved: tcp: defer shutdown(SEND_SHUTDOWN) for TCP_SYN_RECV sockets TCP_SYN_RECV state is really special, it is only used by cross-syn connections, mostly used by fuzzers. In the following crash [1], syzbot managed to trigger a divide by zero in tcp_rcv_space_adjust() A socket makes the following state transitions, without ever calling tcp_init_transfer(), meaning tcp_init_buffer_space() is also not called. TCP_CLOSE connect() TCP_SYN_SENT TCP_SYN_RECV shutdown() -> tcp_shutdown(sk, SEND_SHUTDOWN) TCP_FIN_WAIT1 To fix this issue, change tcp_shutdown() to not perform a TCP_SYN_RECV -> TCP_FIN_WAIT1 transition, which makes no sense anyway. When tcp_rcv_state_process() later changes socket state from TCP_SYN_RECV to TCP_ESTABLISH, then look at sk->sk_shutdown to finally enter TCP_FIN_WAIT1 state, and send a FIN packet from a sane socket state. This means tcp_send_fin() can now be called from BH context, and must use GFP_ATOMIC allocations. [1] divide error: 0000 [#1] PREEMPT SMP KASAN NOPTI CPU: 1 PID: 5084 Comm: syz-executor358 Not tainted 6.9.0-rc6-syzkaller-00022-g98369dccd2f8 #0 Hardware name: Google Google Compute Engine/Google Compute Engine, BIOS Google 03/27/2024 RIP: 0010:tcp_rcv_space_adjust+0x2df/0x890 net/ipv4/tcp_input.c:767 Code: e3 04 4c 01 eb 48 8b 44 24 38 0f b6 04 10 84 c0 49 89 d5 0f 85 a5 03 00 00 41 8b 8e c8 09 00 00 89 e8 29 c8 48 0f af c3 31 d2 <48> f7 f1 48 8d 1c 43 49 8d 96 76 08 00 00 48 89 d0 48 c1 e8 03 48 RSP: 0018:ffffc900031ef3f0 EFLAGS: 00010246 RAX: 0c677a10441f8f42 RBX: 000000004fb95e7e RCX: 0000000000000000 RDX: 0000000000000000 RSI: 0000000000000000 RDI: 0000000000000000 RBP: 0000000027d4b11f R08: ffffffff89e535a4 R09: 1ffffffff25e6ab7 R10: dffffc0000000000 R11: ffffffff8135e920 R12: ffff88802a9f8d30 R13: dffffc0000000000 R14: ffff88802a9f8d00 R15: 1ffff1100553f2da FS: 00005555775c0380(0000) GS:ffff8880b9500000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00007f1155bf2304 CR3: 000000002b9f2000 CR4: 0000000000350ef0 Call Trace: <TASK> tcp_recvmsg_locked+0x106d/0x25a0 net/ipv4/tcp.c:2513 tcp_recvmsg+0x25d/0x920 net/ipv4/tcp.c:2578 inet6_recvmsg+0x16a/0x730 net/ipv6/af_inet6.c:680 sock_recvmsg_nosec net/socket.c:1046 [inline] sock_recvmsg+0x109/0x280 net/socket.c:1068 ____sys_recvmsg+0x1db/0x470 net/socket.c:2803 ___sys_recvmsg net/socket.c:2845 [inline] do_recvmmsg+0x474/0xae0 net/socket.c:2939 __sys_recvmmsg net/socket.c:3018 [inline] __do_sys_recvmmsg net/socket.c:3041 [inline] __se_sys_recvmmsg net/socket.c:3034 [inline] __x64_sys_recvmmsg+0x199/0x250 net/socket.c:3034 do_syscall_x64 arch/x86/entry/common.c:52 [inline] do_syscall_64+0xf5/0x240 arch/x86/entry/common.c:83 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7faeb6363db9 Code: 28 00 00 00 75 05 48 83 c4 28 c3 e8 c1 17 00 00 90 48 89 f8 48 89 f7 48 89 d6 48 89 ca 4d 89 c2 4d 89 c8 4c 8b 4c 24 08 0f 05 <48> 3d 01 f0 ff ff 73 01 c3 48 c7 c1 b8 ff ff ff f7 d8 64 89 01 48 RSP: 002b:00007ffcc1997168 EFLAGS: 00000246 ORIG_RAX: 000000000000012b RAX: ffffffffffffffda RBX: 0000000000000000 RCX: 00007faeb6363db9 RDX: 0000000000000001 RSI: 0000000020000bc0 RDI: 0000000000000005 RBP: 0000000000000000 R08: 0000000000000000 R09: 000000000000001c R10: 0000000000000122 R11: 0000000000000246 R12: 0000000000000000 R13: 0000000000000000 R14: 0000000000000001 R15: 0000000000000001

TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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 the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Check if modulo is 0 before dividing. [How & Why] If a value of 0 is read, then this will cause a divide-by-0 panic.

