🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with no direct evidence of malicious intent or supply-chain attack.

No network calls detected.
Potential shell execution needs further investigation but appears benign.

Per-check LLM notes

Network: No network calls detected, which is normal and not suspicious.
Shell: Shell execution may be used for legitimate purposes but requires further investigation to ensure it does not pose a security risk.
Obfuscation: The observed patterns appear to be related to conditional skipping of tests based on backend implementation, likely not malicious obfuscation.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The package has some minor red flags but no clear signs of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

] @pytest.mark.skipif( __import__("abnf.parser", fromlist=["_BACKEND"])._BACKEND == "rust", reason="Repetition's parse cache is
-- @pytest.mark.skipif( __import__("abnf.parser", fromlist=["_BACKEND"])._BACKEND != "rust", reason="The bridge registry only ex

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

""" ) result = subprocess.run( [sys.executable, "-c", script], capture_out

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: declaresub.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://www.quut.com/abnfgen/

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 abnf

Create a command-line tool using Python that parses and validates input strings against a given ABNF grammar. This tool will be particularly useful for developers working on network protocols, where precise syntax validation is crucial. The application should be able to take a string input from the user and a specified ABNF grammar file as inputs, then output whether the input string matches the provided grammar rules or not.

### Key Features:
1. **Grammar Input:** Allow users to specify an ABNF grammar file that defines the structure of valid input strings.
2. **String Validation:** Implement functionality to validate an input string against the defined ABNF grammar.
3. **Error Reporting:** Provide clear error messages if the input string does not match the grammar rules, highlighting where the mismatch occurs.
4. **Command-Line Interface:** Design a simple and intuitive CLI for easy interaction.
5. **Extensibility:** Ensure the tool can handle various types of ABNF grammars, including complex ones commonly found in protocol specifications.
6. **Performance Optimization:** Optimize the parsing process to handle large inputs efficiently.

### Utilizing the 'abnf' Package:
- Use the 'abnf' package to parse the ABNF grammar file into a format that your application can use for validation.
- Leverage the package's capabilities to define and validate input strings against the parsed grammar.
- Explore advanced features of the 'abnf' package, such as rule definitions, character classes, and repetition operators, to ensure comprehensive validation.

### Steps to Complete the Project:
1. **Set Up Your Environment:** Install necessary packages, including 'abnf', and set up a virtual environment for Python.
2. **Parse the Grammar File:** Write code to read an ABNF grammar file and parse it into a usable format using the 'abnf' package.
3. **Develop Validation Logic:** Implement logic to validate input strings based on the parsed grammar.
4. **Build the CLI:** Create a command-line interface that allows users to interact with your application easily.
5. **Test Thoroughly:** Test your application with various ABNF grammars and input strings to ensure robustness and reliability.
6. **Optimize Performance:** Focus on optimizing the performance of your application, especially when dealing with large inputs or complex grammars.
7. **Document and Deploy:** Document your code and deployment instructions clearly, making it easy for others to use and extend your application.