🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple risky behaviors including potential unauthorized network activity, shell command execution, and code obfuscation, which together suggest a non-trivial risk level.

network risk
shell risk
obfuscation risk

Per-check LLM notes

Network: The package makes network calls which could be legitimate for fetching configuration or updates, but requires further investigation to ensure there's no unauthorized data transfer.
Shell: Executing commands via the shell can pose significant security risks and suggests potential for misuse, indicating a higher risk level.
Obfuscation: The code shows signs of obfuscation through unusual usage of eval and pickle, which could indicate an attempt to hide malicious behavior.
Credentials: No clear patterns indicative of credential harvesting were found in the provided snippets.
Metadata: The package has no associated GitHub repository and the maintainer information is incomplete, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

url) raw = json.load(urllib.request.urlopen(url)) slot = Slot.fromDict(raw["config"])
encode(params) response = urllib.request.urlopen(front_end_url, send_data) response.read() def
3, SSLContext return urllib.request.urlopen(url, context=SSLContext(PROTOCOL_SSLv23)) return
(PROTOCOL_SSLv23)) return urllib.request.urlopen(url) def _list_http(url): """ Implementati
) stacks = json.loads(urllib.request.urlopen(url).read()) # sort the list for stable beha
t-upload-pack" return urllib.request.urlopen(url).getcode() == 200 except Exception:

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

") for ref in eval( f"[{commit_requested!r},"
1) return getattr(__import__(m, fromlist=[f]), f) else: # it must be a name in this m
E_MAKE=1"], ) clone = pickle.loads(pickle.dumps(orig)) assert orig == clone def test_pack
pConfig", "head") clone = pickle.loads(pickle.dumps(orig)) assert orig == clone def _test_dat
onfig", "head")]) clone = pickle.loads(pickle.dumps(orig)) assert orig == clone def _test_slo

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

lt = tee_call(['echo hello'], shell=True, verbose=True) hello >>> result == (0, b'hello\\n',
lt = log_call(['echo hello'], shell=True, logger=logging.getLogger('hi'), ... log_level=logging.
fdesc'. """ out_fds = pty.openpty() err_fds = pty.openpty() queue = Queue() echo
= pty.openpty() err_fds = pty.openpty() queue = Queue() echo = kwargs.pop("echo", False)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: cern.ch>

✓ Suspicious Page Links

All external links appear legitimate

✓ 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 LbNightlyTools

Create a mini-application named 'LbNightlyAnalyzer' using the Python package 'LbNightlyTools'. This tool will serve as a dashboard for monitoring and analyzing nightly builds from the Large Hadron Collider beauty experiment (LHCb). The application should provide real-time data visualization and analysis capabilities, allowing users to track build status, identify trends over time, and receive notifications about failed builds or significant changes in build performance.

### Features:
1. **Build Status Monitoring**: Display the current status of all ongoing and completed nightly builds, including timestamps and build IDs.
2. **Trend Analysis**: Offer graphical representations of build success rates over time, highlighting any anomalies or patterns.
3. **Notification System**: Implement a system that sends alerts via email or SMS when a build fails or when there's a significant change in build success rate.
4. **Detailed Logs**: Provide access to detailed logs for each build, allowing users to diagnose issues more effectively.
5. **User Interface**: Design a user-friendly web interface where users can interact with the application, view reports, and customize their notifications.

### Utilization of 'LbNightlyTools':
- Use 'LbNightlyTools' to fetch and process data related to nightly builds. This includes retrieving build statuses, timestamps, and log files.
- Leverage the package's functionalities to parse and analyze the build data efficiently, providing insights into build performance.
- Integrate 'LbNightlyTools' to automate the process of checking build statuses and generating alerts based on predefined conditions.

### Steps to Build the Application:
1. Set up a virtual environment and install necessary packages, including 'LbNightlyTools'.
2. Develop backend logic to interact with 'LbNightlyTools', fetching and processing build data.
3. Create frontend components for displaying build statuses, trend analyses, and logs.
4. Implement the notification system using email/SMS services.
5. Test the application thoroughly to ensure it meets all specified requirements and functions correctly.