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Mitigation Strategies Analysis for quivrhq/quivr

Mitigation Strategy: Encryption at Rest for Knowledge Bases (Quivr-Specific)

  • Description:
    1. Identify Quivr's Knowledge Base Storage: Determine the specific database or storage mechanism Quivr uses to store ingested documents, website data, and other knowledge.
    2. Enable Storage Encryption: Configure the identified storage system (e.g., database, file system) to use encryption at rest. Refer to the documentation of the storage solution for specific steps on enabling encryption.
    3. Secure Key Management within Quivr Deployment: Ensure that encryption keys are managed securely within the Quivr deployment environment. Avoid storing keys directly within Quivr's configuration files. Utilize environment variables or a dedicated secrets management solution accessible by Quivr.
    4. Verify Encryption in Quivr Environment: After enabling encryption, verify that data stored by Quivr is indeed encrypted at rest. This might involve inspecting storage configurations or using storage-specific tools to confirm encryption status.
  • List of Threats Mitigated:
    • Data Breach of Quivr Knowledge Bases - Severity: High
    • Unauthorized Physical Access to Quivr Data Storage - Severity: High
    • Compliance Violations related to Quivr Data Storage - Severity: High
  • Impact:
    • Data Breach of Quivr Knowledge Bases: Significantly reduces risk by rendering stored knowledge unintelligible without decryption keys.
    • Unauthorized Physical Access to Quivr Data Storage: Significantly reduces risk in scenarios where physical storage media is compromised.
    • Compliance Violations: Significantly reduces risk by addressing data protection requirements for data at rest within Quivr.
  • Currently Implemented: Needs Investigation - It's unclear if Quivr, in its default configuration, automatically enables encryption at rest. This depends on the chosen database and deployment setup.
  • Missing Implementation: Likely missing in default Quivr configurations. Developers deploying Quivr need to proactively enable encryption at rest for their chosen storage solution. Users should ensure their Quivr instance has encryption enabled.

Mitigation Strategy: Enforce Access Control on Quivr Knowledge Bases

  • Description:
    1. Implement User Authentication in Quivr: Ensure Quivr has a robust user authentication system to identify and verify users accessing the application.
    2. Define Knowledge Base Permissions within Quivr: Within Quivr's application logic, implement a system to define permissions for knowledge bases. This should allow administrators to control which users or roles can access, modify, or delete specific knowledge bases.
    3. Integrate Permissions into Quivr UI and Backend: Reflect these permissions in Quivr's user interface, preventing unauthorized users from seeing or interacting with restricted knowledge bases. Enforce these permissions in Quivr's backend logic to prevent API access bypasses.
    4. Regularly Audit Quivr User Access: Periodically review user accounts and knowledge base permissions within Quivr to ensure they are correctly configured and up-to-date.
  • List of Threats Mitigated:
    • Unauthorized Access to Sensitive Quivr Knowledge - Severity: High
    • Data Leakage from Quivr due to Over-Permissive Access - Severity: Medium
    • Insider Threats within Quivr User Base - Severity: Medium
  • Impact:
    • Unauthorized Access to Sensitive Quivr Knowledge: Significantly reduces risk by restricting access to authorized users only within the Quivr application.
    • Data Leakage from Quivr: Moderately reduces risk by limiting access to a need-to-know basis within the Quivr environment.
    • Insider Threats within Quivr: Moderately reduces risk by controlling what actions internal users can perform on knowledge bases within Quivr.
  • Currently Implemented: Needs Investigation - The level of access control in Quivr needs to be assessed. Basic user management might exist, but granular knowledge base permissions might be lacking.
  • Missing Implementation: Potentially missing granular access control for knowledge bases within Quivr itself. Developers should implement a permission system within Quivr to manage access to sensitive knowledge. Users should request and utilize such features if available.

Mitigation Strategy: Robust Input Validation and Sanitization for Quivr Ingested Data

  • Description:
    1. Identify Quivr Data Ingestion Points: Specifically focus on data ingestion methods used by Quivr, such as document uploads, website scraping, and potentially API integrations for knowledge sources.
    2. Implement Sanitization in Quivr Ingestion Modules: Within Quivr's code responsible for data ingestion, implement sanitization routines. This should include:
      • HTML Sanitization in Quivr Scraper: If Quivr scrapes websites, sanitize HTML content within the scraping module before storing it in knowledge bases.
      • Document Parsing Sanitization in Quivr: When parsing uploaded documents, sanitize content to prevent injection attacks when the content is later displayed or processed by Quivr.
      • General Input Sanitization in Quivr: Apply general sanitization to all ingested data within Quivr's ingestion pipeline to remove or escape potentially harmful characters.
    3. Context-Aware Sanitization in Quivr: Ensure sanitization is context-aware within Quivr. For example, sanitize differently for displaying content in the UI versus processing it in backend logic.
  • List of Threats Mitigated:
    • Cross-Site Scripting (XSS) in Quivr UI from Ingested Data - Severity: High
    • HTML Injection in Quivr Displayed Content - Severity: Medium
    • Data Integrity Issues in Quivr Knowledge Bases - Severity: Medium
  • Impact:
    • Cross-Site Scripting (XSS) in Quivr UI: Significantly reduces risk of XSS vulnerabilities originating from malicious content ingested by Quivr.
    • HTML Injection in Quivr Displayed Content: Moderately reduces risk of users being misled or manipulated by injected HTML in Quivr.
    • Data Integrity Issues in Quivr Knowledge Bases: Moderately reduces risk of data corruption due to malformed or malicious input processed by Quivr.
  • Currently Implemented: Needs Investigation - The level of input sanitization within Quivr's data ingestion processes needs to be examined in the codebase.
  • Missing Implementation: Potentially missing comprehensive sanitization within Quivr's data ingestion modules. Developers should implement robust sanitization specifically within Quivr's code that handles document parsing, web scraping, and other data intake.

