Cyber Resilience

CVE-2024-47528

XSS in Librenms ≤ 24.9.0

Public PoCXSS
Published
01 October 2024
Modified
19 December 2024
Patch / advisory
CVSS Score v4 4.6
Click a component to see what it means
Raw vectorCVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:A/VC:N/VI:N/VA:N/SC:L/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
EPSS Score 0.0039 32th percentile
Risk Priority 35 floored blend · peak EPSS

Summary

CVE-2024-47528 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Librenms Librenms. Its CVSS base score is 4.6 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 32th 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

Vulnerability Data

LibreNMS is an open-source, PHP/MySQL/SNMP-based network monitoring system. Stored Cross-Site Scripting (XSS) can be achieved by uploading a new Background for a Custom Map. Users with "admin" role can set background for a custom map, this allow the upload of…

more

SVG file that can contain XSS payload which will trigger on load. This led to Stored Cross-Site Scripting (XSS). The vulnerability is fixed in 24.9.0.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1185 Browser Session Hijacking Collection
Adversaries may take advantage of security vulnerabilities and inherent functionality in browser software to change content, modify user-behaviors, and intercept information as part of various browser session hijacking techniques.
T1539 Steal Web Session Cookie Credential Access
An adversary may steal web application or service session cookies and use them to gain access to web applications or Internet services as an authenticated user without needing credentials.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
T1505 Server Software Component Persistence
Adversaries may abuse legitimate extensible development features of servers to establish persistent access to systems.
T1505.003 Web Shell Persistence
Adversaries may backdoor web servers with web shells to establish persistent access to systems.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-5060Same product: Librenms Librenms
CVE-2023-48295Same product: Librenms Librenms
CVE-2023-4347Same product: Librenms Librenms
CVE-2023-4978Same product: Librenms Librenms
CVE-2023-4979Same product: Librenms Librenms
CVE-2023-4981Same product: Librenms Librenms
CVE-2023-4980Same product: Librenms Librenms
CVE-2023-4982Same product: Librenms Librenms
CVE-2023-37308Same product class: network monitoring / SIEM
CVE-2023-33231Same product class: network monitoring / SIEM

Affected Assets

librenms
librenms
≤ 24.9.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.1.2
  • V1.2.1
  • V1.2.3
  • V5.1.1

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.

Malicious-code protection at entry points blocks dangerous file types from being accepted and executed.

Least functionality restricts the file types and automatic processing capabilities the system will accept.

SC-18 Mobile Code partial match

Mobile-code controls define, authorize, and block unacceptable uploaded code before automatic processing occurs.

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.

PR.PS-05 mostly match
prevents

Restricting execution of unauthorized software directly blocks dangerous uploaded files from running.

PR.PS-06 mostly match
prevents

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).

PR.PS-01 partial match
prevents

Hardened configuration baselines can enforce allowed file types and processing rules.

PR.PS-02 partial match
prevents

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.

prevents

Secure coding standards explicitly require correct output encoding and escaping to preserve message structure.

finds

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

prevents

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.

prevents

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.

prevents

Application security requirements include explicit rules for safe output handling and encoding.

References