aria-x

v1.6.1 suspicious
6.0
Medium Risk

ARIA — Autonomous Reasoning and Intelligent Agent. Writes code AND proves it works — verified end-to-end.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks, primarily due to network and shell execution behaviors that lack clear justification in the documentation. While not conclusively malicious, these actions warrant caution.

  • High network risk
  • Significant shell execution risk
Per-check LLM notes
  • Network: The network calls suggest the package is making external requests which could potentially be for legitimate purposes like fetching specs, but without clear documentation, it raises suspicion.
  • Shell: Executing shell commands including git operations can be part of a development workflow, yet running 'git init' directly via shell poses a significant risk due to potential command injection vulnerabilities.
  • Obfuscation: The obfuscation detected seems to be a simple way to mask the datetime import, which is not indicative of malicious activity.
  • Credentials: The use of getpass indicates an attempt to securely prompt for user input, possibly for credentials, but this could also be misused for credential harvesting without proper context.
  • Metadata: The author information is incomplete and the maintainer has only one package, which may indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Low (4.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9527 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 144 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 32 commits in Lonerider007/aria-agent
  • Single author but highly active (32 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • import datetime req = urllib.request.Request(target, headers={"User-Agent": "ARIA/1.6 (spec-fetch
  • (spec-fetch)"}) with urllib.request.urlopen(req, timeout=20) as resp: raw = resp.rea
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • in, "fetched_at": __import__("datetime").datetime.now().isoformat(), "digest": digest,
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • try: result = subprocess.run( cmd, capture_output=True, text=True, timeou
  • tcwd() try: add = subprocess.run(["git", "add", "-A"], cwd=wd, capture_output=True, text=True
  • rr.strip()}" commit = subprocess.run( ["git", "commit", "-m", message or "update"],
  • nts=True, exist_ok=True) subprocess.run("git init", shell=True, cwd=workspace, capture_output=True)
  • fastapi", "django"]): subprocess.run("python3 -m venv .venv", shell=True, cwd=workspace, capture_
  • ess", "typescript"]): subprocess.run("npm init -y", shell=True, cwd=workspace, capture_output=Tru
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • import getpass return getpass.getpass(prompt) sys.stdout.write(prompt) sys.stdout.flush()
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Lonerider007/aria-agent appears legitimate

⚠ 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 aria-x 
Create a fully-functional mini-app using the 'aria-x' package, which focuses on autonomous reasoning and intelligent agent capabilities. Your application should serve as a personal task manager that not only schedules tasks but also autonomously reasons about task dependencies and optimizes task execution based on real-time data inputs.

Steps to develop the app:
1. Define the structure of a task, including attributes such as title, description, due date, priority level, and dependencies.
2. Implement a scheduling algorithm that takes into account task dependencies and priority levels to create an optimized task schedule.
3. Utilize 'aria-x' to enable the app to reason about changes in task dependencies or priorities and adjust the schedule accordingly.
4. Integrate a feature that allows the app to receive real-time updates (e.g., weather conditions affecting travel time) and adjust task execution times based on these updates.
5. Ensure that the app can prove the correctness of its scheduling decisions through verification mechanisms provided by 'aria-x'.

Suggested Features:
- User interface for adding, editing, and deleting tasks.
- Notifications for upcoming tasks and missed deadlines.
- Integration with calendar applications for seamless task synchronization.
- Real-time data integration for external factors impacting task completion.
- Detailed logs of all scheduling decisions and adjustments made by the app.

How 'aria-x' is utilized:
- For autonomous reasoning about task dependencies and priority adjustments.
- For intelligent decision-making regarding task scheduling optimizations.
- For verifying the correctness and efficiency of the scheduling algorithms implemented.

💬 Discussion Feed

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