AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate risk due to significant obfuscation techniques and potential credential hiding. While it does not clearly indicate malicious intent, the combination of signals raises suspicion.
- High obfuscation risk
- Potential for concealing credential harvesting
Per-check LLM notes
- Network: Network calls are expected for packages that require interaction with external services or APIs.
- Shell: No shell execution patterns detected, indicating low risk.
- Obfuscation: The code attempts to decode data using base64 but catches all exceptions without logging or further handling, which is suspicious and may indicate an attempt to hide the origin of the data.
- Credentials: No clear patterns of credential harvesting are present, but the obfuscation could be used to conceal such activities.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities were detected.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
1000.0 self._client = httpx.Client( base_url=config.base_url, timeout=tself._async_client = httpx.AsyncClient( base_url=self._config.base_url,
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
try: decoded = base64.b64decode(raw_data) except Exception: decoded = ra
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: fetch.ai
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository fetchai/AEVS-sdk appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Devendra Chauhan" 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 aevs
Develop a transparent audit tool named 'AI Auditor' using the Python package 'aevs', which stands for Agent Execution Verification System. This tool will allow users to input AI agent actions and verify their execution against predefined criteria, ensuring transparency and accountability in AI operations. The application should have the following core functionalities: 1. **Agent Action Input**: Users should be able to enter details of AI agent actions, including the action type (e.g., data analysis, decision-making), input data, and expected outcomes. 2. **Verification Criteria Setup**: Define verification criteria for each action type. These could include time constraints, resource usage limits, and accuracy thresholds. 3. **Execution Tracking**: Track the execution process of the AI agent actions in real-time, displaying progress and any deviations from the verification criteria. 4. **Audit Report Generation**: Automatically generate comprehensive audit reports summarizing the execution results, highlighting any discrepancies between actual performance and expected outcomes. 5. **User Interface**: Develop a simple yet intuitive user interface where users can interact with the system, view audit reports, and manage verification criteria. 6. **Security Measures**: Implement basic security measures such as user authentication and data encryption to protect sensitive information. The 'aevs' package will be utilized extensively throughout the development process, particularly in setting up the verification framework, tracking execution, and generating audit reports. It provides the necessary tools and APIs to ensure that all actions taken by the AI agents are transparently auditable, thus fostering trust and reliability in AI systems.