autosentry

v0.13.1 suspicious
5.0
Medium Risk

Self-healing supervisor for long-running processes — watch a command, catch the failure, fix it, leave a paper trail.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential network and shell execution vulnerabilities, which could be exploited for malicious purposes. However, there is no direct evidence of malicious activity.

  • Moderate network risk
  • High shell risk
Per-check LLM notes
  • Network: Network calls to external URLs might be legitimate for checking versions or downloading files, but could also indicate data exfiltration or C2 activities.
  • Shell: Execution of shell commands poses a significant risk as it can be used to perform arbitrary actions on the system, potentially indicating malicious intent.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has an incomplete profile and may be new or inactive, raising some suspicion but not definitive proof of malice.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (57248 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

  • 276 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 32 commits in ulmentflam/autosentry
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • """ try: with urllib.request.urlopen(_PYPI_JSON, timeout=timeout) as resp: # noqa: S310
  • version try: with urllib.request.urlopen(_INSTALL_SH_URL, timeout=30) as resp: # noqa: S310
  • encode("utf-8") req = urllib.request.Request( # noqa: S310 self.url, dat
  • try: with urllib.request.urlopen(req, timeout=15) as resp: # noqa: S310
  • not None else None req = urllib.request.Request(url, data=data, headers=headers, method=method) # n
  • a: S310 try: with urllib.request.urlopen(req, timeout=15) as resp: # noqa: S310
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • """ try: result = subprocess.run( # noqa: S603 cmd, capture_output=T
  • return ( subprocess.run( # noqa: S603 — explicit list, no shell [py
  • int: try: return subprocess.call(cmd) # noqa: S603 — explicit list, no shell except File
  • return 1 completed = subprocess.run( # noqa: S603 ["sh", "-s"], input=script,
  • letedProcess[str]: return subprocess.run( # noqa: S603 cmd, capture_output=True, text=True,
  • ne: return "" r = subprocess.run( # noqa: S603 ["git", "-C", str(cwd), "status", "--
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: jwo3.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ulmentflam/autosentry 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 autosentry 
Create a robust monitoring and recovery tool named 'ProcessGuard' using the Python package 'autosentry'. This tool will be designed to monitor critical system processes on a Linux server, ensuring they remain operational even under unexpected failures. The application should perform the following tasks:

1. **Initialization**: Allow users to specify which processes to monitor through a configuration file or command-line arguments.
2. **Monitoring**: Continuously monitor the specified processes to detect any anomalies or crashes.
3. **Failure Detection**: Automatically identify when a monitored process fails or becomes unresponsive.
4. **Automatic Recovery**: Attempt to restart the failed process automatically after detecting a failure.
5. **Logging**: Maintain a comprehensive log of all monitoring activities, including start times, end times, and any actions taken during recovery attempts.
6. **Alerting**: Send email alerts to administrators whenever a process fails and recovery attempts fail.
7. **Configuration Flexibility**: Allow customization of recovery strategies and alert settings via a configuration file.
8. **User Interface**: Provide a simple CLI interface for users to interact with the application, such as starting/stopping monitoring, viewing logs, and configuring settings.

Utilize the 'autosentry' package to handle the core functionalities of monitoring and recovery. Specifically, leverage its ability to watch commands, catch failures, attempt fixes, and maintain logs to ensure a seamless user experience and reliable operation of critical processes.

💬 Discussion Feed

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