🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its low-effort metadata and lack of community engagement, which raises concerns about its legitimacy and potential for misuse.

Metadata risk of 7/10
Lack of community engagement

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows several low-effort indicators and lack of community engagement, suggesting potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: 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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agent-strike

Create a security testing tool named 'AgentGuard' using the Python package 'agent-strike'. This tool will simulate offensive security scenarios against language models to identify vulnerabilities. Here’s how you can develop it:

1. **Setup Environment**: Begin by setting up your development environment with Python 3.x and installing the necessary packages including 'agent-strike' once it becomes available.

2. **Core Functionality**: Implement the core functionality of 'AgentGuard' which includes launching simulated attacks on various language models to test their robustness against adversarial inputs. Utilize 'agent-strike' to generate these adversarial inputs effectively.

3. **Attack Vectors**: Define different types of attack vectors such as injection attacks, evasion attacks, and poisoning attacks. Each vector should be designed to test specific weaknesses in the language models.

4. **Report Generation**: Develop a feature that generates comprehensive reports after each test session. These reports should include details about the attack vectors used, the responses from the language models, and any identified vulnerabilities.

5. **User Interface**: Create a simple yet effective user interface where users can select the type of tests they want to run, input the target language model, and view the results.

6. **Customization Options**: Allow users to customize their attack strategies by specifying parameters like the complexity of the adversarial inputs, the frequency of attacks, and the types of responses they expect from the language models.

7. **Integration with Other Tools**: Explore ways to integrate 'AgentGuard' with other security tools or platforms, making it easier for security professionals to incorporate it into their existing workflows.

8. **Documentation and Support**: Provide thorough documentation on how to use 'AgentGuard', including setup instructions, usage guides, and FAQs. Also, establish a support system for users who encounter issues or need assistance.

By following these steps, you’ll create a powerful and versatile tool that leverages the capabilities of 'agent-strike' to enhance the security of language models.