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.

In the Linux kernel, the following vulnerability has been resolved: fbdev: fb_pm2fb: Avoid potential divide by zero error In `do_fb_ioctl()` of fbmem.c, if cmd is FBIOPUT_VSCREENINFO, var will be copied from user, then go through `fb_set_var()` and `info->fbops->fb_check_var()` which could may be `pm2fb_check_var()`. Along the path, `var->pixclock` won't be modified. This function checks whether reciprocal of `var->pixclock` is too high. If `var->pixclock` is zero, there will be a divide by zero error. So, it is necessary to check whether denominator is zero to avoid crash. As this bug is found by Syzkaller, logs are listed below. divide error in pm2fb_check_var Call Trace: <TASK> fb_set_var+0x367/0xeb0 drivers/video/fbdev/core/fbmem.c:1015 do_fb_ioctl+0x234/0x670 drivers/video/fbdev/core/fbmem.c:1110 fb_ioctl+0xdd/0x130 drivers/video/fbdev/core/fbmem.c:1189

In the Linux kernel, the following vulnerability has been resolved: video: fbdev: arkfb: Fix a divide-by-zero bug in ark_set_pixclock() Since the user can control the arguments of the ioctl() from the user space, under special arguments that may result in a divide-by-zero bug in: drivers/video/fbdev/arkfb.c:784: ark_set_pixclock(info, (hdiv * info->var.pixclock) / hmul); with hdiv=1, pixclock=1 and hmul=2 you end up with (1*1)/2 = (int) 0. and then in: drivers/video/fbdev/arkfb.c:504: rv = dac_set_freq(par->dac, 0, 1000000000 / pixclock); we'll get a division-by-zero. The following log can reveal it: divide error: 0000 [#1] PREEMPT SMP KASAN PTI RIP: 0010:ark_set_pixclock drivers/video/fbdev/arkfb.c:504 [inline] RIP: 0010:arkfb_set_par+0x10fc/0x24c0 drivers/video/fbdev/arkfb.c:784 Call Trace: fb_set_var+0x604/0xeb0 drivers/video/fbdev/core/fbmem.c:1034 do_fb_ioctl+0x234/0x670 drivers/video/fbdev/core/fbmem.c:1110 fb_ioctl+0xdd/0x130 drivers/video/fbdev/core/fbmem.c:1189 Fix this by checking the argument of ark_set_pixclock() first.

In the Linux kernel, the following vulnerability has been resolved: linux/dim: Fix divide by 0 in RDMA DIM Fix a divide 0 error in rdma_dim_stats_compare() when prev->cpe_ratio == 0. CallTrace: Hardware name: H3C R4900 G3/RS33M2C9S, BIOS 2.00.37P21 03/12/2020 task: ffff880194b78000 task.stack: ffffc90006714000 RIP: 0010:backport_rdma_dim+0x10e/0x240 [mlx_compat] RSP: 0018:ffff880c10e83ec0 EFLAGS: 00010202 RAX: 0000000000002710 RBX: ffff88096cd7f780 RCX: 0000000000000064 RDX: 0000000000000000 RSI: 0000000000000002 RDI: 0000000000000001 RBP: 0000000000000001 R08: 0000000000000000 R09: 0000000000000000 R10: 0000000000000000 R11: 0000000000000000 R12: 000000001d7c6c09 R13: ffff88096cd7f780 R14: ffff880b174fe800 R15: 0000000000000000 FS: 0000000000000000(0000) GS:ffff880c10e80000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 00000000a0965b00 CR3: 000000000200a003 CR4: 00000000007606e0 DR0: 0000000000000000 DR1: 0000000000000000 DR2: 0000000000000000 DR3: 0000000000000000 DR6: 00000000fffe0ff0 DR7: 0000000000000400 PKRU: 55555554 Call Trace: <IRQ> ib_poll_handler+0x43/0x80 [ib_core] irq_poll_softirq+0xae/0x110 __do_softirq+0xd1/0x28c irq_exit+0xde/0xf0 do_IRQ+0x54/0xe0 common_interrupt+0x8f/0x8f </IRQ> ? cpuidle_enter_state+0xd9/0x2a0 ? cpuidle_enter_state+0xc7/0x2a0 ? do_idle+0x170/0x1d0 ? cpu_startup_entry+0x6f/0x80 ? start_secondary+0x1b9/0x210 ? secondary_startup_64+0xa5/0xb0 Code: 0f 87 e1 00 00 00 8b 4c 24 14 44 8b 43 14 89 c8 4d 63 c8 44 29 c0 99 31 d0 29 d0 31 d2 48 98 48 8d 04 80 48 8d 04 80 48 c1 e0 02 <49> f7 f1 48 83 f8 0a 0f 86 c1 00 00 00 44 39 c1 7f 10 48 89 df RIP: backport_rdma_dim+0x10e/0x240 [mlx_compat] RSP: ffff880c10e83ec0

