Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/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:XSummary
CVE-2026-33066 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in B3Log Siyuan. Its CVSS base score is 5.3 (Medium).
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-2026-33066 is a cross-site scripting (XSS) vulnerability (CWE-79) in SiYuan, an open-source personal knowledge management system built with Electron. It affects versions 3.6.0 and earlier. The flaw originates in the backend's renderREADME function, which uses lute.New() without invoking SetSanitize(true), permitting raw HTML embedded in Markdown files to bypass sanitization. The frontend then inserts this unsanitized HTML directly into innerHTML without further checks, enabling arbitrary JavaScript execution. The vulnerability carries a CVSS v3.1 base score of 9.0 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H).
An attacker with low privileges, such as a malicious package author on a platform integrated with SiYuan, can embed JavaScript in a package's README.md file. Exploitation occurs when a victim user interacts with the application by clicking to view the package details page, triggering the malicious script. Due to SiYuan's Electron configuration—specifically nodeIntegration set to true and contextIsolation set to false—this client-side XSS escalates seamlessly to full remote code execution (RCE) on the victim's local system, potentially allowing arbitrary file access, data theft, or further compromise.
The issue was addressed in SiYuan version 3.6.1. The GitHub security advisory (GHSA-4663-4mpg-879v) and the patching commit (b382f50e1880ed996364509de5a10a72d7409428) detail the fix, which involves enabling sanitization in the lute renderer to strip dangerous HTML elements from Markdown input. Security practitioners should urge users to update to 3.6.1 or later and review Electron-based applications for similar insecure configurations.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-13628
Vulnerability Data
SiYuan is a personal knowledge management system. In versions 3.6.0 and below, the backend renderREADME function uses lute.New() without calling SetSanitize(true), allowing raw HTML embedded in Markdown to pass through unmodified. The frontend then assigns the rendered HTML to innerHTML…
more
without any additional sanitization. A malicious package author can embed arbitrary JavaScript in their README that executes when a user clicks to view the package details. Because SiYuan's Electron configuration enables nodeIntegration: true with contextIsolation: false, this XSS escalates directly to full Remote Code Execution. The issue was patched in version 3.6.1.
- 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.