🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its obfuscated code and network activity, although it lacks clear evidence of malicious intent. Further scrutiny is advised.

moderate obfuscation risk
network risk

Per-check LLM notes

Network: The package makes network calls which could be legitimate for functionality but may also indicate potential data exfiltration or C2 activities.
Shell: No shell execution patterns detected, indicating low risk of direct system command execution.
Obfuscation: The code uses base64 decoding and JSON loading which may indicate an attempt to hide functionality or logic, suggesting a moderate risk of obfuscation.
Credentials: No clear patterns for harvesting credentials were detected, but further investigation is needed to rule out subtle credential handling.
Metadata: The package shows signs of potential inactivity and lack of community support, raising suspicion.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

9 test file(s) detected (e.g. test_agentpay_sdk.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (11804 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

25 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 100 commits in romudille-bit/agentpay
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

let.public_key} with httpx.Client(timeout=60.0) as client: # ── First request — n
list): resp = httpx.post( self.BASE_RPC_URL,
try: resp = httpx.get(BAZAAR_SEARCH_URL, params=params, timeout=10.0)
dow.append(now) with httpx.Client(timeout=60.0) as client: # ── First request — pr
try: resp = httpx.get(f"{self.gateway_url}/tools/{tool_name}", timeout=5.0)
try: resp = httpx.get(f"{self.gateway_url}/tools", timeout=5.0) if res

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

decoded = json.loads(base64.b64decode(header)) assert decoded["x402Version"] == 2
decoded = json.loads(base64.b64decode(header)) assert "outputSchema" not in decoded["accep
decoded = json.loads(base64.b64decode(header)) assert isinstance(decoded, dict) as
future_iso = ( __import__("datetime").datetime.now( tz=__import__("datetime").tim
etime.now( tz=__import__("datetime").timezone.utc ) + __import__("datetime").timedel
.timezone.utc ) + __import__("datetime").timedelta(seconds=60) ).isoformat() async d

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 agentpay-x402

Create a financial advisor chatbot named 'EcoBot' using the Python package 'agentpay-x402'. EcoBot should serve as a personal economic intelligence tool, helping users manage their finances more effectively through interactive conversations. The chatbot will leverage 'agentpay-x402' to offer real-time financial advice, track spending, set and monitor budgets, and provide session summaries at the end of each interaction. Here’s a breakdown of the project requirements:

1. **Setup**: Begin by installing the 'agentpay-x402' package and setting up a basic chatbot framework. Ensure you have a way to authenticate users securely.
2. **Quickstart Integration**: Utilize the `quickstart()` function from 'agentpay-x402' to initialize the bot's economic intelligence capabilities. This should be done in one line of code as per the package documentation.
3. **User Interface**: Design a simple text-based interface where users can interact with EcoBot. The interface should allow users to input commands such as 'check balance', 'set budget', 'track expenses', etc.
4. **Financial Advice**: Implement a feature where EcoBot can analyze the user's current financial situation and provide personalized advice based on the data retrieved through 'agentpay-x402'. This includes suggestions on saving money, investing, and managing debt.
5. **Budget Management**: Use 'agentpay-x402' to enforce hard budget caps for users. If a user exceeds their budget, EcoBot should alert them immediately and suggest ways to cut back.
6. **Session Receipts**: At the end of each session, EcoBot should generate a receipt summarizing the session's activities, including any financial transactions made, advice given, and overall financial health status.
7. **Integration with Stellar or Base**: For advanced users, integrate EcoBot with Stellar or Base for more sophisticated financial operations. Users should be able to perform basic financial transactions within the chatbot interface.
8. **Testing & Deployment**: Thoroughly test the chatbot with various scenarios to ensure reliability and accuracy. Once tested, deploy the chatbot to a platform where it can be accessed by users.

By following these steps, you'll create a comprehensive financial management tool that leverages the powerful features of 'agentpay-x402' to enhance users' financial literacy and management skills.