🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks, particularly concerning obfuscation and credential handling, which could be exploited. However, there's no concrete evidence of malicious intent.

High obfuscation risk due to base64 decoding and exec() usage
Significant credential risk from environmental variable checks

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Subprocess execution is present but without clear malicious intent, suggesting moderate caution.
Obfuscation: Base64 decoding and exec() usage suggests potential for code injection or hiding malicious content.
Credentials: Environmental variable checks for tokens indicate possible unauthorized access to sensitive information.
Metadata: The package shows signs of low maintainer effort and lacks a linked GitHub repository, which raises some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (10671 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

458 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

import base64;" "exec(base64.b64decode(" f"{encoded!r}" ").decode('utf-8'))" )
"import base64;" "exec(base64.b64decode(" f"{encoded!r}" ").decode('utf-8

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

, enabled=debug) result = subprocess.run(command, check=False) debug_log_result(result, enabled=d
, enabled=debug) result = subprocess.run(command, check=False, capture_output=True, text=True) de
enabled=debug) process = subprocess.Popen(command, stdout=log_file, stderr=subprocess.STDOUT, env=env)
mote_addr) process = subprocess.Popen( command, stdin=subprocess.PIPE if i
try: result = subprocess.run( [multipass_command(), "transfer", staged_so
try: result = subprocess.run( [multipass_command(), "transfer", "{0}:{1}"

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

nv("AGSEKIT_GITHUB_TOKEN") or os.getenv("GITHUB_TOKEN") if token: headers["Authorization"] = f"Bearer

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: yandex.ru>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agsekit

Create a Python-based mini-application named 'SafeAgentMonitor' that leverages the functionalities of the 'agsekit' package to monitor and ensure the safety and security of agents operating within a network environment. This application will serve as a robust tool for administrators to manage and safeguard their agents effectively. Here are the steps and features to consider:

1. **Project Setup**: Initialize your project with a virtual environment and install the 'agsekit' package along with other necessary dependencies like 'requests' for making HTTP requests.
2. **Configuration Module**: Develop a configuration module where users can define settings such as agent IDs, monitoring intervals, and alert thresholds. These configurations will be stored in a YAML file for easy management.
3. **Monitoring Engine**: Implement a monitoring engine that periodically checks each agent’s status using 'agsekit'. This includes verifying if the agent is active, its last check-in time, and any anomalies detected during operation.
4. **Alert System**: Integrate an alert system that triggers notifications via email or SMS when an agent's status falls below predefined safety thresholds. Use the 'smtplib' library for sending emails.
5. **Reporting Interface**: Create a simple command-line interface (CLI) using 'argparse' that allows users to view real-time statuses, historical data, and generate reports on agent performance and safety metrics.
6. **Security Enhancements**: Ensure that all communication between the SafeAgentMonitor and the agents is encrypted. Utilize SSL/TLS protocols for secure data transmission.
7. **Testing & Documentation**: Write comprehensive tests for each module and provide detailed documentation explaining how to set up and use SafeAgentMonitor effectively.

The 'agsekit' package will primarily be used for executing commands and retrieving information about the agents. Your task is to creatively integrate these features into a cohesive application that enhances network security and operational efficiency.