asammdf

v8.8.16 suspicious
5.0
Medium Risk

ASAM MDF measurement data file parser

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows elevated risks due to potential shell execution commands and incomplete metadata, which could indicate tampering or malicious intent.

  • Shell risk is high with potential for system modification.
  • Incomplete author metadata raises suspicion.
Per-check LLM notes
  • Network: The network calls appear to be fetching PR data from GitHub API which seems benign.
  • Shell: The shell executions include commands that could potentially modify the system state and execute arbitrary code, suggesting higher risk.
  • Obfuscation: The observed pattern suggests some form of obfuscation, but it may be part of the package's intended functionality, such as handling binary data or specific data structures.
  • Credentials: No clear indicators of credential harvesting or secret storage were detected.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

  • 13 test file(s) detected (e.g. test-lz4-abi.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://asammdf.readthedocs.io/en/master
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (10768 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 267 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

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

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • load(f) data = json.loads(urllib.request.urlopen(GITHUB_API_PR_URL).read().decode("utf-8")) prs =
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • nt]], list[int]]: DG = "DG\x00\x00\x00\x00\x40\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00" others = "(D[VTZIL]|AT|C[AGHNC]|EV|FH|HL|LD|MD|R[DVI]|
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • format(build["user"]) os.system( """ rm -rf zstd-{user}-{sha} &&
  • ld["hash"]) else: os.system("cd ../ && make -j && cd tests") return "../zstd"
  • != None: os.system( """ echo "{
  • n" try: content = subprocess.check_output("coverage report", encoding="utf-8", cwd=ROOT) except:
  • nv)) if pipe: s = subprocess.Popen(cmd_args, stdout=subprocess.PIP
  • = env) else: s = subprocess.Popen(cmd_args, env = env) stdout_data, stderr_data = s.commun
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository danielhrisca/asammdf 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 asammdf 
Create a Python-based mini-application that allows users to analyze ASAM MDF (Measurement Data Format) files, commonly used in automotive engineering for logging vehicle data. Your application should be able to parse MDF files and provide detailed analysis and visualization of the logged data. Here’s a step-by-step guide on how to approach this project:

1. **Setup Environment**: Start by setting up your Python environment. Install necessary packages such as `asammdf`, `matplotlib` for plotting, and `pandas` for data manipulation.
2. **File Parsing**: Use the `asammdf` package to load MDF files into your application. Explore the capabilities of `asammdf` to understand how it parses and organizes MDF data.
3. **Data Analysis**: Implement functionality within your application to perform basic data analysis on the parsed MDF data. This could include calculating statistical measures like mean, median, standard deviation, etc., for various signals.
4. **Visualization**: Integrate `matplotlib` to visualize the data. Create plots for time series analysis, histograms, and any other relevant visual representations of the data.
5. **User Interface**: Develop a simple command-line interface (CLI) or a graphical user interface (GUI) using `tkinter` or another library of your choice. The UI should allow users to select MDF files, view analysis results, and interact with visualizations.
6. **Advanced Features**: Consider adding advanced features such as filtering options based on specific signal names or time ranges, exporting analysis results to CSV or Excel formats, and support for multiple file inputs at once.
7. **Documentation & Testing**: Write documentation for your application and ensure all functionalities work as expected through thorough testing.

By following these steps, you will create a comprehensive tool for analyzing automotive data stored in MDF format, leveraging the powerful `asammdf` package.

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