AI Analysis
The package exhibits moderate risks due to potential shell execution and code obfuscation, which could be exploited for malicious purposes. However, there is no concrete evidence of malicious intent, just increased suspicion.
- Shell execution risk
- Code obfuscation
Per-check LLM notes
- Network: The network patterns detected seem to be related to potential compression or data handling, which might not align with the package's primary purpose but could be benign.
- Shell: The shell execution patterns indicate the package may spawn subprocesses, potentially for tasks like monitoring or managing system processes, which could introduce risks if not properly sanitized or controlled.
- Obfuscation: The code shows signs of obfuscation which could be used to hide malicious activities, but it's not conclusive without further context.
- Credentials: No clear patterns indicative of credential harvesting were found.
- Metadata: The maintainer's author information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (6.2/10)
Test suite present — 21 test file(s) found
21 test file(s) detected (e.g. archiver_tests.py)
Some documentation present
Detailed PyPI description (5809 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
10 type-annotated function signatures detected in source
Active multi-contributor project
3 unique contributor(s) across 72 commits in rogdham/backports.zstdSmall but multi-author team (3–4 contributors)
Heuristic Checks
Found 1 network call pattern(s)
iginal_stdout()) opener = urllib.request.build_opener() if gzip: opener.addheaders.append
Found 5 obfuscation pattern(s)
nc) @classmethod def eval(cls, cand): return cand.func() if isinstance(cand, crror, errtext) as cm: compile(statement, '<test string>', 'exec') err = cm.exception testcase.assertIsNotNone(err.lways', SyntaxWarning) compile(statement, '<testcase>', 'exec') testcase.assertEqual(len(warns), 1, warns) warn,resh: __import__(modname) except ImportError: ret.close() restored_1 = pickle.loads(saved_1) first, *rest = restored_1.iterdir()
Found 6 shell execution pattern(s)
try: rc = subprocess.run(["launchctl", "managername"],self.mem_watchdog = subprocess.Popen([sys.executable, watchdog_script],= [python, *args] p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stType'] proc = subprocess.Popen(cmd, stdout=subprocelex.quote, cmd))) subprocess.run(cmd, check=True) else: subprocess.run(cmue) else: subprocess.run(cmd, stdout=subprocess.PIPE,
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: rogdham.net>
All external links appear legitimate
Repository rogdham/backports.zstd appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a command-line utility called 'ZstdCompress' using Python that leverages the 'backports.zstd' package for compressing and decompressing files. This tool will serve as a simple yet powerful way to manage file sizes on disk by reducing their size through zstandard (zstd) compression, which is known for its high compression ratio and fast decompression speed. ### Features: - **Compression**: Users should be able to input a file path and have it compressed into a .zst file using zstd compression. - **Decompression**: Conversely, users should also be able to provide a .zst file path to decompress it back to its original format. - **Compression Level Control**: Offer adjustable compression levels from 1 to 22, where 1 is fastest but least compressed, and 22 is slowest but most compressed. - **Output Path Customization**: Allow users to specify an output directory for both compressed and decompressed files. - **Help Menu**: Include a help menu (--help) that explains usage and all available options. ### How 'backports.zstd' is Utilized: - The 'backports.zstd' package provides functions for compressing and decompressing data using the zstandard algorithm. Your utility will use these functions to handle the actual compression and decompression tasks. Specifically, you'll need to utilize the `compress()` and `decompress()` methods provided by the package. ### Steps to Create the Utility: 1. **Setup Environment**: Ensure your development environment has Python installed. Install the 'backports.zstd' package via pip. 2. **Define Main Functionality**: Write functions to handle file reading/writing, compression, and decompression using the 'backports.zstd' package. 3. **Argument Parsing**: Use argparse to parse command-line arguments such as file paths, output directories, and compression levels. 4. **Error Handling**: Implement error handling for common issues like invalid file paths or unsupported file types. 5. **Testing**: Test your utility thoroughly with various files and scenarios to ensure reliability and efficiency. 6. **Documentation**: Provide clear documentation explaining how to install, use, and maintain your utility. 7. **Deployment**: Package your utility as a standalone executable or upload it to a repository for easy sharing and distribution.
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