CVE-2022-1815
Published: 25 May 2022
Summary
CVE-2022-1815 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Diagrams Drawio. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 3.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
Deeper analysis
CVE-2022-1815 is an exposure of sensitive information vulnerability, also categorized under CWE-918 as server-side request forgery, affecting the jgraph/drawio repository (diagrams.net) in versions prior to 18.1.2. The flaw received a CVSS 3.1 score of 7.5, reflecting network-accessible attack vectors with low complexity and no required privileges or user interaction, resulting in high confidentiality impact.
An unauthenticated remote attacker can exploit the issue to force the application into disclosing sensitive internal data or resources that should remain inaccessible. The absence of proper validation on outbound requests enables the attacker to achieve information leakage without any prior authentication or user assistance.
The referenced GitHub commit c287bef9101d024b1fd59d55ecd530f25000f9d8 and associated huntr.dev report document the remediation that was incorporated into release 18.1.2. The EPSS score has remained flat at its peak value of 0.2487 with no material upward trajectory after disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-25091
Vulnerability details
Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository jgraph/drawio prior to 18.1.2.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing attempts to access or extract sensitive data, revealing exposure of sensitive information to unauthorized actors.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.