Cyber Resilience

CVE-2024-3028

HighPublic PoC

Published: 16 April 2024

Published
16 April 2024
Modified
09 July 2025
KEV Added
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Patch
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CVSS Score v3 7.2 CVSS:3.0/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H
EPSS Score 0.0019 41.3th percentile
Risk Priority 15 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2024-3028 is a high-severity Improper Input Validation (CWE-20) vulnerability in Mintplexlabs Anythingllm. Its CVSS base score is 7.2 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 41.3th 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 Enterprise AI Assistants; in the Other ATLAS/OWASP Terms risk domain; MITRE ATLAS techniques in scope: Exfiltration via AI Inference API (AML.T0024), External Harms (AML.T0048).

EU & UK References

Vulnerability details

mintplex-labs/anything-llm is vulnerable to improper input validation, allowing attackers to read and delete arbitrary files on the server. By manipulating the 'logo_filename' parameter in the 'system-preferences' API endpoint, an attacker can construct requests to read sensitive files or the application's…

more

'.env' file, and even delete files by setting the 'logo_filename' to the path of the target file and invoking the 'remove-logo' API endpoint. This vulnerability is due to the lack of proper sanitization of user-supplied input.

CWE(s)
CWE-20NVD-CWE-noinfo

AI Security AnalysisAI

AI Category
Enterprise AI Assistants
Risk Domain
Other ATLAS/OWASP Terms
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
mintplex-labs/anything-llm is an open-source full-stack AI application/platform for running and interacting with LLMs, functioning as an enterprise AI assistant with features like chat interfaces, document processing, and system configuration APIs.

Related Threats

MITRE ATT&CK Enterprise TechniquesAI

T1005 Data from Local System Collection
Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration.
attack.mitre.org →
T1070.004 File Deletion Stealth
Adversaries may delete files left behind by the actions of their intrusion activity.
attack.mitre.org →
T1083 File and Directory Discovery Discovery
Adversaries may enumerate files and directories or may search in specific locations of a host or network share for certain information within a file system.
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 →
T1552.001 Credentials In Files Credential Access
Adversaries may search local file systems and remote file shares for files containing insecurely stored credentials.
attack.mitre.org →
Why these techniques?

Path traversal in public-facing API enables exploitation (T1190), arbitrary file read for local data collection (T1005), file discovery (T1083), and credentials from files like .env (T1552.001); arbitrary delete facilitates file deletion for indicator removal (T1070.004).

MITRE ATLAS TechniquesAI

MITRE ATLAS techniques

AML.T0024: Exfiltration via AI Inference APIAML.T0048: External Harms

Affected Assets

mintplexlabs
anythingllm
≤ 1.0.0

Mitigating Controls

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

PM-14 Testing, Training, and Monitoring
addresses: CWE-20

Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.

SA-11 Developer Testing and Evaluation
addresses: CWE-20

Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.

SI-10 Information Input Validation
addresses: CWE-20

Directly implements checks on information inputs to reject invalid data before processing.

SI-8 Spam Protection
addresses: CWE-20

Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.

References