argus-alm

v0.15.16 suspicious
5.0
Medium Risk

Argus

๐Ÿค– AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, particularly in network and shell command execution. However, there are no definitive indicators of malicious intent.

  • network calls with retry mechanisms
  • use of subprocess.run and check_output
Per-check LLM notes
  • Network: Network calls via requests.Session may be legitimate for API interactions, but the presence of retries could indicate potential automated tasks.
  • Shell: Use of subprocess.run and check_output to execute shell commands can pose risks if not properly sanitized or intended for unintended operations like key management or system interaction.
  • Obfuscation: The observed patterns suggest potential obfuscation but could also be part of normal base64 decoding operations.
  • Credentials: No clear indicators of credential harvesting detected.
  • Metadata: Low risk, but concerns about maintainer's identity and effort level suggest caution.

๐Ÿ“ฆ Package Quality Overall: Medium (5.6/10)

โ—ˆ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
  • Test runner config found: pyproject.toml
  • Classifier: Framework :: Pytest
โ—ˆ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3038 chars)
โ—‹ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
โ—ˆ Medium Type Annotations 5.0

Partial type annotation coverage

  • 173 type-annotated function signatures detected in source
โœฆ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in scylladb/argus
  • Active community โ€” 5 or more distinct contributors

๐Ÿ”ฌ Heuristic Checks

โš  Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • quests.Session: session = requests.Session() adapter = _build_retry_adapter(max_retries) sessio
  • quests.Session: session = requests.Session() retry = Retry( total=TUNNEL_API_RETRIES,
โš  Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • try: key_blob = base64.b64decode(parts[1].encode("ascii"), validate=True) except ValueErr
  • +\s*\[.*?\]"), re.compile(r"executable=/[\w/]+\s+executable_version=[\d\.]"), re.compile(r"line_numbe
โš  Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • e() process = subprocess.Popen( # noqa: S603 command,
  • r]: try: result = subprocess.run( # noqa: S603 ["ssh-keyscan", "-T", "5", "-p",
  • ths.public_key) result = subprocess.run( # noqa: S603 [ "ssh-keygen",
  • : git_info[key] = subprocess.check_output(command, shell=True, stderr=DEVNULL).decode("utf-8").strip()
  • process.check_output(command, shell=True, stderr=DEVNULL).decode("utf-8").strip() except sub
โœ“ Credential Harvesting

No credential harvesting patterns detected

โœ“ Typosquatting

No typosquatting candidates detected

โœ“ Registered Email Domain

Email domain looks legitimate: scylladb.com>

โœ“ Suspicious Page Links

All external links appear legitimate

โœ“ Git Repository History

Repository scylladb/argus appears legitimate

โš  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)
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 argus-alm 
Create a fully functional mini-application named 'Argus Guardian' that monitors and manages system resources using the 'argus-alm' package. This application will serve as a comprehensive tool for system administrators and developers to keep track of various system metrics such as CPU usage, memory consumption, disk space, network traffic, and more. Additionally, it should provide real-time alerts via email or SMS when any of these metrics exceed predefined thresholds.

Step 1: Setup the Project Environment
- Initialize a new Python project and install the 'argus-alm' package along with other necessary dependencies such as 'psutil' for system monitoring and 'twilio' for sending SMS alerts.

Step 2: Define System Metrics
- Use the 'argus-alm' package to define and collect system metrics including CPU usage, memory usage, disk space, and network traffic. Implement functions to retrieve these metrics periodically (e.g., every 5 minutes).

Step 3: Create Thresholds and Alerts
- Allow users to set custom thresholds for each metric. When a metric exceeds its threshold, trigger an alert. For example, if the CPU usage goes above 80%, send an email or SMS notification.

Step 4: Integrate Email and SMS Notifications
- Utilize SMTP libraries for sending emails and the Twilio API for sending SMS messages. Ensure that the application can handle multiple recipients for notifications.

Step 5: User Interface
- Develop a simple command-line interface (CLI) where users can configure settings, view current system status, and manage alerts. Optionally, create a basic web interface using Flask or Django for a more user-friendly experience.

Suggested Features:
- Historical data logging and visualization
- Customizable alert frequency and methods
- Support for additional metrics like temperature and process monitoring
- Integration with cloud services for remote monitoring

By following these steps and utilizing the 'argus-alm' package effectively, you'll develop a robust and versatile tool for system resource management.

๐Ÿ’ฌ Discussion Feed

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