The Semantic Kernel project aims to democratize access to Large Language Models (LLMs) by providing a developer-friendly SDK for building intelligent applications. It simplifies the integration of LLMs from various providers (OpenAI, Azure OpenAI, Hugging Face, etc.) into existing software systems. The SDK offers a framework for defining and orchestrating both "semantic functions" (powered by LLMs and natural language prompts) and "native functions" (traditional code). This abstraction allows developers to focus on application logic rather than the complexities of interacting directly with diverse AI service APIs. The core objective is to accelerate the development of AI-enhanced applications by providing a reusable, extensible, and secure foundation.
This design document is intended for a broad technical audience involved in the security and development lifecycle of applications using Semantic Kernel:
- Security Architects and Engineers: To perform threat modeling, security assessments, and define security requirements for Semantic Kernel-based applications.
- Development Team (Software Engineers, AI/ML Engineers): To understand the architecture, components, and security implications of using Semantic Kernel for building applications.
- DevOps and Infrastructure Engineers: To understand deployment considerations and operational security aspects of Semantic Kernel deployments.
- Product Owners and Stakeholders: To gain a high-level understanding of the system, its functionalities, and inherent security considerations.
- Compliance and Risk Management Teams: To assess the security posture and compliance aspects of applications built with Semantic Kernel.
This document provides a detailed design overview of the Semantic Kernel project, specifically tailored for threat modeling and security analysis. It encompasses:
- Detailed System Architecture: In-depth breakdown of components, modules, and their interactions, emphasizing security boundaries and data flow.
- Comprehensive Data Flow Diagrams: Visual representation of data movement, highlighting sensitive data paths and external service interactions.
- Technology Stack Breakdown: Listing of technologies used, relevant for identifying technology-specific vulnerabilities.
- In-depth Component Analysis: Detailed description of each key component, its functionality, and associated security considerations.
- Initial Threat Landscape Identification: Proactive identification of potential threats and vulnerabilities based on the design, serving as a starting point for formal threat modeling exercises.
- Deployment Considerations (Security Focused): Brief overview of security aspects related to deploying Semantic Kernel applications.
This document is based on publicly available information from the Semantic Kernel GitHub repository and aims to represent a generalized, security-focused architecture. Specific implementations and configurations may introduce variations.
The Semantic Kernel operates as an intermediary layer between applications and AI services, providing orchestration and abstraction. The "Kernel" is the central control point, managing plugins, connectors, memory, and planning capabilities.
graph LR
subgraph "Application Environment"
A["Application Code"]
end
subgraph "Semantic Kernel SDK"
K["Kernel Core"]
P["Plugins (Native & Semantic)"]
C["Connectors (AI Service & Generic)"]
M["Memory & Data Storage"]
PL["Planner & Orchestration"]
end
subgraph "External AI & Data Services"
AIS["AI Services (e.g., OpenAI, Azure OpenAI, Hugging Face)"]
DS["Data Storage Services (Vector DBs, Document Stores)"]
end
A --> K
K --> P
K --> C
K --> M
K --> PL
P --> K
C --> AIS
AIS --> C
C --> DS
DS --> C
M --> K
PL --> K
classDef box stroke:#333,stroke-width:2px,fill:#fff,color:#333
class "Application Environment","Semantic Kernel SDK","External AI & Data Services" box
Description:
- Application Environment: The environment where the user's application code resides and executes. This could be a web server, desktop application, mobile app, or any other software context.
- Semantic Kernel SDK: The core library integrated into the application.
- Kernel Core: The central engine responsible for request processing, plugin management, connector selection, memory access, and plan execution. It acts as the primary security boundary within the SDK.
- Plugins (Native & Semantic): Modular units of functionality.
- Native Plugins: Compiled code (e.g., C#, Python functions) offering deterministic operations. Security depends on the plugin code quality and access control.
- Semantic Plugins: Defined by natural language prompts and configurations, leveraging LLMs for AI-driven tasks. Security risks include prompt injection and unpredictable LLM behavior.
- Connectors (AI Service & Generic): Adapters for interacting with external services.
- AI Service Connectors: Specific connectors for each AI provider (OpenAI, Azure OpenAI, Hugging Face). Security focuses on API key management, secure communication, and input/output validation.
