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Mitigation Strategies Analysis for microsoft/semantic-kernel

Mitigation Strategy: Input Sanitization and Validation (Semantic Kernel Context)

  • Mitigation Strategy: Input Sanitization and Validation (Semantic Kernel Context)
  • Description:
    1. Identify Semantic Kernel Input Points: Pinpoint where user input flows into Semantic Kernel specifically. This includes:
      • User input passed directly to Semantic Functions as arguments.
      • User input used to dynamically construct prompts within Semantic Kernel Skills or Orchestrators.
      • Data from external sources (databases, APIs) that is used to populate Semantic Kernel prompts.
    2. Define Semantic Kernel Validation Rules: Establish validation rules tailored to the context of Semantic Kernel prompts and function arguments. Consider:
      • Validating input types expected by Semantic Functions (e.g., string, number, boolean).
      • Validating input format against prompt templates to prevent template injection.
      • Using Semantic Kernel's built-in input validation mechanisms if available (check Semantic Kernel documentation for features).
    3. Implement Sanitization within Semantic Kernel Flow: Integrate sanitization functions within the Semantic Kernel application logic before input is used in prompts or function calls. This could be:
      • Custom sanitization functions called before invoking Semantic Functions.
      • Sanitization steps within Semantic Kernel Skills or Orchestrators.
      • Using Semantic Kernel's built-in sanitization features if available.
    4. Focus on Prompt Injection Prevention: Prioritize sanitization techniques that are effective against prompt injection attacks, such as:
      • Encoding special characters that could be interpreted as prompt commands.
      • Stripping potentially malicious keywords or syntax.
      • Validating input against expected patterns to detect anomalies.
    5. Error Handling within Semantic Kernel: Implement error handling within Semantic Kernel workflows to gracefully manage invalid input and prevent unexpected behavior or crashes.
  • List of Threats Mitigated:
    • Prompt Injection (High Severity): Prevents attackers from manipulating Semantic Kernel's LLM interactions by injecting malicious instructions through user input processed by Semantic Kernel.
    • Semantic Function Argument Injection (Medium Severity): Prevents attackers from injecting malicious arguments into Semantic Functions, potentially leading to unexpected function behavior or vulnerabilities.
  • Impact:
    • Prompt Injection: High reduction. Directly reduces the risk of prompt injection attacks targeting Semantic Kernel applications.
    • Semantic Function Argument Injection: Medium reduction. Mitigates risks associated with malicious input to Semantic Functions.
  • Currently Implemented: Partially implemented in the customer support chat feature, where basic HTML encoding is applied to user input before it's passed to the Semantic Kernel chat skill.
  • Missing Implementation:
    • No validation of input types or formats specifically for Semantic Functions.
    • Sanitization is not consistently applied across all Semantic Kernel input points, especially in API endpoints interacting with Semantic Kernel.
    • Semantic Kernel specific validation features (if any exist) are not utilized.

Mitigation Strategy: Prompt Engineering for Robustness (Semantic Kernel Focus)

  • Mitigation Strategy: Prompt Engineering for Robustness (Semantic Kernel Focus)
  • Description:
    1. Semantic Kernel Prompt Template Design: Design prompt templates within Semantic Kernel Skills and Orchestrators with a focus on robustness against injection.
      • Use clear delimiters in prompt templates to separate system instructions from user input placeholders (e.g., {{$userInput}}).
      • Structure prompts to explicitly instruct the LLM to treat user input as data within the Semantic Kernel context.
      • Incorporate contextual information from Semantic Kernel's context variables to guide LLM interpretation.
    2. Semantic Function Prompt Review: Specifically review prompts used in Semantic Functions for potential injection vulnerabilities.
      • Ensure prompts are well-defined and minimize ambiguity that could be exploited.
      • Test prompts with various inputs, including potential injection attempts, within the Semantic Kernel environment.
    3. Leverage Semantic Kernel Features for Prompt Management: Utilize Semantic Kernel's features for prompt management and versioning to track changes and maintain secure prompt configurations.
    4. Contextual Awareness in Semantic Kernel Prompts: Utilize Semantic Kernel's context management to pass relevant application state and context into prompts. This can help the LLM understand the intended purpose of user input within the application's workflow and reduce misinterpretations.
    5. Iterative Testing within Semantic Kernel: Test prompt robustness within the Semantic Kernel application by simulating user interactions and injection attempts through the application's interface. Observe the application's behavior and refine prompts accordingly.
  • List of Threats Mitigated:
    • Prompt Injection (High Severity): Makes Semantic Kernel prompts more resilient to manipulation, reducing the effectiveness of injection attacks within the application's workflows.
  • Impact:
    • Prompt Injection: Medium to High reduction. Increases the difficulty of successful prompt injection within Semantic Kernel applications. The effectiveness depends on the sophistication of prompt engineering within Semantic Kernel Skills and Orchestrators.
  • Currently Implemented: Basic prompt engineering with delimiters is used in the customer support chat Skill within Semantic Kernel.
  • Missing Implementation:
    • No explicit "instruction following" prompt design within Semantic Kernel Skills.
    • Contextual information from Semantic Kernel's context is not fully leveraged in prompts for robustness.
    • Systematic testing of prompt robustness against injection attempts within the Semantic Kernel application is lacking.