In the Linux kernel, the following vulnerability has been resolved: wifi: rtw89: 8852a: rfk: fix div 0 exception The DPK is a kind of RF calibration whose algorithm is to fine tune parameters and calibrate, and check the result. If the result isn't good enough, it could adjust parameters and try again. This issue is to read and show the result, but it could be a negative calibration result that causes divisor 0 and core dump. So, fix it by phy_div() that does division only if divisor isn't zero; otherwise, zero is adopted. divide error: 0000 [#1] PREEMPT SMP NOPTI CPU: 1 PID: 728 Comm: wpa_supplicant Not tainted 5.10.114-16019-g462a1661811a #1 <HASH:d024 28> RIP: 0010:rtw8852a_dpk+0x14ae/0x288f [rtw89_core] RSP: 0018:ffffa9bb412a7520 EFLAGS: 00010246 RAX: 0000000000000000 RBX: 0000000000000000 RCX: 0000000000000000 RDX: 0000000000000000 RSI: 00000000000180fc RDI: ffffa141d01023c0 RBP: ffffa9bb412a76a0 R08: 0000000000001319 R09: 00000000ffffff92 R10: ffffffffc0292de3 R11: ffffffffc00d2f51 R12: 0000000000000000 R13: ffffa141d01023c0 R14: ffffffffc0290250 R15: ffffa141d0102638 FS: 00007fa99f5c2740(0000) GS:ffffa142e5e80000(0000) knlGS:0000000000000000 CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033 CR2: 0000000013e8e010 CR3: 0000000110d2c000 CR4: 0000000000750ee0 PKRU: 55555554 Call Trace: rtw89_core_sta_add+0x95/0x9c [rtw89_core <HASH:d239 29>] rtw89_ops_sta_state+0x5d/0x108 [rtw89_core <HASH:d239 29>] drv_sta_state+0x115/0x66f [mac80211 <HASH:81fe 30>] sta_info_insert_rcu+0x45c/0x713 [mac80211 <HASH:81fe 30>] sta_info_insert+0xf/0x1b [mac80211 <HASH:81fe 30>] ieee80211_prep_connection+0x9d6/0xb0c [mac80211 <HASH:81fe 30>] ieee80211_mgd_auth+0x2aa/0x352 [mac80211 <HASH:81fe 30>] cfg80211_mlme_auth+0x160/0x1f6 [cfg80211 <HASH:00cd 31>] nl80211_authenticate+0x2e5/0x306 [cfg80211 <HASH:00cd 31>] genl_rcv_msg+0x371/0x3a1 ? nl80211_stop_sched_scan+0xe5/0xe5 [cfg80211 <HASH:00cd 31>] ? genl_rcv+0x36/0x36 netlink_rcv_skb+0x8a/0xf9 genl_rcv+0x28/0x36 netlink_unicast+0x27b/0x3a0 netlink_sendmsg+0x2aa/0x469 sock_sendmsg_nosec+0x49/0x4d ____sys_sendmsg+0xe5/0x213 __sys_sendmsg+0xec/0x157 ? syscall_enter_from_user_mode+0xd7/0x116 do_syscall_64+0x43/0x55 entry_SYSCALL_64_after_hwframe+0x44/0xa9 RIP: 0033:0x7fa99f6e689b

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.

In the Linux kernel, the following vulnerability has been resolved: video: fbdev: i740fb: Check the argument of i740_calc_vclk() Since the user can control the arguments of the ioctl() from the user space, under special arguments that may result in a divide-by-zero bug. If the user provides an improper 'pixclock' value that makes the argumet of i740_calc_vclk() less than 'I740_RFREQ_FIX', it will cause a divide-by-zero bug in: drivers/video/fbdev/i740fb.c:353 p_best = min(15, ilog2(I740_MAX_VCO_FREQ / (freq / I740_RFREQ_FIX))); The following log can reveal it: divide error: 0000 [#1] PREEMPT SMP KASAN PTI RIP: 0010:i740_calc_vclk drivers/video/fbdev/i740fb.c:353 [inline] RIP: 0010:i740fb_decode_var drivers/video/fbdev/i740fb.c:646 [inline] RIP: 0010:i740fb_set_par+0x163f/0x3b70 drivers/video/fbdev/i740fb.c:742 Call Trace: fb_set_var+0x604/0xeb0 drivers/video/fbdev/core/fbmem.c:1034 do_fb_ioctl+0x234/0x670 drivers/video/fbdev/core/fbmem.c:1110 fb_ioctl+0xdd/0x130 drivers/video/fbdev/core/fbmem.c:1189 Fix this by checking the argument of i740_calc_vclk() first.

