aicoin

v1.1.0 suspicious
6.0
Medium Risk

AICoin - Decentralized AI computing network where nodes mine AIC tokens by contributing GPU/CPU compute power to run AI inference

๐Ÿค– AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several suspicious behaviors including potential credential harvesting, obfuscated code, and execution of shell commands which could indicate malicious intent or misuse. However, without concrete evidence of malicious activities, it cannot be conclusively classified as malicious.

  • credential risk
  • shell command execution
  • code obfuscation
Per-check LLM notes
  • Network: The network calls seem to be standard HTTP/WS requests which could be legitimate depending on the package's functionality.
  • Shell: Executing shell commands like 'nvidia-smi' and 'rustc --version' might be part of the package's intended functionality, but running arbitrary commands is risky and could indicate potential misuse.
  • Obfuscation: The obfuscation patterns suggest an attempt to hide code logic, which could be benign but raises suspicion for potential evasion of analysis.
  • Credentials: Direct handling and prompt for user input of password indicates risk of credential harvesting, especially with insufficient context provided for legitimate use.
  • Metadata: The maintainer's lack of activity and information raises some concerns, but there is no clear evidence of malicious intent.

๐Ÿ“ฆ Package Quality Overall: Medium (7.0/10)

โ—ˆ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
โ—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/ctz168/aicoin#readme
  • Detailed PyPI description (10385 chars)
โ—ˆ Medium Contributing Guide 7.0

Some contribution signals present

  • Governance file: governance.py
โ—ˆ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 550 type-annotated function signatures detected in source
โœฆ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 11 commits in ctz168/aicoin
  • Small but multi-author team (3โ€“4 contributors)

๐Ÿ”ฌ Heuristic Checks

โš  Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • self._client_session = aiohttp.ClientSession( connector=connector,
  • arer {token}" async with aiohttp.ClientSession() as session: async with session.request(method, url
  • t aiohttp async with aiohttp.ClientSession() as session: async with session.ws_connect(ws_u
  • async with aiohttp.ClientSession() as http_session: local
  • True self._session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=5)) # ๅฏๅŠจๅŒๆญฅ HTTP
โš  Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • hex(nonce_hex) full = base64.b64decode(ciphertext_b64) ciphertext = full[:-16] tag
  • code=True ) model_obj.eval() async def handle_completions(request): data =
  • vice) self.model.eval() # ่ฎก็ฎ—ๆจกๅž‹ๅคงๅฐ param_count = sum(p.nume
  • vice) self.model.eval() # ๆๅ–็ป„ไปถๅผ•็”จๅนถ้‡Šๆ”พไธ้œ€่ฆ็š„ๅฑ‚ logger.info("
  • # ๅŠ ๆƒ้šๆœบ้€‰ๆ‹ฉ r = __import__("random").random() * total_weight cumulative = 0.0
  • numpy_arr = pickle.loads(zlib.decompress(data[2:])) elif first_byte == 0x00:
โš  Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • bprocess result = subprocess.run( ["nvidia-smi", "--query-gpu=name,memory.tot
  • t subprocess result = subprocess.run( ["nvidia-smi", "--query-gpu=memory.used", "--fo
  • try: result = subprocess.run( ["nvidia-smi", "--query-gpu=index,name,memo
  • """ try: result = subprocess.run( ["rustc", "--version"], capture_out
  • """ try: result = subprocess.run( ["cargo", "--version"], capture_out
  • """ try: result = subprocess.run( [sys.executable, "-m", "maturin", "--version"],
โš  Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • password = args.password or getpass.getpass("ๅฏ†็ : ") if not email or not password: print("้”™่ฏฏ
โœ“ Typosquatting

No typosquatting candidates detected

โœ“ Registered Email Domain

Email domain looks legitimate: proton.me>

โœ“ Suspicious Page Links

All external links appear legitimate

โš  Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
โš  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)
โœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

๐Ÿ’ก AI App Starter Prompt

Use this prompt to build a project with aicoin 
Create a decentralized AI computing mini-application using the 'aicoin' package. This application will serve as a simple node manager for users to join the AICoin network, contribute their GPU/CPU resources, and earn AIC tokens for running AI inference tasks. Hereโ€™s a step-by-step guide on how to develop this mini-application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary libraries and the 'aicoin' package.
2. **User Interface**: Develop a user-friendly interface where users can sign up/login to manage their node contributions and view their earned AIC tokens.
3. **Node Registration**: Implement a feature allowing users to register their computing nodes (with details like available GPU/CPU resources) to the AICoin network.
4. **Task Assignment**: Utilize the 'aicoin' package to assign AI inference tasks to registered nodes based on their resource availability and network requirements.
5. **Token Rewards**: Integrate token reward mechanisms where nodes are rewarded AIC tokens for successfully completing assigned tasks.
6. **Monitoring & Reporting**: Include functionalities to monitor task execution status and generate reports on resource usage and token earnings for each node.
7. **Security Measures**: Ensure all data transmissions are secure and implement measures to prevent unauthorized access or manipulation of the system.
8. **Testing & Deployment**: Thoroughly test the application for any bugs or issues before deploying it to a public or private network.

Suggested Features:
- Real-time monitoring of node performance and task progress.
- Detailed analytics on resource utilization and token distribution.
- User profiles showcasing contribution history and total earnings.
- Support for multiple types of AI models and inference tasks.
- Integration with popular cryptocurrency wallets for seamless token management.

The 'aicoin' package is utilized primarily for managing the decentralized network operations, including node registration, task assignment, and token rewards. It provides the necessary tools and APIs to facilitate these processes efficiently.