Cyber Resilience

CVE-2024-3570

XSS in Mintplexlabs Anythingllm ≤ 1.0.0

Public PoCXSS
Published
10 April 2024
Modified
09 July 2025
Patch / advisory
View advisory
CVSS Score v3.1 5.4
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N
EPSS Score 0.0031 24th percentile
Risk Priority 40 floored blend · peak EPSS

Summary

CVE-2024-3570 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 24th 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)

A05:2025 Injection

EU & UK References

Vulnerability Data

A stored Cross-Site Scripting (XSS) vulnerability exists in the chat functionality of the mintplex-labs/anything-llm repository, allowing attackers to execute arbitrary JavaScript in the context of a user's session. By manipulating the ChatBot responses, an attacker can inject malicious scripts to…

more

perform actions on behalf of the user, such as creating a new admin account or changing the user's password, leading to a complete takeover of the AnythingLLM application. The vulnerability stems from the improper sanitization of user and ChatBot input, specifically through the use of `dangerouslySetInnerHTML`. Successful exploitation requires convincing an admin to add a malicious LocalAI ChatBot to their AnythingLLM instance.

CWE(s)
CWE-79

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
  • CWE-1426 — XSS via unsanitized generative ChatBot output rendered into DOM
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.
Classification Reason
AnythingLLM is an open-source platform providing a multi-user ChatGPT-like interface for LLMs, including chat functionality with LocalAI ChatBots, fitting the Enterprise AI Assistants category.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1185 Browser Session Hijacking Collection
Adversaries may take advantage of security vulnerabilities and inherent functionality in browser software to change content, modify user-behaviors, and intercept information as part of various browser session hijacking techniques.
attack.mitre.org →
T1539 Steal Web Session Cookie Credential Access
An adversary may steal web application or service session cookies and use them to gain access to web applications or Internet services as an authenticated user without needing credentials.
attack.mitre.org →
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
attack.mitre.org →
T1189 Drive-by Compromise Initial Access
Adversaries may gain access to a system through a user visiting a website over the normal course of browsing.
attack.mitre.org →
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-1649Shared CWE-79
CVE-2023-25837Shared CWE-79
CVE-2023-22438Shared CWE-79
CVE-2023-27614Shared CWE-79
CVE-2023-47164Shared CWE-79
CVE-2023-49145Shared CWE-79
CVE-2023-47853Shared CWE-79
CVE-2023-28474Shared CWE-79
CVE-2023-37636Shared CWE-79
CVE-2023-1006Shared CWE-79

Affected Assets

mintplexlabs
anythingllm
≤ 1.0.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V1.1.2
  • V1.3.2

Mitigating Controls (NIST 800-53 r5) AI

SA-11 Developer Testing and Evaluation good match

Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.

SI-10 Information Input Validation good match

Input validation directly enforces neutralization of untrusted data before it reaches web output generation.

SI-15 Information Output Filtering good match

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.

PR.PS-06 mostly match
prevents

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).

PR.PS-02 partial match
prevents

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.

A.8.29 Security testing in development and acceptance mostly match
finds

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

A.5.6 Contact with special interest groups partial match
prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

A.5.7 Threat intelligence partial match
prevents

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.

A.8.25 Secure development life cycle partial match
prevents

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.

A.8.28 Secure coding partial match
prevents

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.

A.8.7 Protection against malware none match
none

Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.

References