CVE-2022-0482 (GCVE-0-2022-0482)
Vulnerability from cvelistv5 – Published: 2022-03-09 10:20 – Updated: 2024-08-02 23:32Title
Exposure of Private Personal Information to an Unauthorized Actor in alextselegidis/easyappointments
Summary
Exposure of Private Personal Information to an Unauthorized Actor in GitHub repository alextselegidis/easyappointments prior to 1.4.3.
Severity
9.1 (Critical)
CWE
- CWE-359 - Exposure of Private Personal Information to an Unauthorized Actor
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://huntr.dev/bounties/2fe771ef-b615-45ef-9b4… | x_refsource_CONFIRM |
| https://github.com/alextselegidis/easyappointment… | x_refsource_MISC |
| http://packetstormsecurity.com/files/166701/Easy-… | x_refsource_MISC |
| https://opencirt.com/hacking/securing-easy-appoin… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| alextselegidis | alextselegidis/easyappointments |
Affected:
unspecified , < 1.4.3
(custom)
|
guessed |
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 922f9bd1-b5d0-4906-8683-d5d8c32f98f1
Exploited: Yes
Characteristics
Severity:
91.0
Timestamps
First Seen: 2023-10-19
Asserted: 2023-10-19
Last Seen: 2026-08-17
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: Easy!Appointments / Easy!Appointments | Class: other-software | Severity: Critical (CVSS 9.1) | IoT: no | In CISA KEV: no | Honeypot connections on 2026-08-17: 1
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 1 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | other-software |
| 7D Avg | 0 |
| Vendor | Easy!Appointments |
| 30D Avg | 0 |
| 90D Avg | 0 |
| Product | Easy!Appointments |
| Cisa Kev | no |
| Connections | 1 |
| Observation Date | 2026-08-17 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 9.1 |
| Vulnerability Severity | Critical |
References
Created: 2026-07-01 07:18 UTC
| Updated: 2026-08-19 01:01 UTC
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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