| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| In the Linux kernel, the following vulnerability has been resolved:
usb: gadget: f_fs: Clear ffs_eventfd in ffs_data_clear.
ffs_data_clear is indirectly called from both ffs_fs_kill_sb and
ffs_ep0_release, so it ends up being called twice when userland closes ep0
and then unmounts f_fs.
If userland provided an eventfd along with function's USB descriptors, it
ends up calling eventfd_ctx_put as many times, causing a refcount
underflow.
NULL-ify ffs_eventfd to prevent these extraneous eventfd_ctx_put calls.
Also, set epfiles to NULL right after de-allocating it, for readability.
For completeness, ffs_data_clear actually ends up being called thrice, the
last call being before the whole ffs structure gets freed, so when this
specific sequence happens there is a second underflow happening (but not
being reported):
/sys/kernel/debug/tracing# modprobe usb_f_fs
/sys/kernel/debug/tracing# echo ffs_data_clear > set_ftrace_filter
/sys/kernel/debug/tracing# echo function > current_tracer
/sys/kernel/debug/tracing# echo 1 > tracing_on
(setup gadget, run and kill function userland process, teardown gadget)
/sys/kernel/debug/tracing# echo 0 > tracing_on
/sys/kernel/debug/tracing# cat trace
smartcard-openp-436 [000] ..... 1946.208786: ffs_data_clear <-ffs_data_closed
smartcard-openp-431 [000] ..... 1946.279147: ffs_data_clear <-ffs_data_closed
smartcard-openp-431 [000] .n... 1946.905512: ffs_data_clear <-ffs_data_put
Warning output corresponding to above trace:
[ 1946.284139] WARNING: CPU: 0 PID: 431 at lib/refcount.c:28 refcount_warn_saturate+0x110/0x15c
[ 1946.293094] refcount_t: underflow; use-after-free.
[ 1946.298164] Modules linked in: usb_f_ncm(E) u_ether(E) usb_f_fs(E) hci_uart(E) btqca(E) btrtl(E) btbcm(E) btintel(E) bluetooth(E) nls_ascii(E) nls_cp437(E) vfat(E) fat(E) bcm2835_v4l2(CE) bcm2835_mmal_vchiq(CE) videobuf2_vmalloc(E) videobuf2_memops(E) sha512_generic(E) videobuf2_v4l2(E) sha512_arm(E) videobuf2_common(E) videodev(E) cpufreq_dt(E) snd_bcm2835(CE) brcmfmac(E) mc(E) vc4(E) ctr(E) brcmutil(E) snd_soc_core(E) snd_pcm_dmaengine(E) drbg(E) snd_pcm(E) snd_timer(E) snd(E) soundcore(E) drm_kms_helper(E) cec(E) ansi_cprng(E) rc_core(E) syscopyarea(E) raspberrypi_cpufreq(E) sysfillrect(E) sysimgblt(E) cfg80211(E) max17040_battery(OE) raspberrypi_hwmon(E) fb_sys_fops(E) regmap_i2c(E) ecdh_generic(E) rfkill(E) ecc(E) bcm2835_rng(E) rng_core(E) vchiq(CE) leds_gpio(E) libcomposite(E) fuse(E) configfs(E) ip_tables(E) x_tables(E) autofs4(E) ext4(E) crc16(E) mbcache(E) jbd2(E) crc32c_generic(E) sdhci_iproc(E) sdhci_pltfm(E) sdhci(E)
[ 1946.399633] CPU: 0 PID: 431 Comm: smartcard-openp Tainted: G C OE 5.15.0-1-rpi #1 Debian 5.15.3-1
[ 1946.417950] Hardware name: BCM2835
[ 1946.425442] Backtrace:
[ 1946.432048] [<c08d60a0>] (dump_backtrace) from [<c08d62ec>] (show_stack+0x20/0x24)
[ 1946.448226] r7:00000009 r6:0000001c r5:c04a948c r4:c0a64e2c
[ 1946.458412] [<c08d62cc>] (show_stack) from [<c08d9ae0>] (dump_stack+0x28/0x30)
[ 1946.470380] [<c08d9ab8>] (dump_stack) from [<c0123500>] (__warn+0xe8/0x154)
[ 1946.482067] r5:c04a948c r4:c0a71dc8
[ 1946.490184] [<c0123418>] (__warn) from [<c08d6948>] (warn_slowpath_fmt+0xa0/0xe4)
[ 1946.506758] r7:00000009 r6:0000001c r5:c0a71dc8 r4:c0a71e04
[ 1946.517070] [<c08d68ac>] (warn_slowpath_fmt) from [<c04a948c>] (refcount_warn_saturate+0x110/0x15c)
[ 1946.535309] r8:c0100224 r7:c0dfcb84 r6:ffffffff r5:c3b84c00 r4:c24a17c0