Mitigation Strategy: Plugin and Function Vetting and Auditing (Semantic Kernel)

  • Mitigation Strategy: Plugin and Function Vetting and Auditing (Semantic Kernel)
  • Description:
    1. Semantic Kernel Plugin/Function Inventory: Maintain a clear inventory of all Semantic Kernel Plugins and Functions used in the application, including both native and semantic functions.
    2. Vetting Process for Semantic Kernel Components: Establish a vetting process specifically for Semantic Kernel Plugins and Functions before integration. This includes:
      • Code Review for Native Semantic Kernel Plugins: Thorough code review of native plugins for security vulnerabilities, malicious code, and adherence to secure coding practices within the Semantic Kernel context.
      • Prompt Review for Semantic Functions: Careful review of prompts defining Semantic Functions for security implications, unintended behaviors, and potential prompt injection risks.
      • Dependency Analysis for Semantic Kernel Plugins: Analyze dependencies of native Semantic Kernel plugins for vulnerabilities and outdated libraries.
    3. Semantic Kernel Plugin/Function Security Testing: Perform security testing focused on Semantic Kernel components:
      • Static Analysis for Semantic Kernel Plugins: Use static analysis tools to scan native Semantic Kernel plugin code for vulnerabilities.
      • Dynamic Analysis of Semantic Kernel Functions: Test the runtime behavior of Semantic Functions and native plugins within the Semantic Kernel environment for unexpected actions or vulnerabilities.
    4. Semantic Kernel Plugin/Function Provenance: Track the source and provenance of Semantic Kernel Plugins to ensure they originate from trusted sources and are not tampered with.
    5. Documentation for Semantic Kernel Components: Document the purpose, functionality, dependencies, and security review status of each Semantic Kernel Plugin and Function.
  • List of Threats Mitigated:
    • Malicious Plugin/Function Execution within Semantic Kernel (High Severity): Prevents malicious code or functionality from being introduced into the Semantic Kernel application through compromised or malicious plugins/functions.
    • Vulnerable Plugin/Function Exploitation within Semantic Kernel (High Severity): Reduces the risk of vulnerabilities in Semantic Kernel plugins/functions being exploited to compromise the application or Semantic Kernel's execution environment.
    • Unintended Semantic Function Behavior (Medium Severity): Identifies and mitigates unintended or poorly designed behavior in Semantic Functions that could lead to security issues or application errors within Semantic Kernel workflows.
  • Impact:
    • Malicious Plugin/Function Execution within Semantic Kernel: High reduction. Significantly reduces the risk of malicious components within the Semantic Kernel application.
    • Vulnerable Plugin/Function Exploitation within Semantic Kernel: High reduction. Reduces the likelihood of exploitable vulnerabilities in Semantic Kernel components.
    • Unintended Semantic Function Behavior: Medium reduction. Improves the security and reliability of Semantic Kernel workflows by ensuring functions behave as expected.
  • Currently Implemented: Informal code review is conducted for newly developed native Semantic Kernel plugins by senior developers.
  • Missing Implementation:
    • No formal vetting process specifically for Semantic Kernel Plugins and Functions.
    • No systematic security testing (static/dynamic analysis) of Semantic Kernel components.
    • Semantic Functions are not formally reviewed for security implications within the Semantic Kernel context.
    • Dependency analysis is not performed for Semantic Kernel plugins.
    • Documentation and provenance tracking for Semantic Kernel plugins are missing.

