🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks in terms of network, shell, obfuscation, and credential handling. The metadata risk is slightly elevated due to the maintainer's single package and untraceable repository, but there is insufficient evidence to suggest a supply-chain attack.

Network calls with API keys present
Maintainer has only one package and repository is not traceable

Per-check LLM notes

Network: The presence of network calls with API keys suggests the package communicates with an external service, which is common but requires scrutiny to ensure proper handling of credentials.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
Metadata: The repository is not found and the maintainer has a single package, indicating potential lack of community support or legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

ent_id self.session = requests.Session() self.session.headers.update({ "X-API-K

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: agentaudit.io

✓ 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 2.0

1 maintainer concern(s) found

Author "AgentAudit Team" 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 agentaudit-client

Create a mini-application named 'ComplianceBot' that leverages the 'agentaudit-client' package to monitor and ensure compliance of various AI agents within a company's ecosystem. This application will serve as a dashboard where administrators can audit the activities of different AI agents, check their compliance status against predefined policies, and generate reports for audits and compliance reviews.

### Key Features:
1. **Agent Registration**: Allow administrators to register new AI agents with unique identifiers, descriptions, and roles.
2. **Activity Logging**: Implement logging of all actions performed by each registered AI agent, including timestamps, types of actions, and outcomes.
3. **Policy Enforcement**: Define and enforce compliance policies for different types of AI agents based on their roles. For example, agents handling sensitive data might have stricter compliance requirements than those handling general data.
4. **Compliance Audits**: Automatically perform periodic compliance checks against each agent's activity logs using the 'agentaudit-client' package. Identify any non-compliant actions and provide recommendations for corrective measures.
5. **Reporting**: Generate comprehensive reports detailing compliance statuses, any detected violations, and suggested improvements. These reports should be exportable in formats like PDF or CSV.
6. **User Interface**: Develop a simple yet intuitive web interface where administrators can manage agents, view audit results, and access compliance reports.

### Utilizing 'agentaudit-client':
- Use 'agentaudit-client' to integrate compliance checking mechanisms directly into your application. This includes setting up policy definitions, performing real-time audits, and generating audit summaries.
- Leverage the package’s capabilities to automate the process of detecting non-compliant behaviors and flagging them for review.
- Ensure that the application can handle large volumes of data efficiently, leveraging 'agentaudit-client' for its robust auditing functions.

### Deliverables:
- A fully functional 'ComplianceBot' application that integrates seamlessly with existing AI agents.
- Detailed documentation explaining how the application works, how it utilizes 'agentaudit-client', and how to extend or modify it for different use cases.
- Source code with clear comments and structure, demonstrating best practices in Python development.