akita-sentinel

v0.2.10 suspicious
6.0
Medium Risk

Akita Sentinel — local AI agent monitoring. Watch your agents, scan new skills before they run.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated risks in shell execution and credential handling, suggesting potential misuse of permissions and unauthorized access to user credentials. These issues, combined with incomplete metadata, raise suspicion.

  • High shell risk indicating potential system permission changes.
  • Elevated credential risk due to 'keyring' module usage.
Per-check LLM notes
  • Network: The network calls appear to be checking backend reachability and status, which is common for service packages.
  • Shell: The shell execution patterns indicate potential system permission changes, which may be unexpected and pose a risk if not properly documented or necessary for the package's functionality.
  • Obfuscation: The detected obfuscation patterns seem to be related to GUI layout and color settings, which are likely benign.
  • Credentials: The usage of the 'keyring' module indicates potential interaction with user credentials or secrets, raising concerns about possible unauthorized access or harvesting.
  • Metadata: The package shows some red flags such as missing repository and author details, indicating potential unreliability.

📦 Package Quality Overall: Low (4.2/10)

◈ 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 (10585 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 181 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 9.0

Found 6 network call pattern(s)

  • try: req = urllib.request.Request(status_url, headers=_poll_headers) with
  • oll_headers) with urllib.request.urlopen(req, context=_ssl_ctx, timeout=5) as resp:
  • alse try: urllib.request.urlopen(test_url, context=_ssl_probe.create_default_context(
  • achable = True except urllib.request.HTTPError: _backend_reachable = True # HTTP err
  • alse try: urllib.request.urlopen(test_url, context=_ssl_probe2.create_default_context
  • chable2 = True except urllib.request.HTTPError: _backend_reachable2 = True # HTTP er
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • geometry(f"{w}x{h}") root.eval("tk::PlaceWindow . center") # ── Colours ──────────────
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ._readerthread spawn. subprocess.run( ["icacls", str(p), "/inheritance:r", "/grant:r"
  • """ try: result = subprocess.run( ["icacls", str(p)], check=True,
  • try: result = subprocess.run( ["tasklist", "/FI", f"PID eq {pid}", "/NH"]
  • FakeThread signature. subprocess.run( ["icacls", str(p), "/inheritance:r", "/grant:r"
  • e errors) then load fresh subprocess.run( ["launchctl", "unload", str(_plist_path())],
⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • key, probe_val) val = keyring.get_password(KEYCHAIN_SERVICE, test_key) try: keyring
  • mport keyring return keyring.get_password(service, key) except Exception as e: logger.warn
  • ys()): existing = keyring.get_password(service, key) if existing is not None:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: plugpipe.ai>

✓ 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 akita-sentinel 
Create a desktop application using Python that monitors local AI agents running on your machine, ensuring their safety and integrity. This application, named 'Guardian AI', will utilize the 'akita-sentinel' package to achieve its goals. Here’s a detailed breakdown of the steps and features:

1. **Setup and Initialization**:
   - Initialize a new Python project.
   - Install necessary packages including 'akita-sentinel'.
   - Set up a user-friendly GUI using a library like PyQt5 or Tkinter.

2. **Agent Monitoring**:
   - Integrate 'akita-sentinel' to continuously monitor AI agents running on the local machine.
   - Display a list of detected AI agents in the GUI, showing their status (running, paused, stopped).

3. **Security Checks**:
   - Implement a feature where 'akita-sentinel' scans new AI skills before they start running.
   - Provide real-time alerts if any potential security risks are identified.

4. **User Interaction**:
   - Allow users to pause, resume, or stop individual AI agents from the GUI.
   - Include options for users to configure alert settings and notification preferences.

5. **Logging and Reporting**:
   - Log all activities related to AI agents, including start/stop times and any alerts issued.
   - Generate periodic reports summarizing the health and security status of monitored AI agents.

6. **Enhancements**:
   - Add a feature to automatically update 'akita-sentinel' to the latest version.
   - Consider integrating machine learning models to predict potential risks based on historical data.

By following these steps, you will create a robust tool that helps safeguard your local AI environment, leveraging the powerful capabilities of 'akita-sentinel'.

💬 Discussion Feed

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