Mitigation Strategy: Principle of Least Privilege for Semantic Kernel Plugins and Functions

  • Mitigation Strategy: Principle of Least Privilege for Semantic Kernel Plugins and Functions
  • Description:
    1. Semantic Kernel Permission Mapping: Map the required permissions for each Semantic Kernel Plugin and Function based on its intended functionality within the Semantic Kernel environment. Consider:
      • Access to specific Semantic Kernel Context variables.
      • Permissions to call other Semantic Kernel Functions or Plugins.
      • Access to external resources through Semantic Kernel (e.g., file system access mediated by a Semantic Kernel plugin).
    2. Implement Semantic Kernel Permission Controls (if available): Utilize any permission control mechanisms provided by Semantic Kernel itself to restrict plugin and function access. (Check Semantic Kernel documentation for features related to plugin/function permissions or sandboxing).
    3. Code-Level Permission Checks in Semantic Kernel Plugins: If Semantic Kernel doesn't provide built-in permission controls, implement permission checks within the code of native Semantic Kernel plugins. This might involve:
      • Checking for specific context variables before performing sensitive actions.
      • Implementing access control logic within plugin methods to restrict resource access.
    4. Regular Semantic Kernel Permission Review: Periodically review the permissions (or implicit access rights) of Semantic Kernel Plugins and Functions to ensure they adhere to the principle of least privilege and are still necessary.
    5. Granular Permissions within Semantic Kernel: Strive for granular control over plugin and function access within the Semantic Kernel environment, rather than broad permissions.
  • List of Threats Mitigated:
    • Privilege Escalation via Semantic Kernel Plugin/Function (High Severity): Prevents a compromised or malicious Semantic Kernel plugin/function from gaining excessive privileges within the Semantic Kernel application and performing unauthorized actions.
    • Lateral Movement within Semantic Kernel (Medium Severity): Limits the ability of a compromised Semantic Kernel plugin/function to access and compromise other parts of the Semantic Kernel application or its context.
    • Data Breach via Semantic Kernel Plugin/Function (High Severity): Reduces the risk of data breaches by limiting the access of Semantic Kernel plugins/functions to sensitive data managed within or accessible through Semantic Kernel.
  • Impact:
    • Privilege Escalation via Semantic Kernel Plugin/Function: High reduction. Significantly limits the potential damage from a compromised plugin/function within the Semantic Kernel application.
    • Lateral Movement within Semantic Kernel: Medium reduction. Makes lateral movement within the Semantic Kernel environment more difficult.
    • Data Breach via Semantic Kernel Plugin/Function: High reduction. Reduces the attack surface for data breaches originating from Semantic Kernel plugins/functions.
  • Currently Implemented: No specific principle of least privilege is currently implemented for Semantic Kernel plugins and functions. Plugins generally operate within the same security context as the main Semantic Kernel application.
  • Missing Implementation:
    • No permission control mechanisms are in place specifically for Semantic Kernel plugins and functions.
    • Permissions are not defined or enforced based on the principle of least privilege within the Semantic Kernel environment.
    • Regular permission reviews for Semantic Kernel components are not conducted.

Mitigation Strategy: Data Sanitization and Anonymization for Semantic Kernel - LLM Interaction

  • Mitigation Strategy: Data Sanitization and Anonymization for Semantic Kernel - LLM Interaction
  • Description:
    1. Identify Sensitive Data in Semantic Kernel Context: Determine what data processed within Semantic Kernel workflows is considered sensitive (e.g., user PII stored in Semantic Kernel context variables, sensitive data retrieved by Semantic Kernel plugins).
    2. Implement Sanitization/Anonymization in Semantic Kernel Flow: Integrate sanitization and anonymization techniques within the Semantic Kernel application logic before sensitive data is included in prompts sent to LLMs. This could be:
      • Sanitization steps within Semantic Kernel Skills or Orchestrators.
      • Custom sanitization functions called before invoking Semantic Functions that interact with LLMs.
      • Using Semantic Kernel's data transformation features (if any) for sanitization.
    3. Apply Before Semantic Kernel - LLM Call: Ensure sanitization/anonymization is applied immediately before the point where Semantic Kernel sends prompts containing sensitive data to the LLM service.
    4. Context-Aware Sanitization in Semantic Kernel: Implement sanitization that is context-aware within Semantic Kernel workflows. Ensure that sanitized/anonymized data still allows Semantic Kernel and the LLM to perform the intended task effectively within the application's logic.
    5. Semantic Kernel Output Sanitization (if needed): Consider sanitizing or filtering LLM outputs within Semantic Kernel before they are used further in the application or presented to the user, especially if outputs might inadvertently re-introduce sensitive information.
  • List of Threats Mitigated:
    • Data Leakage to LLM Provider via Semantic Kernel (High Severity): Prevents sensitive user data processed by Semantic Kernel from being inadvertently shared with third-party LLM providers through Semantic Kernel's LLM interactions.
    • Privacy Violations via Semantic Kernel - LLM Interaction (High Severity): Protects user privacy by preventing the exposure of sensitive information to LLMs through Semantic Kernel prompts and potentially in LLM outputs processed by Semantic Kernel.
    • Compliance Violations related to Semantic Kernel Data Handling (Medium to High Severity): Helps comply with data privacy regulations by minimizing the processing of sensitive data by LLMs through Semantic Kernel workflows.
  • Impact:
    • Data Leakage to LLM Provider via Semantic Kernel: High reduction. Significantly reduces the risk of sensitive data processed by Semantic Kernel being exposed to external LLM providers.
    • Privacy Violations via Semantic Kernel - LLM Interaction: High reduction. Protects user privacy in the context of Semantic Kernel's LLM interactions.
    • Compliance Violations related to Semantic Kernel Data Handling: Medium to High reduction. Contributes to regulatory compliance for data handling within Semantic Kernel applications.
  • Currently Implemented: No data sanitization or anonymization is currently implemented within the Semantic Kernel application logic before sending data to the LLM. Raw user input and context variables are directly used in prompts.
  • Missing Implementation:
    • No identification of sensitive data within Semantic Kernel workflows and context variables.
    • No sanitization or anonymization techniques are implemented within Semantic Kernel Skills or Orchestrators.
    • No functions exist to sanitize or anonymize data specifically within the Semantic Kernel application flow before LLM interaction.

