🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks related to network and shell operations, suggesting potential vulnerabilities or malicious intent. While there's no clear evidence of credential harvesting, the combination of risks points towards a possible supply-chain attack.

High network risk due to unsecured network calls
High shell risk from potential execution of arbitrary commands

Per-check LLM notes

Network: The presence of network calls with authentication credentials suggests potential unauthorized access attempts or data exfiltration.
Shell: Executing arbitrary shell commands and using system calls can indicate the possibility of executing malicious code or creating backdoors.
Obfuscation: The observed patterns suggest an attempt to obfuscate code through base64 encoding and the use of eval which can be indicative of malicious intent.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The package shows some red flags such as missing maintainer information and an inaccessible git repository, indicating potential issues with accountability and transparency.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

3 test file(s) detected (e.g. as_mqtt.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7241 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

33 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

try: response = requests.get(url, stream=True) if response.status_code >= 400
out']) response = requests.get(url, auth = ('user', 'pass'), timeout=timeout) i
try: response = requests.post(url, value, timeout=5) retval.setContent(respons
password=None): ssh = paramiko.SSHClient() ssh.set_missing_host_key_policy(paramiko.AutoAddPo
password']) ssh = paramiko.SSHClient() target['ssh'] = ssh ssh.set_missin

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

) message_bytes = base64.b64decode(base64_bytes) value.setContent(message_bytes.dec
oken[1:].isnumeric): val = eval(token) if isinstance(val, int): value.setValue(int, v
(broad) _RUNTIME_ERR_RE = re.compile(r"^(RuntimeError|FatalError|Error during execution)") def run_script(script_path: Path) -> dict[str, An

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

else: os.system(value) return self.nextPC() # Arithmetic su
etContent()) result = os.popen(command).read() return ECValue(type=str, content=res
background']: subprocess.Popen(["sh", "-c", value]) else: os.sy

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 allspeak-ai

Create a multilingual code snippet generator tool using the 'allspeak-ai' Python package. This tool will allow developers to generate code snippets in various programming languages based on natural language descriptions provided in their preferred language. For example, a user could describe a functionality they want to implement in English, and the tool would generate the corresponding code in Python, JavaScript, Java, etc.

### Core Features:
1. **Language Support**: The tool should support at least five popular programming languages: Python, JavaScript, Java, C++, and Ruby.
2. **Multilingual Input**: Users should be able to input their requirements in multiple languages such as English, Spanish, French, German, and Chinese.
3. **Code Generation**: Based on the natural language description provided, the tool should generate syntactically correct code snippets in the selected programming language.
4. **Interactive Interface**: Develop a simple web-based interface where users can input their requirements and see the generated code snippets.
5. **Error Handling**: Implement basic error handling to provide feedback if the input description is unclear or too complex.
6. **Documentation and Examples**: Include a section in the tool where users can find examples of common functionalities and how to request them in different languages.

### How 'allspeak-ai' is Utilized:
- **Natural Language Processing**: Use 'allspeak-ai' to parse and understand the natural language inputs from users.
- **Code Generation Logic**: Leverage the high-level scripting capabilities of 'allspeak-ai' to map natural language descriptions to specific programming constructs and syntax.
- **Multilingual Support**: 'allspeak-ai' provides multilingual support which will be essential for interpreting user inputs in various languages and generating code accordingly.

### Steps to Build the Tool:
1. **Set Up Environment**: Install necessary libraries including 'allspeak-ai', Flask for the web interface, and any other dependencies required.
2. **Design User Interface**: Create a simple HTML/CSS/JavaScript frontend where users can select their preferred programming language and input their requirements.
3. **Backend Development**: Using Flask, develop the backend logic that integrates with 'allspeak-ai' to process user inputs and generate code snippets.
4. **Testing**: Test the tool with a variety of inputs in different languages to ensure it works correctly across all supported languages and programming languages.
5. **Deployment**: Deploy the application on a cloud platform like Heroku or AWS so it can be accessed publicly.