CVE-2024-53457
XSS in Librenms 24.9.0 – 24.10.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2024-53457 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Librenms Librenms. Its CVSS base score is 5.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 1% of CVEs by exploit likelihood; 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
LibreNMS versions 24.9.0 through 24.10.0 contain a stored cross-site scripting vulnerability in the Device Settings section. The flaw, tracked as CWE-79, permits injection of arbitrary web scripts or HTML through the Display Name parameter, carrying a CVSS 3.1 score of 5.4 that reflects network attack vector, low attack complexity, and required low privileges with user interaction.
An authenticated attacker can store a crafted payload in the Display Name field; when another user subsequently views the device settings page, the script executes within the victim's browser session, enabling limited theft of session data or unauthorized actions scoped to the application.
The two provided references point to a public proof-of-concept repository demonstrating the stored XSS vector but contain no official advisory statements or patch guidance. The associated EPSS score has remained flat at 0.4082 with no material increase after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-3436
Vulnerability Data
A stored cross-site scripting (XSS) vulnerability in the Device Settings section of LibreNMS v24.9.0 to v24.10.0 allows attackers to execute arbitrary web scripts or HTML via a crafted payload injected into the Display Name parameter.
- 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.