CVE-2025-26620 : 상세 정보

CVE-2025-26620

6.3
/
보통
0.12%V4
2025-02-18
17h36 +00:00
Network
2025-02-18
19h05 +00:00
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CVE 설명

Duende.AccessTokenManagement race condition when concurrently retrieving customized Client Credentials Access Tokens

Duende.AccessTokenManagement is a set of .NET libraries that manage OAuth and OpenId Connect access tokens. Duende.AccessTokenManagement contains a race condition when requesting access tokens using the client credentials flow. Concurrent requests to obtain an access token using differing protocol parameters can return access tokens obtained with the wrong scope, resource indicator, or other protocol parameters. Such usage is somewhat atypical, and only a small percentage of users are likely to be affected. Duende.AccessTokenManagement can request access tokens using the client credentials flow in several ways. In basic usage, the client credentials flow is configured once and the parameters do not vary. In more advanced situations, requests with varying protocol parameters may be made by calling specific overloads of these methods: `HttpContext.GetClientAccessTokenAsync()` and `IClientCredentialsTokenManagementService.GetAccessTokenAsync()`. There are overloads of both of these methods that accept a `TokenRequestParameters` object that customizes token request parameters. However, concurrent requests with varying `TokenRequestParameters` will result in the same token for all concurrent calls. Most users can simply update the NuGet package to the latest version. Customizations of the `IClientCredentialsTokenCache` that derive from the default implementation (`DistributedClientCredentialsTokenCache`) will require a small code change, as its constructor was changed to add a dependency on the `ITokenRequestSynchronization` service. The synchronization service will need to be injected into the derived class and passed to the base constructor. The impact of this vulnerability depends on how Duende.AccessTokenManagement is used and on the security architecture of the solution. Most users will not be vulnerable to this issue. More advanced users may run into this issue by calling the methods specified above with customized token request parameters. The impact of obtaining an access token with different than intended protocol parameters will vary depending on application logic, security architecture, and the authorization policy of the resource servers.

CVE 정보

관련 약점

CWE-ID 약점 이름 출처
CWE-367 Time-of-check Time-of-use (TOCTOU) Race Condition
The product checks the state of a resource before using that resource, but the resource's state can change between the check and the use in a way that invalidates the results of the check.

메트릭

메트릭 점수 심각도 CVSS 벡터 출처
V4.0 6.3 MEDIUM CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:L/VA:N/SC:L/SI:L/SA:N

Base: Exploitabilty Metrics

The Exploitability metrics reflect the characteristics of the “thing that is vulnerable”, which we refer to formally as the vulnerable system.

Attack Vector

This metric reflects the context by which vulnerability exploitation is possible.

Network

The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, up to and including the entire Internet. Such a vulnerability is often termed “remotely exploitable” and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).

Attack Complexity

This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit.

Low

The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.

Attack Requirements

This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack.

Present

The successful attack depends on the presence of specific deployment and execution conditions of the vulnerable system that enable the attack. These include: A race condition must be won to successfully exploit the vulnerability. The successfulness of the attack is conditioned on execution conditions that are not under full control of the attacker. The attack may need to be launched multiple times against a single target before being successful. Network injection. The attacker must inject themselves into the logical network path between the target and the resource requested by the victim (e.g. vulnerabilities requiring an on-path attacker).

Privileges Required

This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability.

None

The attacker is unauthenticated prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.

User Interaction

This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system.

None

The vulnerable system can be exploited without interaction from any human user, other than the attacker. Examples include: a remote attacker is able to send packets to a target system a locally authenticated attacker executes code to elevate privileges

Base: Impact Metrics

The Impact metrics capture the effects of a successfully exploited vulnerability. 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 managed by the system due to a successfully exploited vulnerability.

Low

There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the Vulnerable System.

Integrity Impact

This metric measures the impact to integrity of a successfully exploited vulnerability.

Low

Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact to the Vulnerable System.

Availability Impact

This metric measures the impact to the availability of the impacted system resulting from a successfully exploited vulnerability.

None

There is no impact to availability within the Vulnerable System.

Sub Confidentiality Impact

Low

There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the Subsequent System.

Sub Integrity Impact

Low

Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact to the Subsequent System.

Sub Availability Impact

None

There is no impact to availability within the Subsequent System or all availability impact is constrained to the Vulnerable System.

Threat Metrics

The Threat metrics measure the current state of exploit techniques or code availability for a vulnerability.

Environmental Metrics

These metrics enable the consumer analyst to customize the resulting score depending on the importance of the affected IT asset to a user’s organization, measured in terms of complementary/alternative security controls in place, Confidentiality, Integrity, and Availability. The metrics are the modified equivalent of Base metrics and are assigned values based on the system placement within organizational infrastructure.

Supplemental Metrics

Supplemental metric group provides new metrics that describe and measure additional extrinsic attributes of a vulnerability. While the assessment of Supplemental metrics is provisioned by the provider, the usage and response plan of each metric within the Supplemental metric group is determined by the consumer.

EPSS

EPSS는 취약점이 악용될 가능성을 예측하는 점수 모델입니다.

EPSS 점수

EPSS 모델은 0과 1(0~100%) 사이의 확률 점수를 생성합니다. 점수가 높을수록 취약점이 악용될 가능성이 높습니다.

EPSS 백분위수

백분위수는 EPSS 점수에 따라 CVE의 순위를 매기는 데 사용됩니다. 예를 들어 EPSS 점수 기준 95번째 백분위수의 CVE는 다른 CVE의 95%보다 악용될 가능성이 높습니다.

참고 자료