Raw vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:L/A:NSummary
CVE-2026-41318 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Mintplexlabs Anythingllm. Its CVSS base score is 5.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 9th 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 LLM/Generative AI Risks 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-25387
Vulnerability Data
AnythingLLM is an application that turns pieces of content into context that any LLM can use as references during chatting. Prior to version 1.12.1, AnythingLLM's in-chat markdown renderer has an unsafe custom rule for images that interpolates the markdown image's…
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`alt` text into an HTML `alt="..."` attribute without any HTML encoding. Every call-site in the app wraps `renderMarkdown(...)` with `DOMPurify.sanitize(...)` as defense-in-depth — except the `Chartable` component, which renders chart captions with no sanitization. The chart caption is the natural-language text the LLM emits around a `create-chart` tool call, so any attacker who can influence the LLM's output — most cheaply via indirect prompt injection in a shared workspace document, or directly if they can create a chart record in a multi-user workspace — can trigger stored DOM-level XSS in every other user's browser when they open that conversation. AnythingLLM chat history is loaded server-side via `GET /api/workspace/:slug/chats` and rendered directly into the chat UI. Version 1.12.1 contains a patch for this issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1426 — LLM-generated chart caption reaches DOM sink with no validation/sanitization (XSS).
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: anythingllm, llm, prompt injection
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.2.1V1.2.3V1.3.2
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.
Security engineering principles require use of safe templating APIs and proper escaping of external input.
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 standards explicitly require correct output encoding and escaping to preserve message structure.
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.
Application security requirements include explicit rules for safe output handling and encoding.