aim-sdk

v1.23.0 suspicious
5.0
Medium Risk

Python SDK for AIM (Agent Identity Management) - Automatic identity verification for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to potential credential handling issues and the uncertainty around the developer's activity level.

  • credential risk due to use of keyring.get_password
  • author has only one package listed
Per-check LLM notes
  • Network: The observed network calls seem to be related to authentication and session management, which is typical for SDKs interacting with remote services.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: Base64 decoding is commonly used for data serialization and encryption, suggesting legitimate use rather than malicious obfuscation.
  • Credentials: The use of 'keyring.get_password' indicates potential storage and retrieval of sensitive information, which could be a risk if not properly secured.
  • Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

  • 24 test file(s) detected (e.g. test_a2a.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://opena2a.org/docs/aim
  • Detailed PyPI description (10129 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 237 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in opena2a-org/agent-identity-management
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • meout self._session = requests.Session() self._session.headers.update({ "Conten
  • oken}" resp = requests.get(url, headers=headers, timeout=10) if resp.st
  • try: response = requests.post( token_url, json={ '
  • resh" response = requests.post( refresh_url, json={"refresh
  • recovery_response = requests.post( recovery_url,
  • ented) response = requests.post( f"{aim_url.rstrip('/')}/api/v1/auth/revoke"
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • e") encrypted_blob = base64.b64decode(encrypted_blob_b64) ephemeral_pub_bytes = base64.b64
  • ephemeral_pub_bytes = base64.b64decode(ephemeral_pub_b64) # Decrypt using X25519 ECDH + Ch
  • n) ed25519_pk_bytes = base64.b64decode(self._client.public_key) _agent_x25519_pub = crypto_
  • tr) signature_bytes = base64.b64decode(signature) assert len(signature_bytes) == 64 # Ed25
  • fy(canonical.encode("utf-8"), base64.b64decode(sig_header)) def test_verify_action_poll_fails_without_
  • fy(canonical.encode("utf-8"), base64.b64decode(sig_header)) class TestLogActionResult: """Test action
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • rom keyring key = keyring.get_password(self.SERVICE_NAME, self.KEY_NAME) if not key:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: opena2a.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository opena2a-org/agent-identity-management appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "OpenA2A" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aim-sdk 
Develop a mini-application called 'AgentIDVerifier' using the Python package 'aim-sdk'. This application will serve as a user-friendly interface for verifying the identities of AI agents. Here's a step-by-step guide on what your application should achieve:

1. **User Interface**: Create a simple command-line interface (CLI) where users can input details about an AI agent they wish to verify.
2. **Verification Process**: Use the 'aim-sdk' package to interact with the Agent Identity Management system. Implement functions to submit the AI agent's data for verification and retrieve results.
3. **Data Handling**: Ensure the application securely handles sensitive information such as agent credentials or personal data, following best practices for data protection.
4. **Result Display**: Once verification is complete, display the results to the user in a clear and concise manner. Include any additional information provided by the AIM system, such as confidence scores or verification status.
5. **Error Handling**: Implement robust error handling to manage various scenarios, including network issues, incorrect input data, or failed verifications.
6. **Logging**: Integrate logging functionality to record actions taken within the application and any errors encountered during the verification process.
7. **Testing**: Write unit tests to validate the functionality of each component of the application.
8. **Documentation**: Provide comprehensive documentation for users and developers, explaining how to install the application, use its CLI, and troubleshoot common issues.

**Suggested Features**:
- Support for multiple verification methods offered by the AIM system.
- Ability to save verified agent profiles locally for future reference.
- Integration with popular logging frameworks like Loguru or Structlog.
- Option to configure the application through a settings file.

This project aims to demonstrate the practical application of the 'aim-sdk' package while providing a useful tool for managing AI agent identities.