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Mitigation Strategies Analysis for microsoft/semantic-kernel

Mitigation Strategy: Robust Input Sanitization and Validation (Semantic Kernel Specific)

  • Description:

    1. Define Input Schema (SK-Specific): Create a formal schema (JSON Schema, regex, etc.) defining allowed structure, data types, and content for all inputs to the Semantic Kernel, including prompts and specifically plugin parameters. This schema should be tailored to the expected input of the Semantic Kernel functions and plugins.
    2. Implement Validation Logic (Pre-SK): Before any input reaches the Semantic Kernel or its plugins, implement validation logic that checks the input against the defined schema. This is a critical pre-processing step.
    3. Reject Invalid Input (Pre-SK): If input doesn't conform, reject it before it interacts with the Semantic Kernel. Return a clear error, log the rejection.
    4. Enforce Length Limits (Prompt-Specific): Set reasonable maximum length limits for all text-based inputs to the Semantic Kernel, based on the expected use case. Enforce before schema validation.
    5. Escape/Encode Special Characters (LLM-Focused): Use a well-tested library to automatically escape/encode special characters that could be misinterpreted by the LLM as instructions, not data. This is crucial for preventing prompt injection. Do not rely on manual escaping.
    6. Filter Malicious Patterns (Prompt Injection): Maintain a regularly updated list of known prompt injection keywords, phrases, and patterns. Actively filter these from inputs to the Semantic Kernel before schema validation.
    7. Parameterize Prompts (SK Best Practice): Never directly concatenate user input with system prompts or plugin instructions within the Semantic Kernel. Use parameterized prompts or templating within the Semantic Kernel's context to strictly separate user data from core logic.

  • Threats Mitigated:

    • Prompt Injection (Severity: Critical): Prevents attackers from manipulating the LLM's behavior via malicious instructions in inputs to the Semantic Kernel.
    • Denial of Service (DoS) (Severity: Medium): Length limits and filtering of complex inputs help prevent resource exhaustion within the Semantic Kernel.
    • Code Injection (Severity: Critical): If the LLM generates code via the Semantic Kernel, input sanitization helps prevent malicious code injection.

  • Impact:

    • Prompt Injection: Risk reduction: Very High (primary defense).
    • DoS: Risk reduction: Medium (specifically within the SK context).
    • Code Injection: Risk reduction: High (if code generation is used).

  • Currently Implemented:

    • Input validation using regular expressions for basic text fields in the UserInputHandler class (needs to be moved before SK interaction).
    • Length limits on the "question" field in the QuestionForm component (needs to be applied to all SK inputs).
    • Basic escaping of HTML characters in OutputRenderer (insufficient, needs LLM-specific escaping).

  • Missing Implementation:

    • Comprehensive schema validation (JSON Schema) for all Semantic Kernel inputs.
    • Centralized validation layer before Semantic Kernel interaction.
    • Dedicated escaping/encoding library (LLM-focused).
    • Filtering for malicious prompt injection patterns.
    • Consistent use of parameterized prompts within the Semantic Kernel.
    • Plugin input validation specifically within the Semantic Kernel's handling of plugins.

Mitigation Strategy: Strict Output Validation and Sanitization (Semantic Kernel Specific)

  • Description:

    1. Define Output Schema (SK-Function Specific): Define a schema (or rules) describing the expected structure, data types, and content of the LLM's output for each specific Semantic Kernel function or plugin.
    2. Implement Output Validation (Post-SK): Immediately after receiving output from the Semantic Kernel (or a plugin within the SK), validate it against the defined schema. This is a critical post-processing step.
    3. Reject Invalid Output (Post-SK): If output doesn't conform, reject it. Log the rejection, return a default safe value or error (avoiding internal details).
    4. Sanitize for Harmful Content (LLM-Generated): Use a robust sanitization library to remove/neutralize potentially harmful content (HTML, JavaScript, SQL) from the LLM's output, especially if displayed or used in DB queries. Focus on LLM-generated content.
    5. Enforce Output Length Limits (LLM-Specific): Set reasonable maximum length limits for LLM output from the Semantic Kernel to prevent excessively long responses (DoS).
    6. Contextual Validation (SK-Context): Consider the context within the Semantic Kernel where the output will be used. If the output is a number, validate it's a number within an acceptable range as defined by the Semantic Kernel function.

  • Threats Mitigated:

    • Cross-Site Scripting (XSS) (Severity: High): Prevents injecting malicious JavaScript via the LLM's output through the Semantic Kernel.
    • SQL Injection (Severity: High): Prevents SQL injection if LLM output from the Semantic Kernel is used in DB queries.
    • Data Leakage (Severity: Medium): Can help detect/prevent the LLM revealing sensitive info via the Semantic Kernel.
    • Denial of Service (DoS) (Severity: Medium): Output length limits prevent resource exhaustion caused by the Semantic Kernel.
    • Prompt Injection (Indirect - Severity: Medium): Validating output can detect if a prompt injection attack succeeded in altering the LLM's behavior within the Semantic Kernel.

  • Impact:

    • XSS: Risk reduction: Very High (focus on LLM-generated output).
    • SQL Injection: Risk reduction: Very High (focus on LLM-generated output).
    • Data Leakage: Risk reduction: Medium (within the SK context).
    • DoS: Risk reduction: Medium (within the SK context).
    • Prompt Injection (Indirect): Risk reduction: Medium.

