A Floating point exception (division-by-zero) flaw was found in Mupdf for zero width pages in muraster.c. It is fixed in Mupdf-1.20.0-rc1 upstream.

NVIDIA CUDA Toolkit for Linux and Windows contains a vulnerability in cuobjdump, where a division-by-zero error may enable a user to cause a crash, which may lead to a limited denial of service.

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: ALSA: firewire-lib: Avoid division by zero in apply_constraint_to_size() The step variable is initialized to zero. It is changed in the loop, but if it's not changed it will remain zero. Add a variable check before the division. The observed behavior was introduced by commit 826b5de90c0b ("ALSA: firewire-lib: fix insufficient PCM rule for period/buffer size"), and it is difficult to show that any of the interval parameters will satisfy the snd_interval_test() condition with data from the amdtp_rate_table[] table. Found by Linux Verification Center (linuxtesting.org) with SVACE.

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: net: stmmac: Fix zero-division error when disabling tc cbs The commit b8c43360f6e4 ("net: stmmac: No need to calculate speed divider when offload is disabled") allows the "port_transmit_rate_kbps" to be set to a value of 0, which is then passed to the "div_s64" function when tc-cbs is disabled. This leads to a zero-division error. When tc-cbs is disabled, the idleslope, sendslope, and credit values the credit values are not required to be configured. Therefore, adding a return statement after setting the txQ mode to DCB when tc-cbs is disabled would prevent a zero-division error.

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: check stripe size compatibility on remount as well We disable stripe size in __ext4_fill_super if it is not a multiple of the cluster ratio however this check is missed when trying to remount. This can leave us with cases where stripe < cluster_ratio after remount:set making EXT4_B2C(sbi->s_stripe) become 0 that can cause some unforeseen bugs like divide by 0. Fix that by adding the check in remount path as well.

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.

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.

In the Linux kernel, the following vulnerability has been resolved: drm/amd/display: Check denominator crb_pipes before used [WHAT & HOW] A denominator cannot be 0, and is checked before used. This fixes 2 DIVIDE_BY_ZERO issues reported by Coverity.

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

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: 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: 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.

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

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: 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 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 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.

In the Linux kernel, the following vulnerability has been resolved: video: fbdev: cirrusfb: check pixclock to avoid divide by zero Do a sanity check on pixclock value to avoid divide by zero. If the pixclock value is zero, the cirrusfb driver will round up pixclock to get the derived frequency as close to maxclock as possible. Syzkaller reported a divide error in cirrusfb_check_pixclock. divide error: 0000 [#1] SMP KASAN PTI CPU: 0 PID: 14938 Comm: cirrusfb_test Not tainted 5.15.0-rc6 #1 Hardware name: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.11.0-2 RIP: 0010:cirrusfb_check_var+0x6f1/0x1260 Call Trace: fb_set_var+0x398/0xf90 do_fb_ioctl+0x4b8/0x6f0 fb_ioctl+0xeb/0x130 __x64_sys_ioctl+0x19d/0x220 do_syscall_64+0x3a/0x80 entry_SYSCALL_64_after_hwframe+0x44/0xae

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 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 the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.

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 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 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. 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 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 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.

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

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 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 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 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.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.

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.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 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.

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. 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.

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.

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.

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: fbdev: savage: 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. Although pixclock is checked in savagefb_decode_var(), but it is not checked properly in savagefb_probe(). Fix this by checking whether pixclock is zero in the function savagefb_check_var() before info->var.pixclock is used as the divisor. This is similar to CVE-2022-3061 in i740fb which was fixed by commit 15cf0b8.

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.

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.

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.