[ 1946.546708] [<c04a937c>] (refcount_warn_saturate) from [<c0380134>] (eventfd_ctx_put+0x48/0x74)
[ 1946.564476] [<c03800ec>] (eventfd_ctx_put) from [<bf5464e8>] (ffs_data_clear+0xd0/0x118 [usb_f_fs])
[ 1946.582664] r5:c3b84c00 r4:c2695b00
[ 1946.590668] [<bf546418>] (ffs_data_clear [usb_f_fs]) from [<bf547cc0>] (ffs_data_closed+0x9c/0x150 [usb_f_fs])
[ 1946.609608] r5:bf54d014 r4:c2695b00
[ 1946.617522] [<bf547c24>] (ffs_data_closed [usb_f_fs]) from [<bf547da0>] (ffs_fs_kill_sb+0x2c/0x30 [usb_f_fs])
[ 1946.636217] r7:c0dfcb
---truncated--- |
| 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 |
| In the Linux kernel, the following vulnerability has been resolved:
netfilter: nftables: clone set element expression template
memcpy() breaks when using connlimit in set elements. Use
nft_expr_clone() to initialize the connlimit expression list, otherwise
connlimit garbage collector crashes when walking on the list head copy.
[ 493.064656] Workqueue: events_power_efficient nft_rhash_gc [nf_tables]
[ 493.064685] RIP: 0010:find_or_evict+0x5a/0x90 [nf_conncount]
[ 493.064694] Code: 2b 43 40 83 f8 01 77 0d 48 c7 c0 f5 ff ff ff 44 39 63 3c 75 df 83 6d 18 01 48 8b 43 08 48 89 de 48 8b 13 48 8b 3d ee 2f 00 00 <48> 89 42 08 48 89 10 48 b8 00 01 00 00 00 00 ad de 48 89 03 48 83
[ 493.064699] RSP: 0018:ffffc90000417dc0 EFLAGS: 00010297
[ 493.064704] RAX: 0000000000000000 RBX: ffff888134f38410 RCX: 0000000000000000
[ 493.064708] RDX: 0000000000000000 RSI: ffff888134f38410 RDI: ffff888100060cc0
[ 493.064711] RBP: ffff88812ce594a8 R08: ffff888134f38438 R09: 00000000ebb9025c
[ 493.064714] R10: ffffffff8219f838 R11: 0000000000000017 R12: 0000000000000001
[ 493.064718] R13: ffffffff82146740 R14: ffff888134f38410 R15: 0000000000000000
[ 493.064721] FS: 0000000000000000(0000) GS:ffff88840e440000(0000) knlGS:0000000000000000
[ 493.064725] CS: 0010 DS: 0000 ES: 0000 CR0: 0000000080050033
[ 493.064729] CR2: 0000000000000008 CR3: 00000001330aa002 CR4: 00000000001706e0
[ 493.064733] Call Trace:
[ 493.064737] nf_conncount_gc_list+0x8f/0x150 [nf_conncount]
[ 493.064746] nft_rhash_gc+0x106/0x390 [nf_tables] |
| LibJS in Ladybird before f5a6704 mishandles the freeing of the vector that arguments_list references, leading to a use-after-free, and allowing remote attackers to execute arbitrary code via a crafted .js file. NOTE: the GitHub README says "Ladybird is in a pre-alpha state, and only suitable for use by developers." |
| A vulnerability was found in EasyCorp EasyAdmin up to 4.8.9. It has been declared as problematic. Affected by this vulnerability is the function Autocomplete of the file assets/js/autocomplete.js of the component Autocomplete. The manipulation of the argument item leads to cross site scripting. The attack can be launched remotely. Upgrading to version 4.8.10 is able to address this issue. The identifier of the patch is 127436e4c3f56276d548070f99e61b7234200a11. It is recommended to upgrade the affected component. The identifier VDB-258613 was assigned to this vulnerability. |
| Cross Site Scripting vulnerability in ITFlow.org before commit v.432488eca3998c5be6b6b9e8f8ba01f54bc12378 allows a remtoe attacker to execute arbitrary code and obtain sensitive information via the settings.php, settings+company.php, settings_defaults.php,settings_integrations.php, settings_invoice.php, settings_localization.php, settings_mail.php components. |
