CVE-2026-50576 (GCVE-0-2026-50576)
Vulnerability from cvelistv5 – Published: 2026-08-18 16:49 – Updated: 2026-08-18 19:07
VLAI
EPSS
VEX
Title
ePA 3.x Integration: HTTP Header Injection in VAU Inner Requests
Summary
ePA 3.x Integration implements the authorization workflow and writes Medical Information Objects to Germany's electronic patient record. Prior to 1.3.0, ePA 3.x Integration does not neutralize CRLF characters in values used by app/vau/VAUProtokoll.py to construct VAU inner HTTP requests. The build_inner_header function interpolates the uri, host, accept_type, content_type, content_length, USER_AGENT, and insurant_id values into request lines and headers, including x-useragent and x-insurantid. An authenticated attacker who controls a value can inject additional headers into the inner request. Depending on ePA server handling, an injected x-insurantid header can expose another patient's records, and injected Authorization headers can bypass the intended authentication or authorization context. Session-derived USER_AGENT input can also poison requests across the session. This issue is fixed in version 1.3.0.
Severity
6.8 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-18 19:07 UTC
CWE
- CWE-113 - Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/fbeta-GmbH/ePA3-Service-OpenSo… | x_refsource_CONFIRM |
| https://github.com/fbeta-GmbH/ePA3-Service-OpenSo… | x_refsource_MISC |
| https://github.com/fbeta-GmbH/ePA3-Service-OpenSo… | x_refsource_MISC |
| https://github.com/fbeta-GmbH/ePA3-Service-OpenSo… | x_refsource_MISC |
| https://www.machinespirits.de/advisory/013a60 | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| fbeta-GmbH | ePA3-Service-OpenSource |
Affected:
< 1.3.0
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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