Mitigation Strategy: Secure Secrets Management for Quivr API Keys

  • Description:
    1. Identify API Key Usage in Quivr Code: Pinpoint where Quivr uses API keys for language models (e.g., OpenAI), vector databases, or other external services within its codebase.
    2. Externalize API Keys from Quivr Configuration: Ensure API keys are not hardcoded in Quivr's configuration files or source code. Configure Quivr to load API keys from environment variables or a dedicated secrets management system.
    3. Secure Deployment Environment for Quivr Secrets: When deploying Quivr, use secure methods to provide API keys to the application. This could involve setting environment variables in the deployment environment or configuring Quivr to access a secrets vault.
    4. Restrict Access to Quivr Secrets Storage: Limit access to the storage location of API keys (environment variables or secrets vault) to only authorized systems and personnel involved in deploying and managing Quivr.
  • List of Threats Mitigated:
    • Exposure of Quivr API Keys in Configuration or Code - Severity: High
    • Unauthorized Use of Quivr's Language Model API Keys - Severity: High
    • Compromise of Quivr API Keys leading to Service Abuse - Severity: High
  • Impact:
    • Exposure of Quivr API Keys: Significantly reduces risk of accidental or intentional exposure of sensitive API keys associated with Quivr.
    • Unauthorized Use of Quivr's Language Model API Keys: Significantly reduces risk of unauthorized parties using Quivr's API keys if they are not readily accessible.
    • Compromise of Quivr API Keys: Moderately reduces risk by making it harder to obtain API keys compared to hardcoding, though compromise is still possible if the secrets management system is breached.
  • Currently Implemented: Needs Investigation - How Quivr handles API keys needs to be checked in its codebase and documentation. Best practices would dictate using environment variables or a secrets manager.
  • Missing Implementation: Potentially missing secure secrets management practices in default Quivr setup. Developers should ensure Quivr is configured to load API keys from secure external sources. Users deploying Quivr must use secure methods to provide API keys, avoiding hardcoding.

Mitigation Strategy: Implement Input Sanitization for Quivr User Prompts

  • Description:
    1. Locate Prompt Handling in Quivr Code: Identify the code in Quivr that takes user prompts and sends them to the language model API.
    2. Implement Prompt Sanitization in Quivr: Within Quivr's prompt handling logic, add sanitization steps. This could include:
      • Keyword Filtering in Quivr: Filter out or escape potentially harmful keywords or command sequences within user prompts before sending them to the language model.
      • Prompt Length Limits in Quivr: Enforce limits on the length of user prompts within Quivr to prevent excessively long or complex prompts.
      • Regex-Based Sanitization in Quivr: Use regular expressions within Quivr to detect and neutralize potentially malicious patterns in user prompts.
    3. Apply Sanitization Before API Call in Quivr: Ensure sanitization is applied to user prompts within Quivr immediately before the prompt is sent to the language model API.
    4. Regularly Update Quivr Prompt Sanitization Rules: As prompt injection techniques evolve, regularly review and update the sanitization rules implemented in Quivr.
  • List of Threats Mitigated:
    • Prompt Injection Attacks via Quivr Interface - Severity: High
    • Abuse of Language Model Functionality through Quivr - Severity: Medium
    • Unintended Language Model Actions initiated via Quivr Prompts - Severity: Medium
  • Impact:
    • Prompt Injection Attacks via Quivr Interface: Moderately reduces risk of successful prompt injection attacks originating from user input to Quivr.
    • Abuse of Language Model Functionality through Quivr: Moderately reduces risk of users misusing the language model through Quivr by crafting malicious prompts.
    • Unintended Language Model Actions initiated via Quivr Prompts: Moderately reduces risk of the language model performing harmful actions due to malicious prompts entered through Quivr.
  • Currently Implemented: Needs Investigation - The presence and effectiveness of prompt sanitization within Quivr's code needs to be assessed.
  • Missing Implementation: Potentially missing specific prompt sanitization within Quivr. Developers should implement prompt sanitization directly within Quivr's prompt processing logic. Users should be aware of prompt injection risks when using Quivr.