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Threat Model Analysis for microsoft/semantic-kernel

Threat: Prompt Injection Leading to Unauthorized Action Execution

  • Description: An attacker crafts a malicious prompt that bypasses intended application logic and security controls implemented within Semantic Kernel's skills/plugins or the Kernel itself. The attacker leverages vulnerabilities in how SK handles prompts and interacts with the LLM to execute actions it shouldn't. This includes manipulating the flow of execution between SK components.
    • Impact:
      • Unauthorized data modification or deletion within systems accessed by SK plugins.
      • Financial loss through actions triggered by compromised SK skills.
      • Reputational damage due to actions performed by the LLM under SK's control.
      • Compromise of sensitive systems connected to SK.
      • Violation of compliance regulations.
    • Affected Semantic Kernel Component:
      • Kernel.InvokeAsync() (and related functions like RunAsync) - The core function that processes prompts and interacts with the LLM, making it the primary target.
      • Any Skill or Plugin that interacts with external systems or data, especially if those skills have elevated privileges.
      • PromptTemplate - If the template itself contains vulnerabilities or is poorly designed, allowing for injection.
      • IPromptTemplateEngine implementations - Custom template engines could have vulnerabilities.
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Input Validation (SK-Specific): Implement strict input validation within Semantic Kernel skills/plugins, before any interaction with the LLM or external systems. Validate data types, lengths, and allowed characters within the context of the skill.
      • Output Validation (SK-Specific): Implement strict output validation within Semantic Kernel skills/plugins, after receiving the LLM's response and before taking any action based on it. Verify the output conforms to expected formats and does not contain malicious commands or data within the context of the skill.
      • Prompt Engineering (SK-Specific): Design prompts used within SK skills/plugins carefully to minimize injection risk. Use clear instructions, delimiters, and system prompts. Consider few-shot examples with safe inputs, tailored to the specific skill.
      • Least Privilege (SK-Specific): Ensure that SK skills/plugins, and the Kernel itself, have only the minimum necessary permissions to perform their tasks. Avoid granting broad access to external systems.
      • Separate Authorization Layer (SK-Specific): Implement a separate, trusted authorization layer within the SK workflow, after LLM processing but before any external action is taken. This layer should validate the LLM's output and determine if the requested action is permitted, independent of the LLM's response.
      • Sandboxing (SK-Specific): If possible, run SK plugins/skills in a sandboxed environment with restricted permissions, limiting their access to the host system and other SK components.
      • Regular Testing (SK-Specific): Regularly test SK skills/plugins with adversarial prompts specifically designed to exploit potential vulnerabilities within the SK context.
      • Dual-LLM Approach (SK-Specific): Consider using a smaller, more controllable LLM within the SK workflow to pre-process and validate user input or skill inputs before passing them to the main LLM.

Threat: Sensitive Data Leakage Through Prompts (within SK Context)

  • Description: Sensitive information is inadvertently included in prompts passed to Kernel.InvokeAsync() or within PromptTemplate definitions used by Semantic Kernel. This information could be logged by SK, stored by the LLM provider, or intercepted. The key here is that the leakage occurs within the SK processing pipeline.
    • Impact:
      • Exposure of confidential data used internally by SK skills/plugins.
      • Violation of privacy regulations.
      • Reputational damage.
      • Potential for further attacks.
    • Affected Semantic Kernel Component:
      • Kernel.InvokeAsync() (and related functions) - The point where prompts are processed.
      • PromptTemplate - If the template contains hardcoded sensitive data or is constructed using untrusted input.
      • Logging mechanisms within SK (e.g., custom ILogger implementations).
    • Risk Severity: High
    • Mitigation Strategies:
      • Data Loss Prevention (DLP) (SK-Specific): Implement DLP techniques to scan prompts within SK skills/plugins before sending them to the LLM. This could involve custom logic within the skill or a dedicated SK component.
      • Input Sanitization (SK-Specific): Sanitize user input and any data used to construct prompts within SK skills/plugins to remove or redact potentially sensitive data.
      • Parameterization (SK-Specific): Use placeholders or indirect references within PromptTemplate definitions and skill logic instead of including sensitive data directly. Pass sensitive data separately through secure channels, if absolutely necessary, and ensure those channels are protected.
      • Secure Logging (SK-Specific): Configure logging within SK (including custom loggers) to avoid storing sensitive information. Redact or mask sensitive data in logs generated by SK components.
      • Review LLM Provider Policies: Carefully review the privacy policies and security practices of the LLM provider regarding data retention and usage, especially in relation to data processed by SK.

