Evidence used
- No CISA KEV confirmation is currently recorded.
- A structured source references public exploit or proof-of-concept material.
- EPSS is 1.67% for the current model date.
BlackTreeCVE IntelligenceRadarCOVID · radar-covid-backend-dp3t-server
Official source article: GitHub GHSA-W7JX-37X3-W2JX ↗. Check the applicable product and release in the original source.
High technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.
High technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.
Patch availableRadar COVID is the official COVID-19 exposure notification app for Spain. In affected versions of Radar COVID, identification and de-anonymization of COVID-19 positive users that upload Radar COVID TEKs to the Radar COVID server is possible. This vulnerability enables the identification and de-anonymization of COVID-19 positive users when using Radar COVID. The vulnerability is caused by the fact that Radar COVID connections to the server (uploading of TEKs to the backend) are only made by COVID-19 positives. Therefore, any on-path observer with the ability to monitor traffic between the app and the server can identify which users had a positive test. Such an adversary can be the mobile network operator (MNO) if the connection is done through a mobile network, the Internet Service Provider (ISP) if the connection is done through the Internet (e.g., a home network), a VPN provider used by the user, the local network operator in the case of enterprise networks, or any eavesdropper with access to the same network (WiFi or Ethernet) as the user as could be the case of public WiFi hotspots deployed at shopping centers, airports, hotels, and coffee shops. The attacker may also de-anonymize the user. For this additional stage to succeed, the adversary needs to correlate Radar COVID traffic to other identifiable information from the victim. This could be achieved by associating the connection to a contract with the name of the victim or by associating Radar COVID traffic to other user-generated flows containing identifiers in the clear (e.g., HTTP cookies or other mobile flows sending unique identifiers like the IMEI or the AAID without encryption). The former can be executed, for instance, by the Internet Service Provider or the MNO. The latter can be executed by any on-path adversary, such as the network provider or even the cloud provider that hosts more than one service accessed by the victim. The farther the adversary is either from the victim (the client) or the end-point (the server), the less likely it may be that the adversary has access to re-identification information. The vulnerability has been mitigated with the injection of dummy traffic from the application to the backend. Dummy traffic is generated by all users independently of whether they are COVID-19 positive or not. The issue was fixed in iOS in version 1.0.8 (uniform distribution), 1.1.0 (exponential distribution), Android in version 1.0.7 (uniform distribution), 1.1.0 (exponential distribution), Backend in version 1.1.2-RELEASE. For more information see the referenced GitHub Security Advisory.
Radar COVID is the official COVID-19 exposure notification app for Spain. In affected versions of Radar COVID, identification and de-anonymization of COVID-19 positive users that upload Radar COVID TEKs to the Radar COVID server is possible. This vulnerability enables the identification and de-anonymization of COVID-19 positive users when using Radar COVID. The vulnerability is caused by the fact that Radar COVID connections to the server (uploading of TEKs to the backend) are only made by COVID-19 positives. Therefore, any on-path observer with the ability to monitor traffic between the app and the server can identify which users had a positive test. Such an adversary can be the mobile network operator (MNO) if the connection is done through a mobile network, the Internet Service Provider (ISP) if the connection is done through the Internet (e.g., a home network), a VPN provider used by the user, the local network operator in the case of enterprise networks, or any eavesdropper with access to the same network (WiFi or Ethernet) as the user as could be the case of public WiFi hotspots deployed at shopping centers, airports, hotels, and coffee shops. The attacker may also de-anonymize the user. For this additional stage to succeed, the adversary needs to correlate Radar COVID traffic to other identifiable information from the victim. This could be achieved by associating the connection to a contract with the name of the victim or by associating Radar COVID traffic to other user-generated flows containing identifiers in the clear (e.g., HTTP cookies or other mobile flows sending unique identifiers like the IMEI or the AAID without encryption). The former can be executed, for instance, by the Internet Service Provider or the MNO. The latter can be executed by any on-path adversary, such as the network provider or even the cloud provider that hosts more than one service accessed by the victim. The farther the adversary is either from the victim (the client) or the end-point (the server), the less likely it may be that the adversary has access to re-identification information. The vulnerability has been mitigated with the injection of dummy traffic from the application to the backend. Dummy traffic is generated by all users independently of whether they are COVID-19 positive or not. The issue was fixed in iOS in version 1.0.8 (uniform distribution), 1.1.0 (exponential distribution), Android in version 1.0.7 (uniform distribution), 1.1.0 (exponential distribution), Backend in version 1.1.2-RELEASE. For more information see the referenced GitHub Security Advisory.
The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.
An attacker operating through an adjacent network may attempt exploitation without authentication or user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
Radar COVID is the official COVID-19 exposure notification app for Spain. In affected versions of Radar COVID, identification and de-anonymization of COVID-19 positive users that upload Radar COVID TEKs to the Radar COVID server is possible. This vulnerability enables the identification and de-anonymization of COVID-19 positive users when using Radar COVID. The vulnerability is caused by the fact that Radar COVID connections to the server (uploading of TEKs to the backend) are only made by COVID-19 positives. Therefore, any on-path observer with the ability to monitor traffic between the app and the server can identify which users had a positive test. Such an adversary can be the mobile network operator (MNO) if the connection is done through a mobile network, the Internet Service Provider (ISP) if the connection is done through the Internet (e.g., a home network), a VPN provider used by the user, the local network operator in the case of enterprise networks, or any eavesdropper with access to the same network (WiFi or Ethernet) as the user as could be the case of public WiFi hotspots deployed at shopping centers, airports, hotels, and coffee shops. The attacker may also de-anonymize the user. For this additional stage to succeed, the adversary needs to correlate Radar COVID traffic to other identifiable information from the victim. This could be achieved by associating the connection to a contract with the name of the victim or by associating Radar COVID traffic to other user-generated flows containing identifiers in the clear (e.g., HTTP cookies or other mobile flows sending unique identifiers like the IMEI or the AAID without encryption). The former can be executed, for instance, by the Internet Service Provider or the MNO. The latter can be executed by any on-path adversary, such as the network provider or even the cloud provider that hosts more than one service accessed by the victim. The farther the adversary is either from the victim (the client) or the end-point (the server), the less likely it may be that the adversary has access to re-identification information. The vulnerability has been mitigated with the injection of dummy traffic from the application to the backend. Dummy traffic is generated by all users independently of whether they are COVID-19 positive or not. The issue was fixed in iOS in version 1.0.8 (uniform distribution), 1.1.0 (exponential distribution), Android in version 1.0.7 (uniform distribution), 1.1.0 (exponential distribution), Backend in version 1.1.2-RELEASE. For more information see the referenced GitHub Security Advisory.
The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.
An attacker operating through an adjacent network may attempt exploitation without authentication or user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
A structured CVE source labels at least one public reference as exploit material. BlackTree has not independently validated that it is safe, reliable or weaponised.
CWE-200: Exposure of Sensitive Information to an Unauthorized Actor. The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.
CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:NCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
Operational remediation based on structured source evidence.
Published 13 Nov 2020 · Last source change 4 Aug 2024, 15:56 UTC · CWE-200 · Exposure of Sensitive Information to an Unauthorized Actor
Core structured fields are present and their contributing authorities are shown above.
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.