CVE-2021-47277 : Détail

CVE-2021-47277

7.1
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0.1%V4
Local
2024-05-21
14h20 +00:00
2024-12-19
07h38 +00:00
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Descriptions du CVE

kvm: avoid speculation-based attacks from out-of-range memslot accesses

In the Linux kernel, the following vulnerability has been resolved: kvm: avoid speculation-based attacks from out-of-range memslot accesses KVM's mechanism for accessing guest memory translates a guest physical address (gpa) to a host virtual address using the right-shifted gpa (also known as gfn) and a struct kvm_memory_slot. The translation is performed in __gfn_to_hva_memslot using the following formula: hva = slot->userspace_addr + (gfn - slot->base_gfn) * PAGE_SIZE It is expected that gfn falls within the boundaries of the guest's physical memory. However, a guest can access invalid physical addresses in such a way that the gfn is invalid. __gfn_to_hva_memslot is called from kvm_vcpu_gfn_to_hva_prot, which first retrieves a memslot through __gfn_to_memslot. While __gfn_to_memslot does check that the gfn falls within the boundaries of the guest's physical memory or not, a CPU can speculate the result of the check and continue execution speculatively using an illegal gfn. The speculation can result in calculating an out-of-bounds hva. If the resulting host virtual address is used to load another guest physical address, this is effectively a Spectre gadget consisting of two consecutive reads, the second of which is data dependent on the first. Right now it's not clear if there are any cases in which this is exploitable. One interesting case was reported by the original author of this patch, and involves visiting guest page tables on x86. Right now these are not vulnerable because the hva read goes through get_user(), which contains an LFENCE speculation barrier. However, there are patches in progress for x86 uaccess.h to mask kernel addresses instead of using LFENCE; once these land, a guest could use speculation to read from the VMM's ring 3 address space. Other architectures such as ARM already use the address masking method, and would be susceptible to this same kind of data-dependent access gadgets. Therefore, this patch proactively protects from these attacks by masking out-of-bounds gfns in __gfn_to_hva_memslot, which blocks speculation of invalid hvas. Sean Christopherson noted that this patch does not cover kvm_read_guest_offset_cached. This however is limited to a few bytes past the end of the cache, and therefore it is unlikely to be useful in the context of building a chain of data dependent accesses.

Informations du CVE

Faiblesses connexes

CWE-ID Nom de la faiblesse Source
CWE-125 Out-of-bounds Read
The product reads data past the end, or before the beginning, of the intended buffer.

Métriques

Métriques Score Gravité CVSS Vecteur Source
V3.1 7.1 HIGH CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/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.

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 est un modèle de notation qui prédit la probabilité qu'une vulnérabilité soit exploitée.

Score EPSS

Le modèle EPSS produit un score de probabilité compris entre 0 et 1 (0 et 100 %). Plus la note est élevée, plus la probabilité qu'une vulnérabilité soit exploitée est grande.

Percentile EPSS

Le percentile est utilisé pour classer les CVE en fonction de leur score EPSS. Par exemple, une CVE dans le 95e percentile selon son score EPSS est plus susceptible d'être exploitée que 95 % des autres CVE. Ainsi, le percentile sert à comparer le score EPSS d'une CVE par rapport à d'autres CVE.
EPSS First.org

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version To (excluding) 4.4.273

Linux>>Linux_kernel >> Version From (including) 4.5 To (excluding) 4.9.273

Linux>>Linux_kernel >> Version From (including) 4.10 To (excluding) 4.14.237

Linux>>Linux_kernel >> Version From (including) 4.15 To (excluding) 4.19.195

Linux>>Linux_kernel >> Version From (including) 4.20 To (excluding) 5.4.126

Linux>>Linux_kernel >> Version From (including) 5.5 To (excluding) 5.10.44

Linux>>Linux_kernel >> Version From (including) 5.11 To (excluding) 5.12.11

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Références

Les informations affichées sur CVE Find proviennent de plusieurs sources de référence rigoureusement sélectionnées. Les données CVE sont fournies par MITRE Corporation et la National Vulnerability Database (NVD). Le catalogue des vulnérabilités activement exploitées (KEV) provient de la Cybersecurity and Infrastructure Security Agency (CISA), tandis que les scores EPSS sont issus de FIRST.org. Enfin, les données relatives aux faiblesses logicielles (CWE) et aux schémas d'attaque courants (CAPEC) sont maintenues par MITRE Corporation, et les informations sur les configurations logicielles et matérielles (CPE) proviennent du NVD.