- Generic Connectors: Allow integration with other services via REST APIs, databases, or other protocols. Security depends on the target service and connector implementation.
- Memory & Data Storage: Provides mechanisms for data persistence and retrieval. This can range from in-memory storage to integrations with external databases (vector databases, document stores). Security considerations include data encryption, access control, and data integrity.
- Planner & Orchestration: An optional module for automated task planning and execution. Security risks involve plan generation logic vulnerabilities and unintended execution sequences.
- External AI & Data Services: External services consumed by Semantic Kernel.
- AI Services: LLM providers like OpenAI, Azure OpenAI, Hugging Face. Security is primarily focused on secure communication and API key protection on the Kernel side.
- Data Storage Services: External databases used for memory persistence (vector databases, document stores). Security depends on the chosen service's security posture and the connector's secure interaction.
Drilling down into the "Kernel Core" component to highlight internal modules and security-relevant interactions:
graph LR
subgraph "Kernel Core"
RAH["Request Authentication & Authorization Handler"]
RIV["Request Input Validation"]
RE["Request Execution Engine"]
PM["Plugin Management Module"]
CM["Connector Management Module"]
MM["Memory Management Module"]
PLM["Planner Management Module"]
ROF["Response Output Formatting"]
EL["Error & Exception Logging"]
AM["Audit & Monitoring"]
end
subgraph "Plugins"
NP["Native Plugins"]
SP["Semantic Plugins"]
end
subgraph "Connectors"
ASC["AI Service Connectors"]
GC["Generic Connectors"]
end
subgraph "Memory Implementations"
LM["Local Memory (In-Process)"]
EM["External Memory (Databases, Services)"]
end
RAH --> RIV
RIV --> RE
RE --> PM
RE --> CM
RE --> MM
RE --> PLM
PM --> NP
PM --> SP
CM --> ASC
CM --> GC
MM --> LM
MM --> EM
PLM --> RE
RE --> ROF
ROF --> RAH
EL --> AM
AM --> RAH
classDef box stroke:#333,stroke-width:2px,fill:#fff,color:#333
class "Kernel Core",Plugins,Connectors,"Memory Implementations" box
Description of Kernel Core Modules (Security Perspective):
-
Request Authentication & Authorization Handler (RAH):
- Function: Responsible for verifying the identity of the request originator and enforcing access control policies. Determines if the request is allowed to be processed by the Kernel.
- Security Relevance: Crucial for preventing unauthorized access and ensuring only legitimate requests are processed. May involve API key validation, OAuth, or other authentication mechanisms. Authorization policies define what actions are permitted for authenticated users/applications.
-
Request Input Validation (RIV):
- Function: Validates all incoming requests to ensure they conform to expected formats and data types. Sanitizes inputs to prevent injection attacks (e.g., command injection, SQL injection if interacting with databases via generic connectors).
- Security Relevance: First line of defense against many common web application vulnerabilities. Prevents malicious or malformed data from reaching internal components and causing harm.
-
Request Execution Engine (RE):
- Function: The core processing unit that orchestrates the execution of requests. Routes requests to Plugin Manager, Connector Manager, Memory Manager, and Planner Manager as needed. Manages the overall workflow of request processing.
- Security Relevance: Central point for enforcing security policies during request execution. Responsible for secure session management, resource management, and preventing race conditions or other execution-related vulnerabilities.
-
Plugin Management Module (PM):
- Function: Manages the lifecycle of plugins (registration, loading, execution, unloading). Provides an interface for the RE to discover and invoke plugins.
- Security Relevance: Ensures only trusted and authorized plugins are loaded and executed. Plugin isolation and sandboxing (if implemented) are managed here. Vulnerability scanning of plugins and secure plugin update mechanisms are important.
-
Connector Management Module (CM):
- Function: Manages available connectors and selects the appropriate connector based on the target service and configuration. Handles API key storage and retrieval, connection pooling, and potentially request routing/load balancing across connectors.
- Security Relevance: Critical for secure interaction with external services. Secure API key management (secrets management), secure communication protocols (HTTPS), and input/output validation with external services are key security concerns.
