The vulnerability CVE-2026-24477 affects Mintplex-Labs' AnythingLLM application, specifically versions prior to 1.10.0 when configured with Qdrant as the vector database using an API key. AnythingLLM is designed to convert content pieces into context references for large language models (LLMs) during chat interactions, often relying on Qdrant to store semantic knowledge bases for retrieval-augmented generation (RAG). The flaw lies in the exposure of the Qdrant API key in plaintext to unauthenticated users via the /api/setup-complete endpoint. This endpoint inadvertently discloses sensitive credentials without requiring authentication or user interaction. An attacker exploiting this vulnerability gains full read and write access to the Qdrant vector database instance, enabling them to manipulate or exfiltrate the core knowledge base. Since Qdrant stores confidential uploaded documents and semantic indexes, this leads to a compromise of confidentiality and integrity of sensitive data and the disruption of semantic search functionality. The vulnerability is classified under CWE-201 (Insertion of Sensitive Information Into Sent Data). The CVSS 4.0 vector indicates network attack vector, low attack complexity, no privileges or user interaction required, and high impact on confidentiality. No known exploits are reported in the wild yet. The issue is resolved in AnythingLLM version 1.10.0, which removes the API key exposure. Organizations using affected versions should prioritize patching and audit their Qdrant database access logs for suspicious activity.

For European organizations, the impact of CVE-2026-24477 is significant, especially those leveraging AnythingLLM integrated with Qdrant for knowledge management and semantic search in sensitive environments such as finance, healthcare, legal, or government sectors. Unauthorized access to the Qdrant vector database can lead to leakage of confidential documents and intellectual property, undermining data privacy compliance obligations such as GDPR. The integrity of semantic search results can be compromised, potentially causing misinformation or erroneous decision-making based on tampered knowledge bases. Additionally, attackers could disrupt business operations by modifying or deleting critical data in the vector database. The lack of authentication and ease of exploitation increase the risk of widespread abuse. This vulnerability could also facilitate lateral movement within networks if attackers use the exposed data to escalate privileges or access other systems. Overall, the threat poses a high risk to confidentiality, integrity, and availability of critical AI-driven knowledge services in European organizations.

1. Immediate upgrade to AnythingLLM version 1.10.0 or later to eliminate the API key exposure vulnerability. 2. Restrict network access to the /api/setup-complete endpoint using firewall rules or API gateways to prevent unauthenticated external access. 3. Rotate the Qdrant API keys used by AnythingLLM to invalidate any potentially compromised credentials. 4. Implement strict access controls and monitoring on the Qdrant vector database, including logging and alerting on unusual read/write activities. 5. Conduct a thorough audit of AnythingLLM and Qdrant usage to identify any unauthorized access or data exfiltration attempts. 6. Employ network segmentation to isolate AI knowledge base infrastructure from general user networks. 7. Educate development and security teams on secure API key management and avoid embedding sensitive keys in client-accessible endpoints. 8. Consider additional encryption of sensitive data stored in Qdrant to reduce impact if keys are compromised. 9. Regularly review and update security configurations for AI and vector database components as part of the organization's cybersecurity hygiene.