feat(go): add MCP security, execution rings, and lifecycle management to Go SDK

[imran-siddique]

Branch: feat/sdk-parity-go (1 commits ahead of main)

Commits

e18a1b5 feat(go): add MCP security, execution rings, and lifecycle management to Go SDK

[github-actions]

🤖 AI Agent: security-scanner — Security Scan Findings for Pull Request: feat(go): add MCP security, execution rings, and lifecycle management to Go SDK

Security Scan Findings for Pull Request: feat(go): add MCP security, execution rings, and lifecycle management to Go SDK

1. Prompt Injection Defense Bypass

   • Finding: The detectToolPoisoning method in mcp.go checks for specific phrases that could indicate prompt injection. However, the detection relies on a hardcoded list of phrases. An attacker could craft descriptions that do not match these phrases but still contain prompt injection instructions.
   • Rating: 🔴 CRITICAL
   • Suggestion: Implement a more robust natural language processing (NLP) model to detect prompt injection patterns, rather than relying solely on hardcoded phrases. Consider using machine learning techniques to identify suspicious patterns in tool descriptions.

2. Policy Engine Circumvention

   • Finding: The McpSecurityScanner does not enforce strict validation on the tool definitions before scanning. If an attacker can manipulate the input to the scanner, they might bypass checks.
   • Rating: 🟠 HIGH
   • Suggestion: Validate the structure and content of McpToolDefinition before processing it in the scanner. Ensure that only well-formed definitions are accepted.

3. Trust Chain Weaknesses

   • Finding: The implementation does not appear to validate the source of the tool definitions. If a malicious actor can provide a tool definition, they could potentially inject harmful content.
   • Rating: 🟠 HIGH
   • Suggestion: Implement a trust mechanism for tool definitions, such as requiring signatures or certificates to verify the authenticity of the source.

4. Credential Exposure

   • Finding: The code does not explicitly log sensitive information, but the fmt.Printf statements in the examples could inadvertently expose sensitive data if used in production.
   • Rating: 🟡 MEDIUM
   • Suggestion: Replace fmt.Printf with a logging framework that can handle sensitive data appropriately, ensuring that sensitive information is not logged.

5. Sandbox Escape

   • Finding: The execution rings model does not specify how it isolates different privilege levels. If the implementation allows for privilege escalation, it could lead to sandbox escape.
   • Rating: 🟠 HIGH
   • Suggestion: Clearly define and enforce the boundaries between execution rings. Implement strict checks to prevent privilege escalation and ensure that actions are logged and monitored.

6. Deserialization Attacks

   • Finding: The code does not show any deserialization of untrusted data, but if future changes introduce such functionality, it could be vulnerable to attacks.
   • Rating: 🟡 MEDIUM
   • Suggestion: If deserialization is introduced, use safe libraries and validate all incoming data against a strict schema.

7. Race Conditions

   • Finding: The LifecycleManager uses a mutex for state transitions, which is good. However, if there are multiple instances or threads accessing the same LifecycleManager, it could lead to race conditions.
   • Rating: 🟡 MEDIUM
   • Suggestion: Ensure that all access to shared resources is properly synchronized and consider using atomic operations where applicable.

8. Supply Chain Issues

   • Finding: The detectTyposquatting method relies on a hardcoded list of known tools. If an attacker can introduce a similar tool name, it could lead to confusion or exploitation.
   • Rating: 🟡 MEDIUM
   • Suggestion: Regularly update the list of known tools and consider implementing a more dynamic approach to detect typosquatting, such as querying a trusted source for known tool names.

Summary

The pull request introduces significant new features that enhance the security capabilities of the AgentMesh Go SDK. However, several critical and high-risk vulnerabilities have been identified that could compromise the integrity of the security framework. Immediate attention is required to address these findings to ensure the toolkit remains a reliable security layer for downstream users.

[github-actions]

🤖 AI Agent: code-reviewer

Review Summary

This pull request introduces significant new features to the Go SDK of the AgentMesh governance framework, including MCP security scanning, execution privilege rings, and agent lifecycle management. The implementation appears well-structured and includes comprehensive test coverage. However, there are several areas where improvements can be made, particularly around security, concurrency, and type safety.

