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Attack Tree Analysis for microsoft/semantic-kernel

Objective: To gain unauthorized access to sensitive data, manipulate application logic, or disrupt the application's functionality by exploiting vulnerabilities or misconfigurations within the Semantic Kernel framework and its integration.

Attack Tree Visualization

  • Attack Goal: Compromise Semantic Kernel Application [CRITICAL NODE]
    • 1. Exploit Input Manipulation (Prompt Injection & Data Poisoning) [CRITICAL NODE]
      • 1.1. Direct Prompt Injection [HIGH RISK PATH] [CRITICAL NODE]
        • 1.1.1. Bypass Input Sanitization
          • 1.1.1.1. Craft Malicious Prompts to Overwhelm Sanitization [HIGH RISK PATH]
        • 1.1.2. Manipulate Kernel Functions via Prompts [HIGH RISK PATH]
          • 1.1.2.1. Trigger Unintended Function Calls [HIGH RISK PATH]
          • 1.1.2.2. Modify Function Parameters via Prompts [HIGH RISK PATH]
        • 1.1.3. Exfiltrate Data via LLM Response [HIGH RISK PATH]
          • 1.1.3.1. Embed Exfiltration Commands in Prompts [HIGH RISK PATH]
      • 1.2.2. Manipulate User-Generated Content [HIGH RISK PATH]
        • 1.2.2.1. Inject Malicious Content into User Profiles/Inputs [HIGH RISK PATH]
    • 2. Exploit Plugin Vulnerabilities [CRITICAL NODE]
      • 2.1. Native Plugin Vulnerabilities [HIGH RISK PATH] [CRITICAL NODE]
        • 2.1.1. Code Injection in Native Plugins [HIGH RISK PATH]
          • 2.1.1.1. Exploit Unsafe Deserialization in Plugins [HIGH RISK PATH]
        • 2.1.3. Dependency Vulnerabilities in Native Plugins [HIGH RISK PATH]
          • 2.1.3.1. Exploit Known Vulnerabilities in Plugin Libraries [HIGH RISK PATH]
      • 2.2.2. Overly Permissive Semantic Function Access [HIGH RISK PATH]
        • 2.2.2.1. Access Sensitive Resources via Semantic Functions [HIGH RISK PATH]
    • 3. Exploit Configuration and Orchestration Weaknesses [CRITICAL NODE]
      • 3.1. Insecure Kernel Configuration [HIGH RISK PATH] [CRITICAL NODE]
        • 3.1.1. Weak API Key Management [HIGH RISK PATH] [CRITICAL NODE]
          • 3.1.1.1. Expose API Keys in Code or Logs [HIGH RISK PATH] [CRITICAL NODE]
        • 3.1.2. Overly Permissive Access Controls [HIGH RISK PATH]
          • 3.1.2.1. Allow Unauthorized Plugin or Function Access [HIGH RISK PATH]
    • 4. Exploit Dependency Vulnerabilities (Semantic Kernel & Underlying Libraries) [CRITICAL NODE]
      • 4.1. Vulnerabilities in Semantic Kernel Library Itself [HIGH RISK PATH]
        • 4.1.1. Exploit Known CVEs in Semantic Kernel [HIGH RISK PATH]
      • 4.2. Vulnerabilities in Underlying LLM Client Libraries [HIGH RISK PATH]
        • 4.2.1. Exploit CVEs in Libraries Used for LLM Communication [HIGH RISK PATH]

Attack Tree Path: 1.1.1.1. Craft Malicious Prompts to Overwhelm Sanitization

Description: Attackers craft sophisticated prompts designed to bypass input sanitization mechanisms. This could involve using encoding, obfuscation, or complex prompt structures that are not recognized or properly handled by the sanitization filters. Likelihood: Medium Impact: Medium - Can lead to unintended function calls, data manipulation, or information disclosure depending on the prompt's objective. Mitigation:

  • Implement robust, context-aware input sanitization and validation.
  • Use techniques like adversarial prompt testing to identify weaknesses in sanitization.
  • Consider using allow-lists and content security policies in addition to deny-lists.
  • Regularly update and refine sanitization rules based on emerging prompt injection techniques.

