AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate suspicion due to high obfuscation risk, despite low scores in other categories. The single package by the author adds a layer of uncertainty.
- High obfuscation risk
- Single package by the author
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
- Obfuscation: The code snippet shows obfuscated import statements which may be used to hide functionality and intentions of the code.
- Credentials: No clear patterns for harvesting credentials or secrets were detected.
- Metadata: The author has only one package on PyPI, which may indicate a new or less active maintainer, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
lambda p, c: [__import__( "agentegrity.layers.adversarial", fromlist=["ThreatAssessment"] ).ThreatAssessment( channel="test
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository cogensec/agentegrity-framework appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Cogensec Research" 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 agentegrity
Create a mini-application that utilizes the 'agentegrity' package to ensure the integrity of autonomous chatbots in a multi-agent environment. This application should allow users to upload their chatbot models and run them through a series of integrity tests based on the criteria provided by the 'agentegrity' package. The app should include the following features: 1. User Interface: A simple web interface where users can upload their chatbot models. 2. Integrity Testing: Implement tests to evaluate if the chatbots are adversarially coherent, environmentally portable, and verifiably assured. 3. Reporting: Provide detailed reports after each test run, highlighting strengths and weaknesses of the chatbot's integrity. 4. Continuous Improvement: Allow users to iterate on their chatbot models based on feedback from the integrity tests. 5. Integration: Ensure the application integrates seamlessly with popular machine learning frameworks such as TensorFlow or PyTorch. The 'agentegrity' package will be utilized throughout the development process to enforce integrity standards during the testing phase and to generate verifiable proofs of adherence to these standards. Users should be able to download these proofs for further analysis or verification purposes.