Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-5352 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Lunary Lunary. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 40th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
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.
A critical stored Cross-Site Scripting (XSS) vulnerability, identified as CVE-2025-5352, affects the Analytics component in lunary-ai/lunary versions up to 1.9.23. The issue stems from the NEXT_PUBLIC_CUSTOM_SCRIPT environment variable being directly injected into the DOM using React's dangerouslySetInnerHTML without any sanitization or validation, enabling arbitrary JavaScript execution. This flaw, associated with CWE-79 and scored 9.6 on the CVSS v3.1 scale (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H), was published on 2025-08-23.
An attacker who can control the NEXT_PUBLIC_CUSTOM_SCRIPT environment variable—such as during deployment or through server compromise—can exploit this to inject malicious JavaScript that executes persistently in the browsers of all users. Successful exploitation allows complete account takeover, data exfiltration, malware distribution, and ongoing attacks impacting every user until the variable is cleaned.
The vulnerability is fixed in lunary-ai/lunary version 1.9.25, as detailed in the patching commit at https://github.com/lunary-ai/lunary/commit/e2e43e88cecf742bacb639ab880507bbfdfd065c and the associated Huntr bounty report at https://huntr.com/bounties/f1d3dbce-3c3e-480e-b81e-0e8afa05c491. Security practitioners should upgrade to the patched version and review environment variable configurations to mitigate risks.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-25632
Vulnerability Data
A critical stored Cross-Site Scripting (XSS) vulnerability exists in the Analytics component of lunary-ai/lunary versions up to 1.9.23, where the NEXT_PUBLIC_CUSTOM_SCRIPT environment variable is directly injected into the DOM using dangerouslySetInnerHTML without any sanitization or validation. This allows arbitrary JavaScript…
more
execution in all users' browsers if an attacker can control the environment variable during deployment or through server compromise. The vulnerability can lead to complete account takeover, data exfiltration, malware distribution, and persistent attacks affecting all users until the environment variable is cleaned. The issue is fixed in version 1.9.25.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, lunary
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.