agenticaiframework

v3.0.8 suspicious
7.0
High Risk

AgenticAI - Enterprise-grade Python SDK with advanced orchestration, monitoring, and production-ready AI agent capabilities.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows multiple signs of potential misuse, particularly in shell execution and credential handling, raising concerns about its safety.

  • High shell risk indicating potential for executing arbitrary commands
  • Significant credential risk due to extraction of AWS credentials
Per-check LLM notes
  • Network: The network call patterns indicate the package is likely designed to connect to external servers, which could be for legitimate purposes like API calls but requires further investigation.
  • Shell: The shell execution patterns suggest the package may execute commands on the host system, which can pose significant risks if not properly controlled and could potentially be used for malicious activities.
  • Obfuscation: The code uses base64 decoding which could be part of legitimate cryptographic operations but also may hide malicious content.
  • Credentials: The code extracts AWS credentials from environment variables and raises concerns about potential unauthorized access to sensitive information.
  • Metadata: The maintainer has a new or inactive account and lacks detailed author information, which may indicate potential unreliability.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • -> None: self._sock = socket.create_connection((self.host, self.port), timeout=self.timeout) self._
  • return sock = socket.create_connection((self.host, self.port), timeout=self.timeout) sock.s
  • self) -> None: sock = socket.create_connection((self.host, self.port), timeout=self.timeout) self._
  • return sock = socket.create_connection((self.host, self.port), timeout=self.timeout) self._
  • == "wss" else 80) sock = socket.create_connection((host, port), timeout=timeout) if parts.scheme == "wss":
  • s self._session = requests.Session() # Set default headers
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • urce, ]) key_bytes = base64.b64decode(account_key) sig = hmac.new(key_bytes, string_to_sign.en
  • on header.""" key_bytes = base64.b64decode(master_key) string_to_sign = ( f"{verb.lower()}\
  • ).json() return base64.b64decode(resp["audioContent"]) @dataclass class VisionClient: c
  • lts.append((m.group("label"), base64.b64decode(body))) if not results: raise ValueError("No PEM
  • try: return zlib.decompress(raw) except zlib.error: return z
  • error: return zlib.decompress(raw, -zlib.MAX_WBITS) except OSError: return raw
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • : self._process = subprocess.Popen( self.command, stdin=subproc
⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • AWSCredentials": ak = os.environ.get("AWS_ACCESS_KEY_ID") sk = os.environ.get("AWS_SECRET_ACCE
  • _ACCESS_KEY_ID") sk = os.environ.get("AWS_SECRET_ACCESS_KEY") if not ak or not sk:
  • sk, session_token=os.environ.get("AWS_SESSION_TOKEN"), ) def _hmac(key: bytes, msg: str)
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sathishkumarnagarajan.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository isathish/agenticaiframework appears legitimate

⚠ 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 agenticaiframework 
Create a fully-functional mini-application called 'AgenticTaskMaster' that leverages the 'agenticaiframework' package to manage and execute a series of complex tasks orchestrated by AI agents. This application will serve as a personal productivity tool, capable of scheduling tasks, assigning them to different agents based on their expertise, and monitoring their progress in real-time.

**Application Features:**
- **Task Scheduling:** Users can input tasks they want to accomplish, specifying due dates and priorities.
- **Agent Assignment:** Based on the nature of the task (e.g., research, coding, data analysis), the app assigns the most suitable AI agent from a predefined pool.
- **Progress Tracking:** Users receive real-time updates on the status of each task through notifications or a dashboard interface.
- **Performance Monitoring:** The app monitors the performance of each agent, providing insights into efficiency and areas for improvement.
- **Customization:** Users can customize the pool of agents, adding or removing agents based on specific needs.
- **Integration Capabilities:** The application integrates with external tools such as calendars, messaging apps, and databases to enhance functionality.

**How to Utilize 'agenticaiframework':**
- Use the package's orchestration capabilities to dynamically assign tasks to appropriate agents based on predefined criteria.
- Leverage the monitoring features to track the execution of tasks and ensure they meet deadlines and quality standards.
- Employ the production-ready AI agent capabilities to simulate real-world scenarios where multiple agents collaborate to achieve common goals.
- Implement the package's advanced features to optimize task allocation and improve overall productivity.

Your task is to design and implement this application using Python, ensuring it showcases the full potential of 'agenticaiframework'.