Mitigation Strategy: Secure Storage of API Keys and Secrets for Semantic Kernel LLM Access

  • Mitigation Strategy: Secure Storage of API Keys and Secrets for Semantic Kernel LLM Access
  • Description:
    1. Identify Semantic Kernel LLM API Keys: Identify all API keys and secrets specifically used by Semantic Kernel to access LLM services (e.g., OpenAI API keys, Azure OpenAI Service credentials).
    2. Secure Secrets Management for Semantic Kernel: Choose a secure method for storing and managing these LLM API keys used by Semantic Kernel. Options include:
      • Environment variables configured for the Semantic Kernel application environment.
      • Dedicated secrets management services (e.g., Azure Key Vault, HashiCorp Vault) accessed by the Semantic Kernel application.
      • Secure configuration files loaded by Semantic Kernel, ensuring proper access controls.
    3. Semantic Kernel Secrets Loading: Configure Semantic Kernel to load LLM API keys from the chosen secure secrets management solution instead of hardcoding them in Semantic Kernel code or configuration files. (Refer to Semantic Kernel documentation for secure configuration options).
    4. Restrict Access to Semantic Kernel Secrets: Implement access controls to restrict access to the secrets management solution and the stored LLM API keys to only authorized components and personnel involved in deploying and managing the Semantic Kernel application.
    5. Regular Semantic Kernel API Key Rotation: Establish a process for regularly rotating LLM API keys used by Semantic Kernel to limit the impact of potential key compromise.
  • List of Threats Mitigated:
    • Semantic Kernel LLM API Key/Secret Exposure (High Severity): Prevents the accidental or intentional exposure of LLM API keys used by Semantic Kernel, which could grant unauthorized access to LLM services.
    • Unauthorized LLM Access via Semantic Kernel Keys (High Severity): Prevents unauthorized individuals or systems from using the Semantic Kernel application's LLM API keys to access LLM services and potentially incur costs or perform malicious actions through the context of Semantic Kernel.
    • Data Breach via Compromised Semantic Kernel Keys (High Severity): Reduces the risk of data breaches if compromised LLM API keys used by Semantic Kernel are used to access sensitive data through LLM services or related systems within Semantic Kernel workflows.
  • Impact:
    • Semantic Kernel LLM API Key/Secret Exposure: High reduction. Significantly reduces the risk of LLM API keys used by Semantic Kernel being exposed.
    • Unauthorized LLM Access via Semantic Kernel Keys: High reduction. Prevents unauthorized use of LLM services via compromised keys in the context of Semantic Kernel.
    • Data Breach via Compromised Semantic Kernel Keys: High reduction. Reduces the potential for data breaches stemming from compromised LLM API keys used by Semantic Kernel.
  • Currently Implemented: LLM API keys used by Semantic Kernel are currently stored as environment variables on the server.
  • Missing Implementation:
    • No dedicated secrets management service is used for Semantic Kernel LLM API keys.
    • Access control to environment variables containing Semantic Kernel LLM API keys is not strictly enforced.
    • No automated API key rotation process is in place for Semantic Kernel LLM API keys.