CVE-2026-53983 (GCVE-0-2026-53983)
Vulnerability from cvelistv5 – Published: 2026-08-06 15:38 – Updated: 2026-08-07 15:18
VLAI
EPSS
VEX
Title
Ground Station prior to 0.6.0 Unauthenticated Persistent Blind Server-Side Request Forgery via Orbital Data Source URL
Summary
Ground Station prior to 0.6.0 contains an unauthenticated blind server-side request forgery vulnerability in the orbital-source configuration path that allows any unauthenticated Socket.IO client to cause the ground-station process to issue outbound HTTP requests to attacker-chosen destinations. Attackers can connect to the Socket.IO server on port 7000 without credentials due to disabled authentication enforcement and a wildcard CORS policy, then submit a data_submission event with submit-orbital-sources action to persist an attacker-supplied URL in the database, then trigger an orbital sync via the equally unauthenticated background_task:start event. The URL is stored with no scheme allowlist, no host validation, and no rejection of loopback, RFC1918, or link-local (cloud instance metadata at 169.254.169.254) addresses, and is passed directly to requests.get in _fetch_http_3le and _fetch_http_omm in backend/tlesync/source_adapters.py. HTTP status codes and error messages from the outbound request are emitted in the orbital_sync_state Socket.IO event to all connected clients, providing a serviceable oracle for interpreting internal-service and cloud-metadata responses even though the raw response body is not directly leaked. Because the malicious source persists in the database across restarts and re-fires every 24 hours on the scheduled sync cycle, the primitive gives durable long-term SSRF without the attacker needing to remain connected.
Severity
8.6 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-07 15:18 UTC
CWE
- CWE-918 - Server-Side request forgery (SSRF)
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/sgoudelis/ground-station | product |
| https://github.com/sgoudelis/ground-station/secur… | vendor-advisory |
| https://github.com/sgoudelis/ground-station/commi… | patch |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Efstratios Goudelis | Ground Station |
Affected:
0 , < 0.6.0
(semver)
|
guessed |
Date Public
2026-08-06 15:30
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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.
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- 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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vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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