-
Memory Management Module (MM):
- Function: Provides an abstraction layer for interacting with different memory implementations. Manages data storage, retrieval, and potentially caching.
- Security Relevance: Responsible for data security at rest and in transit within the memory layer. Encryption of sensitive data, access control to memory stores, and data sanitization before storage are crucial security aspects.
-
Planner Management Module (PLM):
- Function: Orchestrates the planning process, if enabled. Uses available plugins and user goals to generate execution plans.
- Security Relevance: Ensures the planner generates secure and valid execution plans. Prevents the planner from creating plans that could lead to unintended or malicious actions. Plan validation and review mechanisms might be necessary.
-
Response Output Formatting (ROF):
- Function: Formats the response from the request execution engine into a suitable format for the application. May involve data serialization, error handling, and response sanitization.
- Security Relevance: Prevents information leakage through responses. Sanitizes output to remove sensitive data or error details that should not be exposed to the application or end-user.
-
Error & Exception Logging (EL):
- Function: Handles errors and exceptions that occur during request processing. Logs errors for debugging and monitoring purposes.
- Security Relevance: Proper error logging is essential for security monitoring and incident response. However, logs should be carefully managed to avoid logging sensitive data. Secure log storage and access control are important.
-
Audit & Monitoring (AM):
- Function: Monitors system activity, collects audit logs, and potentially triggers alerts based on security events.
- Security Relevance: Provides visibility into system behavior and security events. Enables detection of security breaches, policy violations, and performance issues. Secure storage and analysis of audit logs are crucial.
This diagram provides a more detailed view of data flow, incorporating security modules and highlighting sensitive data paths.
graph LR
subgraph "Application"
UI["User Input"]
AR["Application Request"]
RR["Response to Application"]
end
subgraph "Semantic Kernel"
RAH["Request Auth & Authz"]
RIV["Request Input Validation"]
KRH["Kernel Request Handler"]
PMF["Plugin Manager Function Invocation"]
CF["Connector Function Call"]
MR["Memory Read/Write"]
PR["Planner Execution"]
KF["Kernel Function Result"]
ROF["Response Output Formatting"]
end
subgraph "AI Services"
AISR["AI Service Request"]
AISRSP["AI Service Response"]
end
subgraph "Memory"
MD["Memory Data (Potentially Sensitive)"]
end
subgraph "Audit Logs"
AL["Audit Logs (Security Events)"]
end
UI --> AR
AR --> RAH
RAH --> RIV
RIV --> KRH
KRH --> PMF
PMF --> NP["Native Plugin Execution"]
PMF --> SP["Semantic Plugin Execution"]
SP --> CF
CF --> AISR
AISR --> AIS["AI Service"]
AIS["AI Service"] --> AISRSP
AISRSP --> CF
CF --> SP
NP --> KF
SP --> KF
KRH --> MR
MR --> MD
MD --> MR
KRH --> PR
PR --> PMF
KF --> ROF
ROF --> RR
RR --> UI
RIV --> EL["Error Logging"]
KRH --> EL
PMF --> EL
CF --> EL
MR --> EL
PR --> EL
RAH --> AM["Audit Monitoring"]
RIV --> AM
KRH --> AM
PMF --> AM
CF --> AM
MR --> AM
PR --> AM
EL --> AL
ROF --> RIV "Response Sanitization (Loop)"
style NP fill:#ccf,stroke:#333,stroke-width:1px
style SP fill:#ccf,stroke:#333,stroke-width:1px
style MD fill:#eee,stroke:#333,stroke-width:1px
style AL fill:#fee,stroke:#333,stroke-width:1px
linkStyle 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38 stroke:#333,stroke-width:1px;
Data Flow Description (Security Enhanced):
- User Input & Application Request: Same as before.
- Request Authentication & Authorization (RAH): Incoming request is first authenticated and authorized before further processing.
- Request Input Validation (RIV): Validates and sanitizes the request to prevent injection attacks.
- Kernel Request Handler (KRH): Core request processing logic, as described before.
- Plugin Manager Function Invocation (PMF), Native Plugin Execution (NP), Semantic Plugin Execution (SP), Connector Function Call (CF), AI Service Interaction (AIS), Memory Read/Write (MR), Planner Execution (PR), Kernel Function Result (KF): Functionality remains similar to the previous description, but now operating within the security context established by RAH and RIV.
