CPE, which stands for Common Platform Enumeration, is a standardized scheme for naming hardware, software, and operating systems. CPE provides a structured naming scheme to uniquely identify and classify information technology systems, platforms, and packages based on certain attributes such as vendor, product name, version, update, edition, and language.
CWE, or Common Weakness Enumeration, is a comprehensive list and categorization of software weaknesses and vulnerabilities. It serves as a common language for describing software security weaknesses in architecture, design, code, or implementation that can lead to vulnerabilities.
CAPEC, which stands for Common Attack Pattern Enumeration and Classification, is a comprehensive, publicly available resource that documents common patterns of attack employed by adversaries in cyber attacks. This knowledge base aims to understand and articulate common vulnerabilities and the methods attackers use to exploit them.
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The Windows kernel in Microsoft Windows Server 2008 SP2 and R2 SP1, Windows 7 SP1, Windows 8.1 and RT 8.1, Windows Server 2012 and R2, Windows 10 Gold, 1511, 1607, 1703, and 1709, Windows Server 2016 and Windows Server, version 1709 allows an information disclosure vulnerability due to the way memory addresses are handled, aka "Windows Kernel Information Disclosure Vulnerability". This CVE is unique from CVE-2018-0811, CVE-2018-0813, CVE-2018-0814, CVE-2018-0894, CVE-2018-0895, CVE-2018-0896, CVE-2018-0897, CVE-2018-0898, CVE-2018-0899, CVE-2018-0900, and CVE-2018-0926.
Missing Release of Memory after Effective Lifetime The product does not sufficiently track and release allocated memory after it has been used, making the memory unavailable for reallocation and reuse.
Improper Initialization The product does not initialize or incorrectly initializes a resource, which might leave the resource in an unexpected state when it is accessed or used.
Metrics
Metrics
Score
Severity
CVSS Vector
Source
V3.0
4.7
MEDIUM
CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
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
A vulnerability exploitable with Local access means that the vulnerable component is not bound to the network stack, and the attacker's path is via read/write/execute capabilities. In some cases, the attacker may be logged in locally in order to exploit the vulnerability, otherwise, she may rely on User Interaction to execute a malicious file.
Attack Complexity
This metric describes the conditions beyond the attacker's control that must exist in order to exploit the vulnerability.
High
A successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected.
Privileges Required
This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.
Low
The attacker is authorized with (i.e. 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 may have the ability to cause an impact only to non-sensitive resources.
User Interaction
This metric captures the requirement for a 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
An important property captured by CVSS v3.0 is the ability for a vulnerability in one software component to impact resources beyond its means, or privileges.
Scope
Formally, Scope refers to the collection of privileges defined by a computing authority (e.g. an application, an operating system, or a sandbox environment) when granting access to computing resources (e.g. files, CPU, memory, etc). These privileges are assigned based on some method of identification and authorization. In some cases, the authorization may be simple or loosely controlled based upon predefined rules or standards. For example, in the case of Ethernet traffic sent to a network switch, the switch accepts traffic that arrives on its ports and is an authority that controls the traffic flow to other switch ports.
Unchanged
An exploited vulnerability can only affect resources managed by the same authority. In this case the vulnerable component and the impacted component are the same.
Base: Impact Metrics
The Impact metrics refer to the properties of the impacted component.
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 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.
None
There is no impact to availability within the impacted component.
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 that one has in the description of a vulnerability.
Environmental Metrics
nvd@nist.gov
V2
1.9
AV:L/AC:M/Au:N/C:P/I:N/A:N
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.
Date
EPSS V0
EPSS V1
EPSS V2 (> 2022-02-04)
EPSS V3 (> 2025-03-07)
EPSS V4 (> 2025-03-17)
2021-04-18
35.03%
–
–
–
–
2021-09-05
–
35.03%
–
–
–
2022-01-09
–
35.03%
–
–
–
2022-02-06
–
–
2.19%
–
–
2022-03-13
–
–
2.19%
–
–
2022-04-03
–
–
2.19%
–
–
2022-06-19
–
–
2.19%
–
–
2022-12-25
–
–
2.19%
–
–
2023-01-01
–
–
2.19%
–
–
2023-02-26
–
–
2.19%
–
–
2023-03-12
–
–
–
0.09%
–
2023-11-05
–
–
–
0.09%
–
2023-11-12
–
–
–
0.09%
–
2023-11-19
–
–
–
0.09%
–
2023-12-03
–
–
–
0.09%
–
2024-01-28
–
–
–
0.09%
–
2024-02-11
–
–
–
0.09%
–
2024-02-25
–
–
–
0.11%
–
2024-03-31
–
–
–
0.11%
–
2024-06-02
–
–
–
0.11%
–
2024-06-16
–
–
–
0.11%
–
2024-08-04
–
–
–
0.12%
–
2024-09-08
–
–
–
0.09%
–
2024-10-20
–
–
–
0.1%
–
2024-11-24
–
–
–
0.12%
–
2024-12-22
–
–
–
25.97%
–
2025-01-05
–
–
–
23.28%
–
2025-02-16
–
–
–
27.36%
–
2025-01-19
–
–
–
23.28%
–
2025-02-16
–
–
–
27.36%
–
2025-03-18
–
–
–
–
6.52%
2025-04-15
–
–
–
–
6.52%
2025-04-15
–
–
–
–
6.52,%
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.