automa_ai

v0.7.1 suspicious
6.0
Medium Risk

PNNL Auto Multi-Agent AI: Dynamic multi-agent system for building applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package AUTOMA-AI presents a notable risk due to its high credential risk and potential for misuse of AWS credentials. While other risks are relatively low, the combination of signals raises concerns about potential supply-chain attacks.

  • High credential risk due to direct access to AWS credentials
  • Potential for supply-chain attack given the high risk associated with credential exposure
Per-check LLM notes
  • Network: The package uses network calls for potential HTTP requests and socket connections, which could be legitimate for API interactions or updates.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The presence of base64 decoding suggests some form of data obfuscation, but it could be legitimate for encrypted or compressed data handling.
  • Credentials: The code snippet shows direct access to AWS credentials via environment variables, which poses a high risk of unauthorized access if not properly secured.
  • Metadata: The package shows some signs of low maintainer activity and metadata quality but does not present clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. adk_agent_test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (25704 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 295 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • try: with socket.create_connection((host, port), timeout=1): return True
  • self._httpx_client = httpx.AsyncClient(timeout=httpx.Timeout(None)) self.agent_card = subag
  • lf.timeout async with httpx.AsyncClient(timeout=timeout) as httpx_client: await self.ini
  • ) -> bool: async with httpx.AsyncClient(timeout=10) as client: try: vali
  • t_token}"} async with httpx.AsyncClient(timeout=10) as client: try: resp
  • or_task() async with httpx.AsyncClient(timeout=httpx.Timeout(None)) as httpx_client: a2
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • es]: return data["type"], base64.b64decode(data["payload"]) class PlainRedisSaver( BaseCheckpoint
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • : aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID") aws_secret_access_key = os.getenv("A
  • aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY") aws_region = os.getenv("AWS_REGI
  • SS_KEY") aws_region = os.getenv("AWS_REGION") if aws_access_key_id is None or aws_secret_
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: pnnl.gov>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with automa_ai 
Create a dynamic multi-agent simulation app using the 'automa_ai' Python package. This application will model a simple traffic management system where multiple agents represent vehicles on a road network. Each vehicle agent will have the ability to make decisions based on real-time traffic conditions and predefined rules. The goal is to simulate traffic flow, identify bottlenecks, and suggest optimal routes for vehicles to reduce congestion and travel time.

Step 1: Set up the environment
- Install Python and necessary libraries including 'automa_ai'.
- Import the required modules from 'automa_ai' and initialize the simulation environment.

Step 2: Define the Road Network
- Create a map of interconnected roads with different capacities and traffic lights at intersections.
- Use 'automa_ai' to define the topology of the network and set initial traffic conditions.

Step 3: Design Vehicle Agents
- Implement vehicle agents with attributes like speed, destination, and current position.
- Utilize 'automa_ai' functionalities to enable agents to perceive their surroundings and make decisions about movement.

Step 4: Implement Traffic Rules and Behaviors
- Define rules for lane changing, stopping at red lights, and following the speed limit.
- Integrate 'automa_ai' to allow agents to adapt their behavior dynamically based on traffic density and other factors.

Step 5: Simulate and Analyze Traffic Flow
- Run the simulation for a specified duration to observe traffic patterns.
- Collect data on travel times, number of collisions, and overall efficiency of the system.
- Use 'automa_ai' tools to visualize the simulation results and analyze performance metrics.

Suggested Features:
- Adjustable parameters for traffic density and road capacity.
- Real-time visualization of traffic flow and agent movements.
- Historical data analysis to identify trends and hotspots.
- Scenario testing to evaluate the impact of changes in traffic rules or infrastructure.

How 'automa_ai' is Utilized:
- The 'automa_ai' package provides the framework for defining and simulating complex systems with interacting agents. It supports the creation of the road network and vehicle agents, enabling them to interact according to predefined rules and conditions. Additionally, it offers advanced analytics and visualization capabilities to interpret simulation outcomes effectively.

💬 Discussion Feed

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