- Response Output Formatting (ROF): Formats the response and importantly, sanitizes the output to prevent information leakage before returning it to the application. There's a feedback loop to RIV conceptually, as output sanitization can be considered a form of validation.
- Response to Application & User Input: Same as before.
- Error Logging (EL): Errors are logged from various stages of request processing for debugging and security monitoring.
- Audit & Monitoring (AM): Security-relevant events from Authentication, Input Validation, Request Handling, Plugin/Connector/Memory interactions, and Error Logging are sent to the Audit & Monitoring module.
- Audit Logs (AL): Audit logs are stored securely for security analysis and incident response.
- Programming Languages:
- C# (.NET): Core SDK. Security considerations include .NET framework vulnerabilities, secure coding practices in C#.
- Python: Python SDK. Python dependency management, vulnerabilities in Python libraries, and secure Python coding practices are relevant.
- Java/JavaScript (if applicable): Security considerations specific to Java/JavaScript ecosystems.
- AI Service Connectors:
- OpenAI/Azure OpenAI, Hugging Face, etc.: Connectors rely on HTTPS for secure communication. API key management is critical. Vulnerabilities in connector libraries themselves are a concern.
- Memory Implementations:
- Volatile Memory: Least secure for sensitive data. Data in memory is vulnerable to memory dumps if the application is compromised.
- Vector Databases (FAISS, Pinecone, Weaviate, etc.): Security depends on the chosen database's security features (encryption at rest, access control, network security). Connector security is also important.
- Document Databases/Search Services (Azure Cognitive Search, Elasticsearch, etc.): Similar security considerations as Vector Databases.
- Serialization and Data Formats:
- JSON: Commonly used. Vulnerabilities related to JSON parsing (e.g., JSON injection) should be considered, although less likely in typical SDK usage.
- Protocol Buffers/gRPC (potentially for internal communication): Security of gRPC channels (TLS), and vulnerabilities in protocol buffer implementations.
- Networking:
- HTTPS (TLS): Mandatory for communication with external AI services and recommended for internal communication if components are distributed. TLS configuration and certificate management are important.
- Build and Deployment:
- Dependency Management Tools (.NET NuGet, Python pip, etc.): Vulnerability scanning of dependencies is crucial. Secure software supply chain practices are important.
- Containerization (Docker, etc.): If deployed in containers, container security best practices should be followed.
- Cloud Deployment Platforms (Azure, AWS, GCP): Leverage cloud provider security features (firewalls, IAM, security monitoring).
-
5.1. Kernel Core (Security Control Point):
- Authentication & Authorization: How is the Kernel authenticated? Is authorization granular? What authentication mechanisms are supported? (API Keys, OAuth, etc.)
- Input Validation: What input validation mechanisms are in place? Are all input channels validated (API requests, configuration files, plugin inputs)? Is input sanitization performed?
- Error Handling & Logging: Are errors handled gracefully? Is sensitive information leaked in error messages or logs? Are logs securely stored and accessed?
- Session Management: If stateful interactions are supported, how are sessions managed securely? Are session tokens protected?
- Resource Management: Are resource limits enforced to prevent denial-of-service? (e.g., rate limiting, request timeouts, memory limits).
-
5.2. Plugins (Native & Semantic - Vulnerability Surface):
- Native Plugin Security: Code review and security testing of Native Plugins are essential. Are plugins developed using secure coding practices? Are there mechanisms to isolate plugins or limit their access to system resources?
- Semantic Plugin Prompt Injection: This is a major vulnerability. How are Semantic Plugins protected against prompt injection attacks? Are there input sanitization or output filtering mechanisms? Can prompt templates be controlled and secured?
- Plugin Isolation & Sandboxing: Are plugins isolated from each other and the Kernel? Is sandboxing used to limit plugin capabilities and prevent them from accessing sensitive resources or performing malicious actions?
- Plugin Provenance & Trust: How is the trustworthiness of plugins established? Is there a plugin registry or marketplace with security vetting? How are plugin updates managed securely?
