AI Analysis
Final verdict: SUSPICIOUS
The package exhibits multiple high-risk behaviors including potential shell injection and credential harvesting, indicating a significant security concern.
- use of 'shell=True' leading to potential shell injection
- accessing sensitive system files like /etc/passwd and /etc/shadow
Per-check LLM notes
- Network: The use of urllib for network calls is common but could be indicative of data exfiltration if the URL is not static and controlled.
- Shell: Use of 'shell=True' is risky as it can lead to shell injection attacks. This suggests potential for executing arbitrary commands and should be reviewed carefully.
- Obfuscation: The use of zlib.decompress suggests an attempt to obscure code logic or data, which is suspicious in the absence of clear documentation or cryptographic context.
- Credentials: Accessing system files like '/etc/passwd' and '/etc/shadow' indicates potential attempts to harvest sensitive information, which is highly suspicious.
- Metadata: The repository's recent creation and low activity suggest potential risk, but there's no concrete evidence of malicious intent.
Package Quality Overall: Medium (5.2/10)
✦ High
Test Suite
9.0
Test suite present — 4 test file(s) found
Test runner config found: pyproject.toml4 test file(s) detected (e.g. test_api_watch.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (18226 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
171 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 100 commits in dead-duke/arachnaSingle author but highly active (100 commits)
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
with contextlib.closing(urllib.request.urlopen(url, timeout=10)) as response: data = js
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
o raw try: data = zlib.decompress(raw) except zlib.error: data = raw # Verify
Shell / Subprocess Execution
score 10.0
Found 5 shell execution pattern(s)
s_shell: result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=30)eturn "" result = subprocess.run(args, capture_output=True, text=True, timeout=30) _ll metacharacters that require shell=True _SHELL_CHARS = {"|", "&", ";", "<", ">", "$", "`", "(", ")"no shell injection). Uses shell=True for commands with shell metacharacters (|, ||, &&, etc).result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=30) else:
Credential Harvesting
score 5.0
Found 2 credential access pattern(s)
:|:& };:", # fork bomb "/etc/passwd", "/etc/shadow", ] def _resolve_base(cmd_part: str) -bomb "/etc/passwd", "/etc/shadow", ] def _resolve_base(cmd_part: str) -> str: """Extra
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 5.0
Git history flags: Repository created very recently: 7 day(s) ago (2026-05-31T10:56:48Z)
Repository created very recently: 7 day(s) ago (2026-05-31T10:56:48Z)Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Artem Terenin" 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 arachna
Create a mini-application called 'CodeSummarizer' that leverages the 'arachna' Python package to summarize code projects into digestible chunks for AI analysis. The application should accept a directory path as input, which contains various source code files of a software project. Using 'arachna', the app will gather these files, break them down into contextually relevant chunks that fit within a specified token limit (e.g., 1024 tokens), and then provide summaries of each chunk. These summaries should highlight key components such as function definitions, variable declarations, and any comments that might provide additional context about the code's purpose or functionality. Additionally, implement a feature that allows users to query specific parts of the summarized content based on keywords or file names. Ensure the application includes a user-friendly interface where users can easily navigate through the summaries and queries. Use 'arachna' effectively to maintain the contextual integrity of the code snippets while ensuring they remain within the token constraints suitable for AI processing.