agentguard-spend

v0.15.2 suspicious
6.0
Medium Risk

All terminology and labels used in AgentGuard materials are descriptive of software functionality only, not legal definitions or guarantees of compliance. Terms like receipt, audit log, evidence, audit trail, and attestation refer solely to cryptographically-signed records produced by the software. Full functional-use disclaimer in README.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to network and metadata concerns, along with high obfuscation risk. These factors collectively raise suspicion about its legitimacy and intent.

  • High obfuscation risk
  • Moderate network and metadata risks
Per-check LLM notes
  • Network: The network calls suggest the package may be communicating with external endpoints, which could indicate legitimate functionality but also potential for data exfiltration.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code uses base64 decoding for key caching and evaluation, which may indicate an attempt to hide logic or data, raising suspicion.
  • Credentials: No clear evidence of credential harvesting patterns, but the use of base64 decoding could potentially be used for hiding sensitive information.
  • Metadata: The repository not being found and the author's lack of details suggest potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • s(payload).encode() req = urllib.request.Request(url, data=data, headers={"content-type": "applicatio
  • on"}, method="POST") with urllib.request.urlopen(req, timeout=5) as response: body = response
  • AULT_ENDPOINT)) req = urllib.request.Request(endpoint, headers={"accept": "application/json"})
  • lication/json"}) with urllib.request.urlopen(req, timeout=5) as response: catalog = _
  • encode("utf-8") request = urllib.request.Request( _BEACON_URL, data=payload,
  • POST", ) try: urllib.request.urlopen(request, timeout=1).close() except Exception:
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • self._public_key_cached = base64.b64decode(pk_b64) return self._public_key_cached async de
  • it(":", 1)[-1] return base64.b64decode(b64) """ PostgreSQL-backed SpendStore + DecisionLogStore.
  • result = await self._r.eval( """ local current = tonumber(redis.
✓ 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: agentguard.run>

✓ 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 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 agentguard-spend 
Create a secure financial tracker application using the 'agentguard-spend' Python package. This application will help users manage their daily expenses while ensuring the integrity and traceability of each transaction through cryptographic methods provided by 'agentguard-spend'. The app should allow users to input and categorize their spending, generate detailed reports, and maintain an immutable record of all transactions.

### Features:
- **Transaction Input**: Users can enter details of their expenses including date, amount, category (e.g., groceries, utilities), and description.
- **Categorization**: Automatically categorize expenses based on predefined categories, but also allow manual adjustment by the user.
- **Report Generation**: Provide users with monthly and yearly expense summaries, categorized breakdowns, and trends over time.
- **Audit Trail**: Utilize 'agentguard-spend' to create a tamper-proof audit trail for each transaction, ensuring that once a transaction is recorded, it cannot be altered without detection.
- **Security Enhancements**: Implement additional security measures such as encryption for sensitive data and two-factor authentication for accessing the application.

### Steps to Build the Application:
1. **Setup Environment**: Install Python and necessary libraries including 'agentguard-spend'.
2. **User Interface Design**: Develop a simple yet effective UI for entering and viewing transactions.
3. **Transaction Handling**: Integrate 'agentguard-spend' to handle the creation of cryptographically signed receipts for each transaction entry.
4. **Data Storage**: Use SQLite or another database system to store transaction data securely.
5. **Reporting Module**: Implement modules to generate reports based on user-defined periods and categories.
6. **Audit Trail Management**: Ensure every transaction has an associated audit log entry that is immutable thanks to 'agentguard-spend'.
7. **Testing and Deployment**: Test the application thoroughly and deploy it on a server or cloud platform.
8. **Documentation**: Provide comprehensive documentation explaining how to use the application and the role of 'agentguard-spend' in securing financial data.