Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-40322 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in B3Log Siyuan. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 23th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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-2026-40322 is a high-severity vulnerability (CVSS 9.0) affecting SiYuan, an open-source personal knowledge management system, in versions 3.6.3 and prior. The issue stems from Mermaid diagrams being rendered with a "loose" securityLevel, producing SVGs that are injected into the DOM using innerHTML. This permits attacker-controlled javascript: URLs embedded in Mermaid code blocks to persist in the output, enabling stored cross-site scripting (XSS; CWE-79) and code injection (CWE-94). The vulnerability is particularly severe in desktop builds based on Electron, where windows are launched with nodeIntegration enabled and contextIsolation disabled.
An attacker with low privileges (PR:L) can exploit this by inserting a malicious Mermaid diagram into a note. A victim user interaction (UI:R) is required: opening the note and clicking the rendered diagram node triggers the javascript: URL, leading to stored XSS. In Electron desktop environments, this escalates to arbitrary code execution (C:H/I:H/A:H) with network accessibility (AV:N/AC:L) and scope change (S:C), potentially compromising the victim's system fully.
The vulnerability has been addressed in SiYuan version 3.6.4, as detailed in the project's release notes and GitHub Security Advisory GHSA-x63q-3rcj-hhp5. Security practitioners should urge users to update to 3.6.4 or later, verify note contents from untrusted sources, and consider disabling or sandboxing Mermaid rendering in affected deployments until patched.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-23330
Vulnerability Data
SiYuan is an open-source personal knowledge management system. In versions 3.6.3 and below, Mermaid diagrams are rendered with securityLevel set to "loose", and the resulting SVG is injected into the DOM via innerHTML. This allows attacker-controlled javascript: URLs in Mermaid…
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code blocks to survive into the rendered output. On desktop builds using Electron, windows are created with nodeIntegration enabled and contextIsolation disabled, escalating the stored XSS to arbitrary code execution when a victim opens a note containing a malicious Mermaid block and clicks the rendered diagram node. This issue has been fixed in version 3.6.4.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
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.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.