CVE-2022-49888 : Detail

CVE-2022-49888

7.8
/
High
Overflow
0.02%V4
Local
2025-05-01
14h10 +00:00
2025-05-04
08h47 +00:00
CVE.org
NVD
Notifications for a CVE
Stay informed of any changes for a specific CVE.
Notifications manage

CVE Descriptions

arm64: entry: avoid kprobe recursion

In the Linux kernel, the following vulnerability has been resolved: arm64: entry: avoid kprobe recursion The cortex_a76_erratum_1463225_debug_handler() function is called when handling debug exceptions (and synchronous exceptions from BRK instructions), and so is called when a probed function executes. If the compiler does not inline cortex_a76_erratum_1463225_debug_handler(), it can be probed. If cortex_a76_erratum_1463225_debug_handler() is probed, any debug exception or software breakpoint exception will result in recursive exceptions leading to a stack overflow. This can be triggered with the ftrace multiple_probes selftest, and as per the example splat below. This is a regression caused by commit: 6459b8469753e9fe ("arm64: entry: consolidate Cortex-A76 erratum 1463225 workaround") ... which removed the NOKPROBE_SYMBOL() annotation associated with the function. My intent was that cortex_a76_erratum_1463225_debug_handler() would be inlined into its caller, el1_dbg(), which is marked noinstr and cannot be probed. Mark cortex_a76_erratum_1463225_debug_handler() as __always_inline to ensure this. Example splat prior to this patch (with recursive entries elided): | # echo p cortex_a76_erratum_1463225_debug_handler > /sys/kernel/debug/tracing/kprobe_events | # echo p do_el0_svc >> /sys/kernel/debug/tracing/kprobe_events | # echo 1 > /sys/kernel/debug/tracing/events/kprobes/enable | Insufficient stack space to handle exception! | ESR: 0x0000000096000047 -- DABT (current EL) | FAR: 0xffff800009cefff0 | Task stack: [0xffff800009cf0000..0xffff800009cf4000] | IRQ stack: [0xffff800008000000..0xffff800008004000] | Overflow stack: [0xffff00007fbc00f0..0xffff00007fbc10f0] | CPU: 0 PID: 145 Comm: sh Not tainted 6.0.0 #2 | Hardware name: linux,dummy-virt (DT) | pstate: 604003c5 (nZCv DAIF +PAN -UAO -TCO -DIT -SSBS BTYPE=--) | pc : arm64_enter_el1_dbg+0x4/0x20 | lr : el1_dbg+0x24/0x5c | sp : ffff800009cf0000 | x29: ffff800009cf0000 x28: ffff000002c74740 x27: 0000000000000000 | x26: 0000000000000000 x25: 0000000000000000 x24: 0000000000000000 | x23: 00000000604003c5 x22: ffff80000801745c x21: 0000aaaac95ac068 | x20: 00000000f2000004 x19: ffff800009cf0040 x18: 0000000000000000 | x17: 0000000000000000 x16: 0000000000000000 x15: 0000000000000000 | x14: 0000000000000000 x13: 0000000000000000 x12: 0000000000000000 | x11: 0000000000000010 x10: ffff800008c87190 x9 : ffff800008ca00d0 | x8 : 000000000000003c x7 : 0000000000000000 x6 : 0000000000000000 | x5 : 0000000000000000 x4 : 0000000000000000 x3 : 00000000000043a4 | x2 : 00000000f2000004 x1 : 00000000f2000004 x0 : ffff800009cf0040 | Kernel panic - not syncing: kernel stack overflow | CPU: 0 PID: 145 Comm: sh Not tainted 6.0.0 #2 | Hardware name: linux,dummy-virt (DT) | Call trace: | dump_backtrace+0xe4/0x104 | show_stack+0x18/0x4c | dump_stack_lvl+0x64/0x7c | dump_stack+0x18/0x38 | panic+0x14c/0x338 | test_taint+0x0/0x2c | panic_bad_stack+0x104/0x118 | handle_bad_stack+0x34/0x48 | __bad_stack+0x78/0x7c | arm64_enter_el1_dbg+0x4/0x20 | el1h_64_sync_handler+0x40/0x98 | el1h_64_sync+0x64/0x68 | cortex_a76_erratum_1463225_debug_handler+0x0/0x34 ... | el1h_64_sync_handler+0x40/0x98 | el1h_64_sync+0x64/0x68 | cortex_a76_erratum_1463225_debug_handler+0x0/0x34 ... | el1h_64_sync_handler+0x40/0x98 | el1h_64_sync+0x64/0x68 | cortex_a76_erratum_1463225_debug_handler+0x0/0x34 | el1h_64_sync_handler+0x40/0x98 | el1h_64_sync+0x64/0x68 | do_el0_svc+0x0/0x28 | el0t_64_sync_handler+0x84/0xf0 | el0t_64_sync+0x18c/0x190 | Kernel Offset: disabled | CPU features: 0x0080,00005021,19001080 | Memory Limit: none | ---[ end Kernel panic - not syncing: kernel stack overflow ]--- With this patch, cortex_a76_erratum_1463225_debug_handler() is inlined into el1_dbg(), and el1_dbg() cannot be probed: | # echo p cortex_a76_erratum_1463225_debug_handler > /sys/kernel/debug/tracing/kprobe_events | sh: write error: No such file or directory | # grep -w cortex_a76_errat ---truncated---

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE-787 Out-of-bounds Write
The product writes data past the end, or before the beginning, of the intended buffer.

Metrics

Metrics Score Severity CVSS Vector Source
V3.1 7.8 HIGH CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

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.

High

There is a total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.

Integrity Impact

This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information.

High

There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to 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

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version From (including) 5.12 To (excluding) 5.15.78

Linux>>Linux_kernel >> Version From (including) 5.16 To (excluding) 6.0.8

Linux>>Linux_kernel >> Version 6.1

Linux>>Linux_kernel >> Version 6.1

Linux>>Linux_kernel >> Version 6.1

References

The information presented on CVE Find originates from several carefully selected reference sources. CVE data is provided by MITRE Corporation and the National Vulnerability Database (NVD). The Known Exploited Vulnerabilities (KEV) catalog is sourced from the Cybersecurity and Infrastructure Security Agency (CISA), while EPSS scores come from FIRST.org. Additionally, data regarding software weaknesses (CWE) and common attack patterns (CAPEC) is maintained by MITRE Corporation, and information on hardware and software configurations (CPE) is provided by the NVD.