CVE Descriptions

io_uring/io-wq: check IO_WQ_BIT_EXIT inside work run loop

In the Linux kernel, the following vulnerability has been resolved: io_uring/io-wq: check IO_WQ_BIT_EXIT inside work run loop Currently this is checked before running the pending work. Normally this is quite fine, as work items either end up blocking (which will create a new worker for other items), or they complete fairly quickly. But syzbot reports an issue where io-wq takes seemingly forever to exit, and with a bit of debugging, this turns out to be because it queues a bunch of big (2GB - 4096b) reads with a /dev/msr* file. Since this file type doesn't support ->read_iter(), loop_rw_iter() ends up handling them. Each read returns 16MB of data read, which takes 20 (!!) seconds. With a bunch of these pending, processing the whole chain can take a long time. Easily longer than the syzbot uninterruptible sleep timeout of 140 seconds. This then triggers a complaint off the io-wq exit path: INFO: task syz.4.135:6326 blocked for more than 143 seconds. Not tainted syzkaller #0 Blocked by coredump. "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. task:syz.4.135 state:D stack:26824 pid:6326 tgid:6324 ppid:5957 task_flags:0x400548 flags:0x00080000 Call Trace: context_switch kernel/sched/core.c:5256 [inline] __schedule+0x1139/0x6150 kernel/sched/core.c:6863 __schedule_loop kernel/sched/core.c:6945 [inline] schedule+0xe7/0x3a0 kernel/sched/core.c:6960 schedule_timeout+0x257/0x290 kernel/time/sleep_timeout.c:75 do_wait_for_common kernel/sched/completion.c:100 [inline] __wait_for_common+0x2fc/0x4e0 kernel/sched/completion.c:121 io_wq_exit_workers io_uring/io-wq.c:1328 [inline] io_wq_put_and_exit+0x271/0x8a0 io_uring/io-wq.c:1356 io_uring_clean_tctx+0x10d/0x190 io_uring/tctx.c:203 io_uring_cancel_generic+0x69c/0x9a0 io_uring/cancel.c:651 io_uring_files_cancel include/linux/io_uring.h:19 [inline] do_exit+0x2ce/0x2bd0 kernel/exit.c:911 do_group_exit+0xd3/0x2a0 kernel/exit.c:1112 get_signal+0x2671/0x26d0 kernel/signal.c:3034 arch_do_signal_or_restart+0x8f/0x7e0 arch/x86/kernel/signal.c:337 __exit_to_user_mode_loop kernel/entry/common.c:41 [inline] exit_to_user_mode_loop+0x8c/0x540 kernel/entry/common.c:75 __exit_to_user_mode_prepare include/linux/irq-entry-common.h:226 [inline] syscall_exit_to_user_mode_prepare include/linux/irq-entry-common.h:256 [inline] syscall_exit_to_user_mode_work include/linux/entry-common.h:159 [inline] syscall_exit_to_user_mode include/linux/entry-common.h:194 [inline] do_syscall_64+0x4ee/0xf80 arch/x86/entry/syscall_64.c:100 entry_SYSCALL_64_after_hwframe+0x77/0x7f RIP: 0033:0x7fa02738f749 RSP: 002b:00007fa0281ae0e8 EFLAGS: 00000246 ORIG_RAX: 00000000000000ca RAX: fffffffffffffe00 RBX: 00007fa0275e6098 RCX: 00007fa02738f749 RDX: 0000000000000000 RSI: 0000000000000080 RDI: 00007fa0275e6098 RBP: 00007fa0275e6090 R08: 0000000000000000 R09: 0000000000000000 R10: 0000000000000000 R11: 0000000000000246 R12: 0000000000000000 R13: 00007fa0275e6128 R14: 00007fff14e4fcb0 R15: 00007fff14e4fd98 There's really nothing wrong here, outside of processing these reads will take a LONG time. However, we can speed up the exit by checking the IO_WQ_BIT_EXIT inside the io_worker_handle_work() loop, as syzbot will exit the ring after queueing up all of these reads. Then once the first item is processed, io-wq will simply cancel the rest. That should avoid syzbot running into this complaint again.

CVE Informations

Related Weaknesses

CWE-ID	Weakness Name	Source
CWE Other	No informations.

Metrics

Metrics	Score	Severity	CVSS Vector	Source
V3.1	5.5	MEDIUM	CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H More informations Base: Exploitabilty Metrics The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component. Attack Vector This metric reflects the context by which vulnerability exploitation is possible. Local The vulnerable component is not bound to the network stack and the attacker’s path is via read/write/execute capabilities. Attack Complexity This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability. Low Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success when attacking the vulnerable component. Privileges Required This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability. Low The attacker requires privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources. User Interaction This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable component. None The vulnerable system can be exploited without interaction from any user. Base: Scope Metrics The Scope metric captures whether a vulnerability in one vulnerable component impacts resources in components beyond its security scope. Scope Formally, a security authority is a mechanism (e.g., an application, an operating system, firmware, a sandbox environment) that defines and enforces access control in terms of how certain subjects/actors (e.g., human users, processes) can access certain restricted objects/resources (e.g., files, CPU, memory) in a controlled manner. All the subjects and objects under the jurisdiction of a single security authority are considered to be under one security scope. If a vulnerability in a vulnerable component can affect a component which is in a different security scope than the vulnerable component, a Scope change occurs. Intuitively, whenever the impact of a vulnerability breaches a security/trust boundary and impacts components outside the security scope in which vulnerable component resides, a Scope change occurs. Unchanged An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority. Base: Impact Metrics The Impact metrics capture the effects of a successfully exploited vulnerability on the component that suffers the worst outcome that is most directly and predictably associated with the attack. Analysts should constrain impacts to a reasonable, final outcome which they are confident an attacker is able to achieve. Confidentiality Impact This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability. None There is no loss of confidentiality within the impacted component. Integrity Impact This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. None There is no loss of integrity within the impacted component. Availability Impact This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability. High There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable). Temporal Metrics The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence in the description of a vulnerability. Environmental Metrics These metrics enable the analyst to customize the CVSS score depending on the importance of the affected IT asset to a user’s organization, measured in terms of Confidentiality, Integrity, and Availability.	nvd@nist.gov

EPSS

EPSS is a scoring model that predicts the likelihood of a vulnerability being exploited.

EPSS Score

The EPSS model produces a probability score between 0 and 1 (0 and 100%). The higher the score, the greater the probability that a vulnerability will be exploited.

EPSS Percentile

The percentile is used to rank CVE according to their EPSS score. For example, a CVE in the 95th percentile according to its EPSS score is more likely to be exploited than 95% of other CVE. Thus, the percentile is used to compare the EPSS score of a CVE with that of other CVE.

EPSS First.org