atomict

v1.2.27 suspicious
5.0
Medium Risk

The client application for the https://atomictessellator.com/

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to network and obfuscation activities, though there are no clear signs of malicious intent such as shell execution or credential harvesting.

  • moderate network risk
  • high obfuscation risk
Per-check LLM notes
  • Network: The presence of network calls suggests the package may communicate with an external API, which could be legitimate but also indicative of data exfiltration or command and control activities.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The presence of base64 and zlib/lz4 decoding suggests potential obfuscation to hide code logic or data.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The author's name is missing or very short and appears to be new or inactive, which raises some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (5.2/10)

◈ 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 (1457 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

  • 196 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in AtomicTessellator/atomic_cli
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • oken {token}" response = requests.get(f"{api_root}/{path}", headers=headers, timeout=120) con
  • not None: response = requests.post( f"{api_root}/{path}", data=payload, headers=hea
  • else: response = requests.post(f"{api_root}/{path}", data=payload, headers=headers, timeout
  • Token {token}" response = requests.patch(f"{api_root}/{path}", data=payload_enc, headers=headers, tim
  • Token {token}" response = requests.delete(f"{api_root}/{path}", headers=headers, timeout=120) if
  • response = requests.post( f"{self.url}/loki/api/v1/push",
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • b") as f: f.write(base64.b64decode(file_content)) console.print(f"[green]Successfully
  • : f.write(base64.b64decode(file_content_b64)) progress
  • fig.fish: if type -q tess eval (env _TESS_COMPLETE=fish_source tess) end """ click.echo(
  • 'zlib': frame_bytes = zlib.decompress(frame_bytes) elif compression_type == 'lz4': fra
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: atomictessellator.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AtomicTessellator/atomic_cli 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 atomict 
Create a Python-based mini-application that integrates with the Atomict Tessellator service using the 'atomict' package. This application will serve as a user-friendly interface for tessellation operations, allowing users to upload images or specify geometric shapes, and then apply various tessellation algorithms provided by the Atomict Tessellator API. The application should have the following features:

1. User Interface: Develop a simple yet intuitive command-line interface (CLI) or a basic graphical user interface (GUI) using Tkinter or similar library.
2. Image Upload/Shape Specification: Allow users to either upload an image file or define geometric shapes through coordinates or other parameters.
3. Algorithm Selection: Provide a selection of tessellation algorithms available from the Atomict Tessellator API. Users should be able to choose which algorithm to use for their input.
4. Output Visualization: Display the tessellated result either in the CLI or GUI, and provide an option to save the output as an image file.
5. Error Handling: Implement robust error handling to manage cases where the input is invalid or the API call fails.
6. Documentation: Include clear documentation on how to install and run the application, along with examples of valid inputs.

To utilize the 'atomict' package, you'll need to first install it via pip if it's not already installed. Then, use its functions to interact with the Atomict Tessellator API. Specifically, focus on how to authenticate with the service, upload data, process requests, and handle responses. Make sure to follow best practices for security, such as securely managing any API keys or credentials needed to access the service.

💬 Discussion Feed

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