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Attack Tree Analysis for quivrhq/quivr

Objective: Exfiltrate Sensitive Data or Gain Unauthorized Backend Access via Quivr

Attack Tree Visualization

Goal: Exfiltrate Sensitive Data or Gain Unauthorized Backend Access via Quivr

├── 1.  Compromise Quivr's Brain Management/Backend Connections  [CRITICAL]
│   ├── 1.1  Exploit Weaknesses in Brain Management Logic
│   │   ├── 1.1.1  Unauthorized Brain Creation/Deletion/Modification
│   │   │   ├── 1.1.1.1  Bypass API Authentication/Authorization for Brain Operations (e.g., /brains/{brain_id}) [CRITICAL]
│   │   │   ├── 1.1.1.2  Inject Malicious Brain Configuration (e.g., pointing to attacker-controlled backend) [HIGH RISK]
│   ├── 1.2  Compromise Backend Connections [CRITICAL]
│   │   ├── 1.2.1  Steal Backend Credentials (e.g., API keys, database passwords) [HIGH RISK]
│   │   │   ├── 1.2.1.1  Exploit Vulnerabilities in Credential Storage/Handling [CRITICAL]

├── 2. Exploit Quivr's Document Upload/Processing
│   ├── 2.2  Exploit Document Parsing/Processing Logic
│   │   ├── 2.2.2  Inject Malicious Content that is Processed by the LLM
│   │   │   ├── 2.2.2.1  Prompt Injection Attacks to Exfiltrate Data or Manipulate LLM Behavior [HIGH RISK]

├── 3. Exploit Quivr's Chat/Query Interface [CRITICAL]
    ├── 3.1  Prompt Injection [HIGH RISK]
    │   ├── 3.1.1  Direct Prompt Injection to the LLM [CRITICAL]

Attack Tree Path: 1. Compromise Quivr's Brain Management/Backend Connections [CRITICAL]

  • Description: This is the core of Quivr's functionality. Compromising this allows control over data storage, retrieval, and backend interactions.

    • 1.1 Exploit Weaknesses in Brain Management Logic

      • 1.1.1 Unauthorized Brain Creation/Deletion/Modification
        • 1.1.1.1 Bypass API Authentication/Authorization for Brain Operations (e.g., /brains/{brain_id}) [CRITICAL]
          • Attack Vector: An attacker exploits weaknesses in the API authentication or authorization mechanisms to perform unauthorized actions on brains (create, delete, modify). This could involve:
            • Forging authentication tokens.
            • Exploiting session management vulnerabilities.
            • Bypassing authorization checks due to misconfigured roles or permissions.
          • Likelihood: Medium (if auth is weak), Low (if auth is strong)
          • Impact: High (full control over brains)
          • Effort: Low (if auth is weak), Medium (if auth is strong)
          • Skill Level: Intermediate
          • Detection Difficulty: Medium (with proper logging), Hard (without logging)
        • 1.1.1.2 Inject Malicious Brain Configuration (e.g., pointing to attacker-controlled backend) [HIGH RISK]
          • Attack Vector: An attacker gains access to modify brain configurations and sets the backend to a system they control. This allows them to intercept all data sent to that brain. This could involve:
            • Exploiting insufficient input validation on brain configuration parameters.
            • Using compromised credentials to access the brain management interface.
          • Likelihood: Medium
          • Impact: High (data exfiltration, backend compromise)
          • Effort: Medium
          • Skill Level: Intermediate
          • Detection Difficulty: Hard (if configuration changes are not audited)

    • 1.2 Compromise Backend Connections [CRITICAL]

      • 1.2.1 Steal Backend Credentials (e.g., API keys, database passwords) [HIGH RISK]
        • Attack Vector: The attacker obtains the credentials needed to directly access the backends connected to Quivr (databases, cloud storage).
        • 1.2.1.1 Exploit Vulnerabilities in Credential Storage/Handling [CRITICAL]
          • Attack Vector: An attacker finds and exploits vulnerabilities in how Quivr stores or handles backend credentials. This could involve:
            • Accessing hardcoded credentials in the codebase or configuration files.
            • Exploiting vulnerabilities in a secrets management system (if one is misconfigured or has vulnerabilities).
            • Gaining access to environment variables containing credentials.
          • Likelihood: Low (if secrets management is used), High (if credentials are hardcoded)
          • Impact: Very High (full backend compromise)
          • Effort: Low (if credentials are easily accessible), High (if strong secrets management is used)
          • Skill Level: Intermediate to Advanced
          • Detection Difficulty: Hard (if no credential access auditing)

Attack Tree Path: 2. Exploit Quivr's Document Upload/Processing

  • 2.2 Exploit Document Parsing/Processing Logic * 2.2.2 Inject Malicious Content that is Processed by the LLM * 2.2.2.1 Prompt Injection Attacks to Exfiltrate Data or Manipulate LLM Behavior [HIGH RISK] * Attack Vector: An attacker uploads a document containing carefully crafted text that, when processed by the LLM, causes it to reveal sensitive information or perform unintended actions. This leverages the inherent vulnerability of LLMs to prompt injection. * Likelihood: High (LLMs are inherently vulnerable to prompt injection) * Impact: Medium to High (data leakage, manipulated responses) * Effort: Low to Medium * Skill Level: Intermediate * Detection Difficulty: Medium (with output monitoring), Hard (without)

Attack Tree Path: 3. Exploit Quivr's Chat/Query Interface [CRITICAL]

  • 3.1 Prompt Injection [HIGH RISK] * 3.1.1 Direct Prompt Injection to the LLM [CRITICAL] * Attack Vector: An attacker directly enters a malicious prompt into the chat interface, attempting to trick the LLM into revealing sensitive information, bypassing security controls, or executing unintended commands. * Likelihood: High * Impact: Medium to High * Effort: Low to Medium * Skill Level: Intermediate * Detection Difficulty: Medium