🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legitimate purposes with minimal risk indicators. The primary concerns are related to shell execution for hardware detection, but there's no evidence of malicious activity.

Moderate shell risk due to ffmpeg command execution
Low risk in other categories such as obfuscation and credential handling

Per-check LLM notes

Network: The network calls seem to be for checking versions and respecting proxy settings, which is common for software updates.
Shell: Executing shell commands like 'ffmpeg -version' may be for hardware codec detection but could pose risks if not properly sanitized or controlled.
Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating no immediate risk to secrets or credentials.
Metadata: The author has only one package, which may indicate a new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

involved). """ # urllib.request.urlopen already respects http_proxy / https_proxy on most
try: req = urllib.request.Request(url, headers={"User-Agent": "HwCodecDetect/1.0"})
etect/1.0"}) with urllib.request.urlopen(req, timeout=DOWNLOAD_TIMEOUT, context=ssl_ctx) as r
y: response = requests.get(version_url, timeout=10) response.raise_for_
try: response = requests.get(version_url, timeout=10) response.raise_for_stat

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

cess.DEVNULL result = subprocess.run( command, check=True, st
DOW try: result = subprocess.run( command, check=True, st
try: result = subprocess.run( ["ffmpeg", "-version"], capture_output=
try: result = subprocess.run( ["ffmpeg", "-hide_banner", "-hwaccels"]
pass try: return subprocess.run( command, check=check, c
KB unit result = subprocess.run( ['wmic', 'OS', 'Get', 'FreePhysicalMemory',

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository whyb/HwCodecDetect appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "whyb" 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 HwCodecDetect

Create a command-line utility named 'VideoDecoderAnalyzer' using Python that leverages the 'HwCodecDetect' package to analyze the capabilities of various hardware video decoders on different operating systems. This utility will serve as a tool for developers and enthusiasts to understand which codecs their hardware supports natively and which require software decoding fallbacks. The application should provide detailed information about each codec, including its performance metrics when decoded via hardware versus software. Here are the key steps and features your project should include:

1. **Installation and Setup**: Ensure that 'HwCodecDetect' and FFmpeg are properly installed and configured on the system. Your application should check these prerequisites at runtime and guide users through installation if necessary.
2. **Detection Mechanism**: Implement a function to automatically detect all available hardware decoders and encoders on the user's machine. Use 'HwCodecDetect' to run tests and gather performance data for each codec supported by the hardware.
3. **User Interface**: Design a clean and intuitive command-line interface that allows users to select which codecs they want to test and view results. Include options for testing both decoders and encoders, and specify whether the test should focus on hardware acceleration or software fallbacks.
4. **Performance Metrics**: For each tested codec, display relevant performance metrics such as frame rate, latency, and CPU/GPU usage. Compare hardware-accelerated performance against software decoding where applicable.
5. **Reporting**: Develop a feature that generates a comprehensive report summarizing the test results. This report should be exportable as a PDF or CSV file for further analysis or record-keeping.
6. **Advanced Features**: Consider adding advanced functionalities like benchmarking multiple codecs simultaneously, comparing performance across different hardware configurations, or even integrating with cloud services for remote testing.

Your goal is to create a versatile and informative tool that not only identifies supported codecs but also provides valuable insights into their performance characteristics under various conditions.