Attack Tree Path: 1.1.2.1. Trigger Unintended Function Calls

Description: Attackers manipulate prompts to trick the LLM into requesting the execution of Semantic Kernel functions that are not intended for the current user or context. This could involve prompting the LLM to call administrative functions or functions that access sensitive data. Likelihood: Medium Impact: Medium - Can lead to unauthorized actions, data access, or privilege escalation depending on the function called. Mitigation:

  • Implement strict role-based access control (RBAC) or attribute-based access control (ABAC) for Semantic Kernel functions.
  • Validate user authorization before executing any function call requested by the LLM.
  • Design functions to be context-aware and only perform actions appropriate to the current user and context.
  • Use function calling features with explicit schema validation to limit function parameters.

Attack Tree Path: 1.1.2.2. Modify Function Parameters via Prompts

Description: Attackers craft prompts to influence the LLM to modify the parameters passed to Semantic Kernel functions. This could involve changing parameters to access different data, perform actions on unintended targets, or bypass security checks. Likelihood: Medium Impact: Medium - Can lead to data breaches, unauthorized modifications, or denial of service depending on the function and parameters manipulated. Mitigation:

  • Validate and sanitize function parameters after they are generated by the LLM and before function execution.
  • Implement strong input validation within function code itself to handle unexpected or malicious parameter values.
  • Use type checking and schema validation for function parameters.
  • Principle of least privilege: functions should only accept the minimum necessary parameters.

Attack Tree Path: 1.1.3.1. Embed Exfiltration Commands in Prompts

Description: Attackers craft prompts that instruct the LLM to retrieve and output sensitive information within its response. This could involve using indirect questions, conditional statements, or other techniques to trick the LLM into revealing data that should be kept confidential. Likelihood: Medium Impact: Medium - Can lead to the disclosure of sensitive data through LLM responses. Mitigation:

  • Carefully review and sanitize LLM responses before displaying them to users or using them in further application logic.
  • Implement output filtering to detect and remove potentially sensitive information from LLM responses.
  • Use prompt engineering techniques to guide the LLM to avoid revealing sensitive information in its responses.
  • Consider using techniques like differential privacy or federated learning if handling sensitive data.

Attack Tree Path: 1.2.2.1. Inject Malicious Content into User Profiles/Inputs

Description: Attackers inject malicious content into user-generated data, such as user profiles, comments, or other inputs. This poisoned data can then be used by the Semantic Kernel application in prompts or as context for LLM operations, leading to indirect prompt injection. Likelihood: Medium to High Impact: Low to Medium - Can lead to the propagation of malicious content, manipulation of application behavior, or social engineering attacks. Mitigation:

  • Sanitize and validate all user-generated content before it is stored and used by the Semantic Kernel application.
  • Implement content moderation and monitoring to detect and remove malicious content.
  • Use input validation on the client-side and server-side to prevent injection of malicious content.
  • Regularly scan user-generated content for malicious patterns.

Attack Tree Path: 2.1.1.1. Exploit Unsafe Deserialization in Plugins

Description: Native plugins might use deserialization to process data, and if this deserialization is performed unsafely on untrusted data, it can lead to code injection vulnerabilities. Attackers can craft malicious serialized data that, when deserialized by the plugin, executes arbitrary code on the server. Likelihood: Low to Medium Impact: High to Critical - Can lead to complete server compromise, data breaches, and denial of service. Mitigation:

  • Avoid using unsafe deserialization techniques in native plugins.
  • If deserialization is necessary, use secure deserialization libraries and practices.
  • Validate and sanitize all data before deserialization.
  • Implement input validation and output encoding within plugins.
  • Regularly audit plugin code for deserialization vulnerabilities.

Attack Tree Path: 2.1.3.1. Exploit Known Vulnerabilities in Plugin Libraries

Description: Native plugins often rely on external libraries. If these libraries have known vulnerabilities, attackers can exploit them to compromise the application. This could involve exploiting CVEs in outdated or vulnerable versions of libraries used by the plugins. Likelihood: Low to Medium Impact: Medium to High - Can lead to code execution, data breaches, or denial of service depending on the vulnerability exploited. Mitigation:

  • Maintain a comprehensive inventory of dependencies used by native plugins.
  • Regularly update plugin dependencies to the latest secure versions.
  • Use dependency scanning tools to automatically identify vulnerable libraries.
  • Implement a patch management process for plugin dependencies.