-
5.3. Connectors (External Service Gateway):
- API Key Management: How are API keys for AI services and other external services managed? Are they stored securely (e.g., using secrets management solutions)? Are they rotated regularly? Avoid hardcoding API keys.
- Secure Communication: Is HTTPS enforced for all communication with external services? Is TLS configuration secure? Are certificates validated properly?
- Input/Output Validation with External Services: Validate data sent to and received from external services. Prevent injection attacks or data corruption due to malicious responses. Handle API errors and timeouts gracefully.
- Rate Limiting & Throttling (Connector Level): Implement rate limiting at the connector level to protect against abuse and prevent overwhelming external services.
-
5.4. Memory (Data Protection):
- Data Encryption at Rest & in Transit: Is sensitive data encrypted when stored in memory (especially persistent memory)? Is data encrypted in transit between the Kernel and external memory stores?
- Access Control to Memory: Are access control mechanisms in place to restrict who can read and write data to memory? Are different levels of access control supported?
- Data Sanitization & Validation (Memory Input): Sanitize and validate data before storing it in memory to prevent injection attacks or data corruption within the memory store itself.
- Data Retention & Disposal: Are data retention policies defined? Is there a secure mechanism for data disposal when it's no longer needed?
-
5.5. Planner (Orchestration Security):
- Plan Generation Logic Security: Is the planner logic secure and resistant to manipulation? Can an attacker influence the planner to generate malicious plans?
- Plan Validation & Review: Are generated plans validated before execution? Is there a mechanism to review and approve plans, especially for sensitive operations?
- Resource Consumption (Planner): Monitor planner resource consumption to prevent denial-of-service due to complex or inefficient planning processes.
Based on the detailed design, the initial threat landscape for Semantic Kernel includes:
- Prompt Injection Attacks (Semantic Plugins): High risk. Malicious prompts can manipulate LLMs to perform unintended actions, bypass security controls, or leak sensitive information. Mitigation requires robust input validation, output filtering, and potentially prompt engineering best practices.
- Native Plugin Vulnerabilities: Medium to High risk (depending on plugin complexity and code quality). Vulnerabilities in Native Plugin code (e.g., buffer overflows, injection flaws) can be exploited to compromise the Kernel or underlying system. Secure coding practices, code reviews, and security testing are essential.
- API Key Compromise (Connectors): High risk. If API keys for AI services are compromised, attackers can abuse AI services under the application's identity, leading to financial loss, data breaches, or service disruption. Secure API key management is critical.
- Insecure Communication (Connectors): Medium risk. If communication with AI services or external memory stores is not properly secured (e.g., using HTTPS), data in transit can be intercepted or tampered with. Enforce HTTPS and proper TLS configuration.
- Data Breaches (Memory): Medium to High risk (depending on data sensitivity and memory implementation). If sensitive data is stored in memory without proper encryption and access control, it can be exposed in case of a security breach. Implement data encryption at rest and in transit, and enforce strict access control.
- Denial of Service (DoS): Medium risk. Attackers could attempt to overload the Kernel, AI services, or memory stores, leading to service disruption. Implement rate limiting, resource quotas, and input validation to mitigate DoS risks.
- Unauthorized Access (Kernel & Plugins): Medium risk. If access control is not properly implemented, unauthorized users or applications could interact with the Kernel or execute plugins, potentially leading to data breaches or system compromise. Implement robust authentication and authorization mechanisms.
- Dependency Vulnerabilities: Medium risk. Vulnerabilities in third-party libraries used by the Kernel, Plugins, or Connectors could be exploited. Regular dependency scanning and updates are necessary.
- Information Leakage (Error Messages & Logs): Low to Medium risk. Overly verbose error messages or insecurely stored logs could leak sensitive information to attackers. Sanitize error messages and secure log storage.
- Planner Logic Manipulation: Low to Medium risk (depending on planner complexity and sensitivity of planned actions). If the planner logic is vulnerable, attackers might be able to influence plan generation to achieve malicious goals. Plan validation and review mechanisms can mitigate this.
This improved design document provides a more comprehensive and security-focused foundation for conducting a thorough threat modeling exercise on the Semantic Kernel project. It highlights key components, data flows, and security considerations, enabling security teams to identify specific threats, assess risks, and define appropriate mitigation strategies.