Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2025-68147 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Opensourcepos Open Source Point Of Sale. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 25th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-204017
Vulnerability Data
Open Source Point of Sale (opensourcepos) is a web based point of sale application written in PHP using CodeIgniter framework. Starting in version 3.4.0 and prior to version 3.4.2, a Stored Cross-Site Scripting (XSS) vulnerability exists in the "Return Policy"…
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configuration field. The application does not properly sanitize user input before saving it to the database or displaying it on receipts. An attacker with access to the "Store Configuration" (such as a rogue administrator or an account compromised via the separate CSRF vulnerability) can inject malicious JavaScript payloads into this field. These payloads are executed in the browser of any user (including other administrators and sales staff) whenever they view a receipt or complete a transaction. This can lead to session hijacking, theft of sensitive data, or unauthorized actions performed on behalf of the victim. The vulnerability has been patched in version 3.4.2 by ensuring the output is escaped using the `esc()` function in the receipt template. As a temporary mitigation, administrators should ensure the "Return Policy" field contains only plain text and strictly avoid entering any HTML tags. There is no code-based workaround other than applying the patch.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.