Attack Tree Path: 2.2.2.1. Access Sensitive Resources via Semantic Functions

Description: Semantic functions might be granted overly broad access to sensitive resources or functionalities. Attackers can exploit this by using semantic functions to access data or perform actions that they should not be authorized to perform, potentially bypassing intended access controls. Likelihood: Medium Impact: Medium to High - Can lead to unauthorized access to sensitive data, privilege escalation, or data manipulation. Mitigation:

  • Implement role-based access control (RBAC) or attribute-based access control (ABAC) for semantic functions.
  • Principle of least privilege: Grant semantic functions only the minimum necessary permissions to access resources.
  • Regularly audit and review access controls for semantic functions.
  • Implement clear separation of duties and responsibilities for different semantic functions.

Attack Tree Path: 3.1.1.1. Expose API Keys in Code or Logs

Description: API keys for LLM providers or other services are sensitive credentials. If these keys are exposed in code repositories, configuration files, or logs, attackers can easily discover and misuse them. This can lead to unauthorized access to LLM services, financial charges, and data breaches. Likelihood: Low to Medium Impact: High to Critical - Can lead to full access to LLM services, financial costs, data breaches, and reputational damage. Mitigation:

  • Use secure secrets management solutions (e.g., Azure Key Vault, HashiCorp Vault) to store and manage API keys.
  • Never hardcode API keys in code or configuration files.
  • Avoid logging API keys in application logs.
  • Implement regular secrets rotation and auditing.
  • Use environment variables or configuration management tools to inject API keys at runtime.

Attack Tree Path: 3.1.2.1. Allow Unauthorized Plugin or Function Access

Description: If the Semantic Kernel application lacks proper access controls, attackers might be able to execute plugins or functions without authorization. This can allow them to bypass intended security measures and perform actions that they should not be permitted to do. Likelihood: Low to Medium Impact: Medium to High - Can lead to unauthorized actions, data access, privilege escalation, or denial of service depending on the capabilities of the accessible plugins and functions. Mitigation:

  • Implement robust access control mechanisms within Semantic Kernel to restrict access to plugins and functions based on user roles or permissions.
  • Use authentication and authorization middleware to verify user identity and permissions before allowing access to Semantic Kernel functionalities.
  • Regularly audit and review access control configurations.
  • Follow the principle of least privilege: grant users only the necessary permissions.

Attack Tree Path: 4.1.1. Exploit Known CVEs in Semantic Kernel

Description: The Semantic Kernel library itself might contain vulnerabilities that are publicly disclosed as CVEs. Attackers can exploit these known vulnerabilities if the application is using a vulnerable version of the library. Likelihood: Low Impact: High to Critical - Can lead to code execution, data breaches, or denial of service depending on the specific vulnerability. Mitigation:

  • Stay updated with the latest Semantic Kernel releases and security patches.
  • Monitor security advisories and CVE databases for known vulnerabilities in Semantic Kernel.
  • Regularly update the Semantic Kernel library to the latest stable version.
  • Implement vulnerability scanning to detect known CVEs in used libraries.

Attack Tree Path: 4.2.1. Exploit CVEs in Libraries Used for LLM Communication

Description: Semantic Kernel relies on client libraries to communicate with LLM providers (e.g., openai-python). These client libraries might also contain vulnerabilities that are publicly disclosed as CVEs. Attackers can exploit these vulnerabilities if the application is using vulnerable versions of these libraries. Likelihood: Low to Medium Impact: Medium to High - Can lead to code execution, data breaches, or denial of service depending on the specific vulnerability. Mitigation:

  • Maintain an inventory of dependencies used by Semantic Kernel, including LLM client libraries.
  • Regularly update these libraries to the latest secure versions.
  • Use dependency scanning tools to automatically identify vulnerable libraries.
  • Implement a patch management process for LLM client library dependencies.