Description: An attacker injects crafted documents or links to malicious websites during Quivr's data ingestion process. This could be done by submitting malicious files through an ingestion API or by manipulating website content if Quivr is configured to scrape websites.
Impact: Code execution on the server running Quivr, data poisoning of Quivr's knowledge base leading to manipulated LLM responses, or denial of service due to resource exhaustion within Quivr.
Quivr Component Affected: Data Ingestion Module, Document Loaders, Web Scrapers
Risk Severity: High
Mitigation Strategies:
- Input validation and sanitization for all data ingested by Quivr.
- Use secure document parsers and libraries within Quivr, keeping them updated.
- Implement file type and size limits for documents uploaded to Quivr.
- For web scraping, implement robust URL validation and content sanitization in Quivr's web scraping module.
- Run Quivr's ingestion processes in sandboxed environments if possible.
- Regularly scan data ingested by Quivr for malware or malicious content.
Threat: Vector Database Vulnerabilities
Description: The underlying vector database used by Quivr (e.g., Pinecone, ChromaDB) might have its own security vulnerabilities. Exploiting these vulnerabilities could directly impact Quivr's functionality and data integrity as Quivr relies on the vector database for its core operations.
Impact: Data breach of Quivr's knowledge base, denial of service affecting Quivr's search and retrieval capabilities, or data manipulation within the vector database corrupting Quivr's knowledge.
Quivr Component Affected: Vector Database (External Dependency, but critical for Quivr's operation)
Risk Severity: High
Mitigation Strategies:
- Choose a reputable and actively maintained vector database provider for Quivr.
- Regularly update the vector database used by Quivr to the latest secure versions and apply security patches.
- Follow security best practices recommended by the vector database provider in Quivr's deployment.
- Implement network security measures to protect access to the vector database used by Quivr.
- Monitor security advisories and vulnerability databases for the chosen vector database used by Quivr.
Description: Misconfiguration of Quivr or the vector database infrastructure could lead to unauthorized access to the vector database from outside the application. This allows direct access to Quivr's underlying data storage, bypassing application access controls.
Impact: Direct access to sensitive data stored in Quivr's vector database, bypassing application-level access controls, leading to data breaches and potential misuse of Quivr's knowledge base.
Quivr Component Affected: Vector Database (External Dependency), Infrastructure Configuration, Quivr Deployment Configuration
Risk Severity: High
Mitigation Strategies:
- Use strong and unique credentials for vector database access within Quivr's configuration.
- Restrict network access to the vector database to only authorized components and networks, specifically Quivr instances.
- Regularly audit and review vector database access configurations related to Quivr.
- Implement proper authentication and authorization mechanisms for accessing the vector database API used by Quivr (if exposed).
- Use network segmentation and firewalls to isolate the vector database used by Quivr.
Threat: Prompt Injection Attacks
Description: Users craft malicious prompts to manipulate the LLM's behavior when interacting with Quivr. This can be done by injecting specific commands or instructions within the user query that are interpreted by the LLM as instructions rather than part of the intended query, affecting Quivr's response generation.
Impact: Information disclosure from Quivr's knowledge base, bypassing access controls within Quivr, generating harmful or unintended outputs from Quivr, or denial of service (indirectly through resource consumption by Quivr's LLM interactions).
Quivr Component Affected: Language Model Interaction Module, Query Processing Module
Risk Severity: High
Mitigation Strategies:
- Implement prompt sanitization and input validation in Quivr to detect and neutralize injection attempts.
- Use techniques like prompt engineering and output filtering within Quivr to constrain LLM behavior.
- Implement content security policies to prevent Quivr's LLM from generating harmful or inappropriate content.
- Consider using LLMs with built-in security features or fine-tuning them for secure use within Quivr.
- Educate users about the risks of prompt injection when interacting with applications using Quivr.
Description: Developers assume the LLM used by Quivr is inherently secure and do not implement sufficient input validation, output sanitization, or access controls around Quivr's interaction with the LLM within their application. This leads to vulnerabilities when using Quivr.
Impact: Increased vulnerability to prompt injection and other LLM-related attacks when using Quivr, due to lack of defensive measures in the application integrating Quivr, making it easier to exploit LLM weaknesses through Quivr.
Quivr Component Affected: Application Integration with Quivr, Security Design of application using Quivr, Input/Output Handling in application using Quivr
Risk Severity: High
Mitigation Strategies:
- Adopt a defense-in-depth approach when integrating Quivr and do not solely rely on the LLM's inherent security.
- Implement robust input validation, output sanitization, and access controls around the application's interaction with Quivr's LLM functionalities.
- Regularly review and test the application's security posture against LLM-related threats in the context of Quivr usage.
- Educate developers about LLM security risks and best practices when using Quivr.
- Perform security audits and penetration testing focusing on LLM interactions through Quivr.
Description: Lack of proper authorization checks within the application when using Quivr's functionalities. This means the application does not adequately verify user permissions before allowing access to Quivr features like data ingestion, knowledge base modification, or querying, exposing Quivr's functionalities without proper control.
Impact: Unauthorized users could perform actions they are not permitted to within Quivr, such as ingesting data they shouldn't access into Quivr, modifying Quivr's knowledge base, or accessing sensitive information through queries to Quivr, leading to data breaches or data manipulation within Quivr.
Quivr Component Affected: Application Integration with Quivr, Authorization Module of application, API Endpoints exposing Quivr features in application
Risk Severity: High
Mitigation Strategies:
- Implement robust authorization checks at the application level before invoking Quivr functionalities.
- Use role-based access control (RBAC) or attribute-based access control (ABAC) to manage user permissions for Quivr features within the application.
- Ensure that all API endpoints and user interfaces interacting with Quivr are properly secured with authorization mechanisms in the application.
- Regularly review and audit access control configurations for Quivr features in the application.
- Follow the principle of least privilege when granting access to Quivr features within the application.
Description: If Quivr exposes an API (even indirectly through the application), vulnerabilities in the API authorization mechanisms could allow attackers to bypass access controls and interact with Quivr directly. This allows direct manipulation of Quivr functionalities without proper authorization.
Impact: Direct unauthorized access to Quivr's functionalities and data, potentially bypassing application-level security measures, leading to data breaches, data manipulation within Quivr, or denial of service of Quivr functionalities.
Quivr Component Affected: Quivr API (if exposed), API Authorization Module, Authentication Module
Risk Severity: Critical
Mitigation Strategies:
- Securely design and implement the Quivr API, following API security best practices.
- Implement strong authentication and authorization mechanisms for the Quivr API (e.g., OAuth 2.0, API keys).
- Regularly audit and test the Quivr API security for vulnerabilities.
- Rate limit Quivr API requests to prevent brute-force attacks and denial of service.
- Minimize the exposed Quivr API surface area and only expose necessary functionalities.
Description: Attacks targeting the underlying vector database could lead to its unavailability, directly impacting Quivr's ability to function as Quivr relies on it for knowledge retrieval and LLM responses.
Impact: Application functionality relying on Quivr becomes unavailable due to vector database outage, leading to denial of service for users of the Quivr-integrated application.
Quivr Component Affected: Vector Database (External Dependency, critical for Quivr), Infrastructure supporting Quivr
Risk Severity: High
Mitigation Strategies:
- Choose a vector database provider with robust DDoS protection and high availability infrastructure for Quivr.
- Implement network security measures to protect the vector database used by Quivr from network-level attacks.
- Monitor vector database availability and performance critical for Quivr's operation.
- Implement redundancy and failover mechanisms for the vector database supporting Quivr.
- Follow security best practices recommended by the vector database provider for DDoS mitigation in the context of Quivr usage.