Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2025-30223 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Beego Beego. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 45th 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-2025-30223 is a Cross-Site Scripting (XSS) vulnerability in Beego, an open-source web framework for the Go programming language. Prior to version 2.3.6, the RenderForm() function fails to properly escape user-controlled data when generating HTML form markup, allowing arbitrary JavaScript injection. This issue, classified under CWE-79, carries a CVSS v3.1 base score of 9.3 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N) due to its network accessibility, low complexity, lack of required privileges, reliance on user interaction, cross-scope impact, and high confidentiality and integrity effects. It impacts any Beego-based application that invokes RenderForm() with untrusted input, as developers may incorrectly assume automatic attribute escaping akin to other frameworks.
Attackers can exploit this vulnerability remotely without authentication by tricking users into interacting with a maliciously crafted form rendered via RenderForm(). Upon execution in the victim's browser, the injected JavaScript can steal session cookies, credentials, or perform account takeovers, enabling further actions like data exfiltration or unauthorized actions on the victim's behalf. The changed scope (S:C) amplifies risks, as exploitation occurs in the browser context rather than the server.
The vulnerability is addressed in Beego version 2.3.6, where the RenderForm() function now properly escapes user-controlled data. Official mitigation guidance is available in the Beego security advisory at https://github.com/beego/beego/security/advisories/GHSA-2j42-h78h-q4fg and the fixing commit at https://github.com/beego/beego/commit/939bb18c66406466715ddadd25dd9ffa6f169e25; practitioners should upgrade immediately and audit uses of RenderForm() in existing applications.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-8839
Vulnerability Data
Beego is an open-source web framework for the Go programming language. Prior to 2.3.6, a Cross-Site Scripting (XSS) vulnerability exists in Beego's RenderForm() function due to improper HTML escaping of user-controlled data. This vulnerability allows attackers to inject malicious JavaScript…
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code that executes in victims' browsers, potentially leading to session hijacking, credential theft, or account takeover. The vulnerability affects any application using Beego's RenderForm() function with user-provided data. Since it is a high-level function generating an entire form markup, many developers would assume it automatically escapes attributes (the way most frameworks do). This vulnerability is fixed in 2.3.6.
- 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.