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Data Sharing Framework has an Inverted Time Comparison in OIDC JWKS and Token Cache

Moderate severity GitHub Reviewed Published Apr 14, 2026 in datasharingframework/dsf • Updated Apr 15, 2026

Package

maven dev.dsf:dsf-bpe-process-api-v2 (Maven)

Affected versions

< 2.1.0

Patched versions

None
maven dev.dsf:dsf-bpe-server (Maven)
< 2.1.0
None

Description

Affected Components

Summary

  • The OIDC JWKS and Metadata Document caches used an inverted time comparison (isBefore instead of isAfter), causing the cache to never return cached values. Every incoming request triggered a fresh HTTP fetch of the OIDC Metadata Document and JWKS keys from the OIDC provider.
  • The OIDC token cache for the FHIR client connections used an inverted time comparison (isBefore instead of isAfter), causing the cache to never invalidate. Every incoming request returned the same OIDC token even if expired.

Impact

  • Performance: Every OIDC-authenticated request added network round-trips to the OIDC provider, increasing latency
  • Reliability: Cached OIDC tokens become unusable after expiration and can only be invalidated by restart of the BPE.
    If the OIDC provider is temporarily unreachable, all requests fail immediately instead of using cached keys
  • Load: Unnecessary load on the OIDC provider, potentially causing rate limiting

Fix (commits 31c2e974d, d3ca59b4d)

  • Fixed cache timeout comparison from isBefore to isAfter in BaseOidcClientWithCache (configuration and JWKS caches) and OidcClientWithCache (configuration, JWKS, and access token caches)
  • Added configurable cache timeouts via dev.dsf.server.auth.oidc.provider.client.cache.timeout.configuration.resource and dev.dsf.server.auth.oidc.provider.client.cache.timeout.jwks.resource (default: PT1H)

References

@hhund hhund published to datasharingframework/dsf Apr 14, 2026
Published to the GitHub Advisory Database Apr 15, 2026
Reviewed Apr 15, 2026
Last updated Apr 15, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements Present
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability Low
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
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. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the 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. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Always-Incorrect Control Flow Implementation

The code contains a control flow path that does not reflect the algorithm that the path is intended to implement, leading to incorrect behavior any time this path is navigated. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-xmj9-7625-f634

Source code

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