  • Currently Implemented:

    • Basic HTML sanitization in OutputRenderer (insufficient, needs to be immediately after SK output and LLM-focused).

  • Missing Implementation:

    • Comprehensive output schema validation for each Semantic Kernel function.
    • Centralized output validation layer immediately after Semantic Kernel output.
    • Robust sanitization library (LLM-focused).
    • Output length limits specifically for Semantic Kernel output.
    • Contextual validation within the Semantic Kernel's context.

Mitigation Strategy: Secure Plugin Management (Semantic Kernel Plugin Context)

  • Description:

    1. Permission Inventory (SK Plugins): Create a detailed inventory of all plugins used by the Semantic Kernel, listing the specific permissions and resources each plugin requires within the Semantic Kernel's context.
    2. Minimize Permissions (SK Plugin Level): Review the inventory and ensure each plugin has only the minimum permissions needed within the Semantic Kernel. Remove unnecessary permissions.
    3. Input/Output Validation (SK Plugin-Specific): Implement strict input and output validation specifically for each plugin used by the Semantic Kernel, treating the plugin as a potential source of untrusted data within the SK. This is in addition to general I/O validation.
    4. Trusted Sources (SK Plugin Acquisition): Only obtain plugins for the Semantic Kernel from reputable and trusted sources. Verify plugin integrity (signatures, checksums).
    5. Regular Updates (SK Plugin Updates): Keep all plugins used by the Semantic Kernel up-to-date to patch vulnerabilities. Automate if possible.
    6. Code Auditing (SK Plugin Focused): If plugin source is available, audit for security issues, focusing on how it interacts with the Semantic Kernel.

  • Threats Mitigated:

    • Privilege Escalation (Severity: High): Prevents a compromised Semantic Kernel plugin from gaining unauthorized access.
    • Remote Code Execution (RCE) (Severity: Critical): Limits a compromised SK plugin from executing arbitrary code.
    • Data Exfiltration (Severity: High): Reduces risk of a compromised SK plugin stealing data.
    • Denial of Service (DoS) (Severity: Medium): Limits a compromised SK plugin from consuming excessive resources.

  • Impact:

    • Privilege Escalation: Risk reduction: Very High (within the SK plugin context).
    • RCE: Risk reduction: High (within the SK plugin context).
    • Data Exfiltration: Risk reduction: High (within the SK plugin context).
    • DoS: Risk reduction: Medium (within the SK plugin context).

  • Currently Implemented:

    • Plugins are loaded from a plugins directory.

  • Missing Implementation:

    • Formal permission inventory for Semantic Kernel plugins.
    • Plugin-specific input/output validation within the Semantic Kernel.
    • Plugin source verification.
    • Automated plugin updates for Semantic Kernel plugins.

Mitigation Strategy: Data Leakage Prevention (Semantic Kernel Context)

  • Description:

    1. Minimize Sensitive Data in Prompts (SK-Specific): Avoid including sensitive data (PII, API keys) in prompts or plugin inputs passed to the Semantic Kernel.
    2. Redaction/Anonymization (Pre-SK): If sensitive data must be included, redact/anonymize it before sending it to the Semantic Kernel.
    3. Monitoring and Alerting (SK Focused):
      • Implement monitoring to detect unusual patterns in Semantic Kernel interactions (long responses, errors, attempts to access restricted resources via the SK).
      • Configure alerts for potential security incidents related to the Semantic Kernel.

  • Threats Mitigated:

    • Data Leakage (Severity: High): Prevents the LLM from revealing sensitive information through the Semantic Kernel.
    • Compliance Violations (Severity: High): Helps ensure compliance with data privacy regulations (related to data processed by the Semantic Kernel).

  • Impact:

    • Data Leakage: Risk reduction: High (within the SK context).
    • Compliance Violations: Risk reduction: High (within the SK context).

  • Currently Implemented:

    • Basic logging of prompts and responses in KernelService (needs to avoid logging sensitive data).

  • Missing Implementation:

    • Redaction/anonymization of sensitive data before Semantic Kernel interaction.
    • Comprehensive monitoring and alerting focused on the Semantic Kernel.

Mitigation Strategy: Denial of Service (DoS) Protection (Semantic Kernel Specific)

  • Description:

    1. Rate Limiting (SK API Calls): Implement rate limiting on API calls to the Semantic Kernel to prevent overwhelming the system.
    2. Resource Quotas (SK Resources): Limit resources (CPU, memory, processing time) consumed by the Semantic Kernel and its plugins.
    3. Input Validation (DoS-Specific, SK-Focused): Enforce strict input length limits and reject complex inputs to the Semantic Kernel.
    4. Timeout (SK Requests): Set reasonable timeout for the requests to the LLM via Semantic Kernel.

  • Threats Mitigated:

    • Denial of Service (DoS) (Severity: Medium): Prevents attackers from making the Semantic Kernel unavailable.
    • Resource Exhaustion (Severity: Medium): Protects against attacks consuming excessive resources via the Semantic Kernel.

  • Impact:

    • DoS: Risk reduction: High (within the SK context).
    • Resource Exhaustion: Risk reduction: High (within the SK context).

  • Currently Implemented:

    • None

  • Missing Implementation:

    • Rate limiting for Semantic Kernel API calls.
    • Resource quotas for the Semantic Kernel.
    • Input validation (DoS-specific) for Semantic Kernel inputs.
    • Timeouts for Semantic Kernel requests.