Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2025-51464 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Aimstack Aim. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 46th 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-51464 is a cross-site scripting vulnerability in Aim version 3.28.0 from aimhubio. It stems from insufficient input sanitization on the /api/reports endpoint, where submitted Python code is processed by Pyodide without sandbox restrictions, allowing direct calls to pyodide.code.run_js() that execute arbitrary JavaScript in the browser of anyone viewing the resulting report.
An unauthenticated remote attacker can submit malicious Python payloads to the endpoint. When a victim subsequently views the generated report, the embedded JavaScript executes with the victim's privileges, enabling theft of session data, account takeover, or other actions consistent with the CVSS 8.8 rating that reflects network attack vector, low complexity, and impacts to confidentiality, integrity, and availability.
The associated GitHub repository, pull request 3333, and Gecko Security analysis provide details on the affected code paths and remediation steps for the reported issue. The EPSS score has remained flat at 0.0188 with no material increase observed since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-22341
Vulnerability Data
Cross-site Scripting (XSS) in aimhubio Aim 3.28.0 allows remote attackers to execute arbitrary JavaScript in victims browsers via malicious Python code submitted to the /api/reports endpoint, which is interpreted and executed by Pyodide when the report is viewed. No sanitisation…
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or sandbox restrictions prevent JavaScript execution via pyodide.code.run_js().
- 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.