| Frontier is Substrate's Ethereum compatibility layer. Prior to commit number `8a93fdc6c9f4eb1d2f2a11b7ff1d12d70bf5a664`, a bug in Frontier's MODEXP precompile implementation can cause an integer underflow in certain conditions. This will cause a node crash for debug builds. For release builds (and production WebAssembly binaries), the impact is limited as it can only cause a normal EVM out-of-gas. Users who do not use MODEXP precompile in their runtime are not impacted. A patch is available in pull request #549. |
| On all versions of 16.1.x, 15.1.x, 14.1.x, 13.1.x, 12.1.x, and 11.6.x of F5 BIG-IP, and F5 BIG-IP Guided Configuration (GC) all versions prior to 9.0, a stored cross-site scripting (XSS) vulnerability exists in an undisclosed page of the BIG-IP Configuration utility that allows an attacker to execute JavaScript in the context of the currently logged-in user. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated |
| Weave GitOps is a simple open source developer platform for people who want cloud native applications, without needing Kubernetes expertise. A vulnerability in the logging of Weave GitOps could allow an authenticated remote attacker to view sensitive cluster configurations, aka KubeConfg, of registered Kubernetes clusters, including the service account tokens in plain text from Weave GitOps's pod logs on the management cluster. An unauthorized remote attacker can also view these sensitive configurations from external log storage if enabled by the management cluster. This vulnerability is due to the client factory dumping cluster configurations and their service account tokens when the cluster manager tries to connect to an API server of a registered cluster, and a connection error occurs. An attacker could exploit this vulnerability by either accessing logs of a pod of Weave GitOps, or from external log storage and obtaining all cluster configurations of registered clusters. A successful exploit could allow the attacker to use those cluster configurations to manage the registered Kubernetes clusters. This vulnerability has been fixed by commit 567356f471353fb5c676c77f5abc2a04631d50ca. Users should upgrade to Weave GitOps core version v0.8.1-rc.6 or newer. There is no known workaround for this vulnerability. |
| TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `QuantizedBiasAdd` is given `min_input`, `max_input`, `min_bias`, `max_bias` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `SparseBincount` is given inputs for `indices`, `values`, and `dense_shape` that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 40adbe4dd15b582b0210dfbf40c243a62f5119fa. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `Save` or `SaveSlices` is run over tensors of an unsupported `dtype`, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 5dd7b86b84a864b834c6fa3d7f9f51c87efa99d4. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. When `mlir::tfg::GraphDefImporter::ConvertNodeDef` tries to convert NodeDefs without an op name, it crashes. We have patched the issue in GitHub commit a0f0b9a21c9270930457095092f558fbad4c03e5. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
| TensorFlow is an open source platform for machine learning. When `mlir::tfg::TFOp::nameAttr` receives null type list attributes, it crashes. We have patched the issue in GitHub commits 3a754740d5414e362512ee981eefba41561a63a6 and a0f0b9a21c9270930457095092f558fbad4c03e5. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. |
