Cyber Resilience

CVE-2022-21696

Medium

Published: 18 January 2022

Published
18 January 2022
Modified
21 November 2024
KEV Added
Patch
CVSS Score v3.1 4.3 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
EPSS Score 0.0021 43.4th percentile
Risk Priority 9 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2022-21696 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Onionshare Onionshare. Its CVSS base score is 4.3 (Medium).

Operationally, ranked at the 43.4th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

EU & UK References

Vulnerability details

OnionShare is an open source tool that lets you securely and anonymously share files, host websites, and chat with friends using the Tor network. In affected versions it is possible to change the username to that of another chat participant…

more

with an additional space character at the end of the name string. An adversary with access to the chat environment can use the rename feature to impersonate other participants by adding whitespace characters at the end of the username.

CWE(s)

Related Threats

No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.

Affected Assets

onionshare
onionshare
≤ 2.5

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.

addresses: CWE-20

Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.

addresses: CWE-20

Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.

addresses: CWE-20

Directly implements checks on information inputs to reject invalid data before processing.

addresses: CWE-20

Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.

References