agentic-swarm

v0.2.0 suspicious
6.0
Medium Risk

Plug-and-play SDK for building immortal, self-healing multi-agent AI systems

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential misuse of AWS credentials and concerns over the maintainer's activity level and repository engagement.

  • credential risk due to access of AWS credentials
  • low engagement and inactive status of the maintainer
Per-check LLM notes
  • Network: The network call pattern indicates legitimate HTTP requests, possibly for API interaction, but requires further investigation into the base URL to confirm legitimacy.
  • Shell: No shell execution patterns detected, suggesting low risk of direct system command execution.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The code is accessing AWS credentials through environment variables which could be a legitimate practice but also poses a risk if not handled securely.
  • Metadata: The repository's low engagement and the maintainer's new/inactive status raise concerns, but there's no clear evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • self._client = httpx.AsyncClient(base_url=self.base_url, timeout=120.0) except Im
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • self.region = region or os.getenv("AWS_REGION", "us-east-1") self.aws_access_key_id = aws_a
  • key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID") self.aws_secret_access_key = aws_sec
  • ey = aws_secret_access_key or os.getenv("AWS_SECRET_ACCESS_KEY") self._model_info = MODEL_INFO.ge
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 agentic-swarm 
Create a fully-functional mini-application named 'SwarmGuard' that leverages the 'agentic-swarm' package to monitor and secure a network of IoT devices. SwarmGuard will utilize the package's capabilities to build an autonomous, self-healing system capable of identifying potential security threats and responding in real-time.

Step 1: Define the core functionalities of SwarmGuard. It should include:
- Real-time monitoring of network traffic for suspicious activities
- Automatic isolation of compromised devices from the network
- Self-healing mechanisms to recover compromised devices without human intervention
- Reporting of incidents and recommendations for preventive measures

Step 2: Set up the environment for developing SwarmGuard using Python. Ensure that the 'agentic-swarm' package is installed and properly configured.

Step 3: Design and implement the agent architecture for SwarmGuard. Each agent should specialize in different tasks such as traffic analysis, threat detection, and response actions.

Step 4: Implement the communication protocol between agents within the swarm. This should allow seamless sharing of information and coordination of actions across the network.

Step 5: Develop a user interface (UI) for administrators to interact with SwarmGuard. The UI should display real-time status updates, incident reports, and allow configuration changes.

Step 6: Test the functionality of SwarmGuard under various simulated attack scenarios. Validate that it can accurately detect and respond to threats while maintaining operational integrity.

Suggested Features:
- Integration with existing SIEM tools for centralized management
- Support for machine learning models to enhance threat detection accuracy
- Ability to scale the swarm based on network size and complexity
- Detailed logging and auditing capabilities for compliance and forensic analysis