CVE-2025-70095: A Cross-site Scripting (XSS)
Source CVE Database V5
Not known to be exploited · CVSS 6.5 Medium.
Data as of 26 Oct 2026, 14:05 UTC
- EPSS
- 0.1% chance of exploitation in 30 days – higher than 8% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:LExploitation evidence
Disclosed: evidence found.
CVE published 13 Feb 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
AI-suggested
European organizations should implement strict input validation and output encoding in the affected OpenSourcePOS modules to neutralize malicious payloads. Until official patches are released, deploying web application firewalls (WAFs) with custom rules to detect and block XSS payloads targeting item management and sales invoice endpoints is recommended. Restrict user privileges to the minimum necessary, reducing the risk posed by low-privilege attackers. Conduct user training to raise awareness about phishing and social engineering tactics that could trigger user interaction with malicious inputs. Regularly monitor logs for suspicious activities related to these functions. Consider isolating POS systems from broader corporate networks to limit lateral movement in case of compromise. Engage with the OpenSourcePOS community or vendors for timely updates and patches.
Description
A cross-site scripting (XSS) vulnerability in the item management and sales invoice function of OpenSourcePOS v3.4.1 allows attackers to execute arbitrary web scripts or HTML via injecting a crafted payload.