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🔴 CRITICAL: Security Issues

1. Lack of Input Validation in LifecycleManager

   • The NewLifecycleManager function does not validate the agentID input. This could allow invalid or malicious agentID values to be used, potentially leading to security vulnerabilities or undefined behavior.
   • Recommendation: Add validation for agentID to ensure it adheres to expected formats (e.g., alphanumeric, length constraints).

2. Potential for Privilege Escalation in RingEnforcer

   • The SetRingPermissions method in the RingEnforcer class is not included in the diff, but if it allows arbitrary modification of permissions without proper authorization checks, it could lead to privilege escalation.
   • Recommendation: Ensure that SetRingPermissions and Assign methods have proper authorization checks to prevent unauthorized privilege escalation.

3. Insufficient Logging for Security Events

   • The LifecycleManager and McpSecurityScanner implementations lack logging for critical security-related events, such as state transitions or detected threats.
   • Recommendation: Add structured logging for all state transitions and detected threats, including metadata like agentID, initiatedBy, and timestamps.

4. Homoglyph Detection in McpSecurityScanner

   • The looksLikeLatinHomoglyph function uses a hardcoded list of homoglyphs. This approach is incomplete and may miss other potential homoglyphs.
   • Recommendation: Use a comprehensive library or dataset for homoglyph detection to ensure better coverage.

5. Risk of Denial of Service in levenshteinDistance

   • The levenshteinDistance function has a time complexity of O(n * m), where n and m are the lengths of the input strings. This could be exploited to cause a denial-of-service attack by providing extremely long strings.
   • Recommendation: Add a maximum length check for tool names to prevent excessive computation.

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🟡 WARNING: Potential Breaking Changes

1. Public API Changes
   • The addition of new features like McpSecurityScanner, RingEnforcer, and LifecycleManager introduces new public APIs. While these are new features and do not modify existing APIs, they should be clearly documented as part of the release notes to inform users of the new capabilities.

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💡 Suggestions for Improvement

1. Concurrency Safety in LifecycleManager

   • The LifecycleManager uses a sync.RWMutex for thread safety, which is good. However, the events slice is directly appended to without any checks for capacity. This could lead to performance degradation due to frequent slice resizing.
   • Recommendation: Pre-allocate a reasonable capacity for the events slice to reduce the overhead of resizing.

2. Enhanced Test Coverage

   • While the test coverage is generally good, consider adding tests for edge cases, such as:
     • Invalid agentID inputs in NewLifecycleManager.
     • Concurrent state transitions in LifecycleManager to ensure thread safety.
     • Edge cases for levenshteinDistance, such as empty strings or very long strings.
     • Tools with extremely large descriptions to test the performance and correctness of detectRugPull.

3. Error Handling in LifecycleManager.Transition

   • The error message in Transition could be more descriptive. For example, instead of "invalid transition from %s to %s", include the agentID and reason in the error message for better debugging.
   • Recommendation: Update the error message to include more context.

4. Documentation

   • The README provides a good overview of the new features, but it would benefit from more detailed examples, especially for complex features like McpSecurityScanner and RingEnforcer.
   • Recommendation: Add detailed usage examples and edge case scenarios to the documentation.

5. Performance Optimization for McpSecurityScanner

   • The detectTyposquatting function compares the tool name against a list of known tools using a linear search. This could become a bottleneck as the list grows.
   • Recommendation: Use a more efficient data structure, such as a trie or a hash map, for storing and searching known tool names.

6. Consider Using a Configurable Threat Model

   • The threat detection logic in McpSecurityScanner is hardcoded, which limits flexibility and adaptability to new threat patterns.
   • Recommendation: Allow users to configure the threat detection rules (e.g., via a JSON or YAML file) to make the scanner more adaptable to evolving threats.

7. Code Comments and Documentation

   • While the code is generally well-commented, some functions (e.g., computeRiskScore) could benefit from additional comments explaining the rationale behind specific implementation choices.
   • Recommendation: Add comments to clarify the logic and assumptions in critical functions.

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Summary of Feedback

• 🔴 CRITICAL: Address input validation, privilege escalation, logging, homoglyph detection, and potential DoS vulnerabilities.
• 🟡 WARNING: Document new public APIs to ensure backward compatibility.
• 💡 SUGGESTION: Improve concurrency safety, test coverage, error handling, documentation, and performance.

Let me know if you need further clarification or assistance!