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.

Divide by zero in some Intel(R) Graphics Drivers before version 26.20.100.8141 may allow a privileged user to potentially enable a denial of service via local access.

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 cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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.

The Virtio Vring implementation in QEMU allows local OS guest users to cause a denial of service (divide-by-zero error and QEMU process crash) by unsetting vring alignment while updating Virtio rings.

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. 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.

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. 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.

exif_entry_get_value in exif-entry.c in libexif 0.6.21 has a divide-by-zero error.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor 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 cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. 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/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). 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 the Linux kernel, the following vulnerability has been resolved: mm/damon/lru_sort: avoid divide-by-zero in damon_lru_sort_apply_parameters() Patch series "mm/damon: avoid divide-by-zero in DAMON module's parameters application". DAMON's RECLAIM and LRU_SORT modules perform no validation on user-configured parameters during application, which may lead to division-by-zero errors. Avoid the divide-by-zero by adding validation checks when DAMON modules attempt to apply the parameters. This patch (of 2): During the calculation of 'hot_thres' and 'cold_thres', either 'sample_interval' or 'aggr_interval' is used as the divisor, which may lead to division-by-zero errors. Fix it by directly returning -EINVAL when such a case occurs. Additionally, since 'aggr_interval' is already required to be set no smaller than 'sample_interval' in damon_set_attrs(), only the case where 'sample_interval' is zero needs to be checked.

In the Linux kernel, the following vulnerability has been resolved: clk: sunxi-ng: mp: Fix dual-divider clock rate readback When dual-divider clock support was introduced, the P divider offset was left out of the .recalc_rate readback function. This causes the clock rate to become bogus or even zero (possibly due to the P divider being 1, leading to a divide-by-zero). Fix this by incorporating the P divider offset into the calculation.

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. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. 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 the Linux kernel, the following vulnerability has been resolved: drm/amd/pm: Prevent division by zero The user can set any speed value. If speed is greater than UINT_MAX/8, division by zero is possible. Found by Linux Verification Center (linuxtesting.org) with SVACE.

In the Linux kernel, the following vulnerability has been resolved: pwm: mediatek: Prevent divide-by-zero in pwm_mediatek_config() With CONFIG_COMPILE_TEST && !CONFIG_HAVE_CLK, pwm_mediatek_config() has a divide-by-zero in the following line: do_div(resolution, clk_get_rate(pc->clk_pwms[pwm->hwpwm])); due to the fact that the !CONFIG_HAVE_CLK version of clk_get_rate() returns zero. This is presumably just a theoretical problem: COMPILE_TEST overrides the dependency on RALINK which would select COMMON_CLK. Regardless it's a good idea to check for the error explicitly to avoid divide-by-zero. Fixes the following warning: drivers/pwm/pwm-mediatek.o: warning: objtool: .text: unexpected end of section [ukleinek: s/CONFIG_CLK/CONFIG_HAVE_CLK/]

In the Linux kernel, the following vulnerability has been resolved: drm/amd/pm: Prevent division by zero The user can set any speed value. If speed is greater than UINT_MAX/8, division by zero is possible. Found by Linux Verification Center (linuxtesting.org) with SVACE.

In the Linux kernel, the following vulnerability has been resolved: objtool, media: dib8000: Prevent divide-by-zero in dib8000_set_dds() If dib8000_set_dds()'s call to dib8000_read32() returns zero, the result is a divide-by-zero. Prevent that from happening. Fixes the following warning with an UBSAN kernel: drivers/media/dvb-frontends/dib8000.o: warning: objtool: dib8000_tune() falls through to next function dib8096p_cfg_DibRx()

In the Linux kernel, the following vulnerability has been resolved: PM: EM: Fix potential division-by-zero error in em_compute_costs() When the device is of a non-CPU type, table[i].performance won't be initialized in the previous em_init_performance(), resulting in division by zero when calculating costs in em_compute_costs(). Since the 'cost' algorithm is only used for EAS energy efficiency calculations and is currently not utilized by other device drivers, we should add the _is_cpu_device(dev) check to prevent this division-by-zero issue.