AI Analysis
Final verdict: SUSPICIOUS
The package exhibits high shell execution risk and moderate network and obfuscation risks. While there are no clear signs of credential harvesting, the combination of these factors raises concerns about potential malicious intent.
- High shell risk indicating potential for unauthorized actions
- Moderate obfuscation and network risks adding to overall suspicion
Per-check LLM notes
- Network: Network calls are common in packages that interact with external services, but the specific endpoints should be reviewed for legitimacy.
- Shell: Execution of shell commands can pose significant risks if not properly sanitized or controlled, suggesting potential for unauthorized actions or command injection.
- Obfuscation: The lambda function generates a UUID and uses it for some purpose which might be obfuscating data or generating unique identifiers, raising suspicion.
- Credentials: No clear patterns of credential harvesting were detected.
- Metadata: The package has no typosquatting or email domain flags, but the repository is not found and the maintainer's information is sparse.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/opensre/autosre#readmeDetailed PyPI description (9261 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
492 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 7.5
Found 5 network call pattern(s)
A: async with httpx.AsyncClient() as client: response = await client.gettry: async with httpx.AsyncClient() as client: response = await client.get(rvice_id] async with httpx.AsyncClient() as client: response = await client.get(""" async with httpx.AsyncClient() as client: response = await client.get(} async with httpx.AsyncClient() as client: response = await client.post(
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
field(default_factory=lambda: __import__('uuid').uuid4().hex[:8]) iterations: int = 0 completed_at:
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
fig]) proc = subprocess.run(cmd, capture_output=True, text=True) if proesult proc = subprocess.run( [script_path] + args, capture_outpu
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: autosre.ai>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 autosre-ai
Develop a fully-functional mini-application named 'SREGuard' using the Python package 'autosre-ai'. This application will serve as an Incident Response Assistant for DevOps teams, focusing on automating the process of incident detection, root cause analysis, and remediation. The goal is to streamline the SRE workflow, reduce downtime, and improve system reliability. Step-by-Step Application Functionality: 1. **Incident Detection**: Implement a real-time monitoring system that integrates with popular monitoring tools such as Prometheus, Grafana, or Datadog. When anomalies are detected based on predefined thresholds or patterns, trigger an alert within the application. 2. **Root Cause Analysis**: Utilize 'autosre-ai' to perform advanced root cause analysis on the triggered incidents. The application should be able to analyze logs, metrics, and traces to identify the underlying issue efficiently. 3. **Auto-Remediation**: Based on the findings from the root cause analysis, the application should suggest or automatically execute remediation steps if deemed safe and effective. These could include restarting services, scaling resources, or rolling back changes. 4. **Post-Incident Review**: After an incident has been resolved, the application should facilitate a post-incident review process. It should compile all relevant data, including the timeline of events, actions taken, and outcomes, into a report for further analysis and learning. Suggested Features: - Integration with multiple monitoring tools via APIs. - Customizable threshold settings for different types of alerts. - A user-friendly interface for managing incidents and reviewing past events. - Detailed documentation and tutorials for setting up and using the application. - Support for logging and tracking the entire incident lifecycle. How 'autosre-ai' is Utilized: - For root cause analysis, the 'autosre-ai' package will be leveraged to analyze complex data sets quickly and accurately. This includes parsing through log files, comparing current state metrics against historical data, and identifying potential correlations. - During auto-remediation, 'autosre-ai' will provide intelligent suggestions based on the severity and nature of the incident. It can also automate certain remediation tasks if configured properly. - Post-incident reviews will benefit from insights provided by 'autosre-ai', which can help in understanding the root causes more deeply and suggesting improvements to prevent future occurrences.
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