Threat: Malicious Plugin Execution (within SK)

  • Description: An attacker creates a malicious plugin specifically designed to target Semantic Kernel, or compromises a legitimate SK plugin. This plugin could then perform unauthorized actions, steal data processed by SK, or disrupt the SK's operation. The focus is on plugins integrated with and executed by Semantic Kernel.
    • Impact:
      • Data breaches of information handled by SK.
      • System compromise through actions performed by the malicious plugin within the SK context.
      • Denial of service of the SK and potentially the entire application.
      • Reputational damage.
    • Affected Semantic Kernel Component:
      • Kernel.ImportSkill() / Kernel.ImportPlugin() - Functions used to load plugins into SK, representing the entry point for malicious plugins.
      • The entire plugin execution pipeline within SK.
      • ISKFunction and related interfaces - Malicious implementations of these interfaces.
    • Risk Severity: High
    • Mitigation Strategies:
      • Plugin Validation (SK-Specific): Implement strict plugin validation and signing specifically for SK plugins. Verify the origin and integrity of all plugins before loading them into the Kernel.
      • Curated Repository (SK-Specific): Use a curated plugin repository with strong access controls and a rigorous review process, specifically for SK plugins.
      • Least Privilege (SK-Specific): Grant SK plugins only the minimum necessary permissions. Avoid giving plugins broad access to the system or other SK components.
      • Sandboxing (SK-Specific): Run SK plugins in a sandboxed environment with restricted access to system resources and other SK components. This is crucial for isolating malicious plugin behavior.
      • Code Auditing (SK-Specific): Regularly audit the code of SK plugins for vulnerabilities, especially those that interact with external systems or handle sensitive data.
      • Dependency Management (SK-Specific): Carefully manage SK plugin dependencies to avoid introducing vulnerabilities through third-party libraries.

Threat: Data Poisoning of Context/Memory (within SK)

  • Description: If Semantic Kernel uses its IMemoryStore implementations (or custom ones) for context or memory, an attacker could poison these stores with malicious data. This directly impacts the LLM's behavior through Semantic Kernel, leading to incorrect, biased, or harmful outputs.
    • Impact:
      • Incorrect or misleading results generated by SK due to poisoned context.
      • Generation of harmful content by the LLM, facilitated by SK.
      • Reputational damage.
      • Potential for manipulation of application behavior controlled by SK.
    • Affected Semantic Kernel Component:
      • IMemoryStore implementations (e.g., VolatileMemoryStore, QdrantMemoryStore, custom implementations) - The direct target of the poisoning.
      • Any component that uses Kernel.Memory or interacts with the memory store for context within SK.
      • Kernel.InvokeAsync() - Indirectly affected, as it uses the poisoned memory.
    • Risk Severity: High
    • Mitigation Strategies:
      • Data Validation (SK-Specific): Validate and sanitize all data before storing it in Semantic Kernel's IMemoryStore implementations. This is crucial for preventing malicious data from entering the memory system.
      • Access Control (SK-Specific): Implement strict access controls for Semantic Kernel's memory stores. Only authorized SK components and processes should be able to write to the memory.
      • Integrity Checks (SK-Specific): Use checksums or other integrity checks to ensure that data stored in SK's memory has not been tampered with.
      • Auditing (SK-Specific): Regularly audit Semantic Kernel's memory stores for anomalies and suspicious modifications.
      • Memory Store Isolation: If possible, isolate the memory store used by SK from the public internet and other untrusted networks. Consider using separate memory stores for different trust levels.

Threat: Kernel Configuration Tampering

  • Description: An attacker gains access to modify the Semantic Kernel's configuration, specifically targeting settings related to API keys, endpoints, model selection, or plugin configurations within the SK context. This could redirect requests to a malicious LLM, expose API keys used by SK, or alter the behavior of the kernel and its plugins.
    • Impact:
      • Exposure of API keys and other secrets used by SK.
      • Redirection of LLM requests made by SK to malicious services.
      • Unauthorized access to data processed by SK.
      • Disruption of application functionality controlled by SK.
    • Affected Semantic Kernel Component:
      • KernelConfig and related configuration loading mechanisms within SK.
      • Any component that reads or modifies configuration settings within SK.
      • IServiceConfig and related interfaces.
    • Risk Severity: High
    • Mitigation Strategies:
      • Secure Configuration Storage (SK-Specific): Store Semantic Kernel's configuration files securely, using environment variables, a secure configuration service (e.g., Azure Key Vault, AWS Secrets Manager), or encrypted files. Prioritize methods that are tightly integrated with the SK deployment environment.
      • Access Control (SK-Specific): Implement strict access controls for Semantic Kernel's configuration files and settings. Only authorized processes and users should be able to modify them.
      • Auditing (SK-Specific): Log all changes to Semantic Kernel's configuration settings.
      • Regular Monitoring (SK-Specific): Regularly monitor Semantic Kernel's configuration for unauthorized modifications.
      • Principle of Least Privilege (SK-Specific): Run the application and Semantic Kernel with the minimum necessary privileges to access configuration data.