🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning behaviors including network and credential risks, despite being low overall. The presence of an external API call and interaction with AWS services without clear documentation raises suspicion.

Network risk due to external API calls
Potential AWS service interaction

Per-check LLM notes

Network: The network calls to an external API suggest the package may be communicating with a server, which could be legitimate if documented. However, further investigation is needed to confirm its purpose.
Shell: No shell execution patterns were detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected in the provided snippet.
Credentials: The code checks for an AWS environment indicating potential interaction with AWS services but does not inherently imply credential harvesting.
Metadata: Low risk due to lack of suspicious elements, but new maintainer and low metadata quality warrant caution.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

75 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 score 3.0

Found 2 network call pattern(s)

try: resp = requests.post( f"{GATEFRAME_API}/agentseal/sign",
one: try: requests.post( f"{GATEFRAME_API}/agentseal/sign",

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

RL IS_SQLITE = False elif os.getenv("AWS_LAMBDA_FUNCTION_NAME"): # Lambda without DATABASE_URL —

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gateframe.io

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Shankar Anand" 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 agentseal-gateframe

Develop a mini-application named 'CryptoEvidenceKeeper' that leverages the 'agentseal-gateframe' Python package to create a secure, tamper-proof log of decisions made by an AI agent. This application will serve as a tool for developers and researchers who want to ensure that their AI agents' actions are verifiable and trustworthy. Here’s a detailed breakdown of what your application should include:

1. **User Interface**: Design a simple yet intuitive user interface where users can input the decision details made by an AI agent.
2. **Decision Logging**: Implement functionality to log each decision made by the AI agent along with the timestamp and any relevant metadata.
3. **Cryptographic Evidence Generation**: Utilize the 'agentseal-gateframe' package to generate cryptographic evidence receipts for each logged decision. These receipts should serve as proof that the decision was indeed made by the AI agent at the specified time.
4. **Verification Tool**: Include a feature that allows users to verify the authenticity of the cryptographic evidence receipts using the 'agentseal-gateframe' package.
5. **Database Integration**: Store all decision logs and their corresponding cryptographic evidence receipts in a local SQLite database for easy retrieval and verification.
6. **Export Functionality**: Provide an option for users to export the verified decision logs and cryptographic evidence receipts into a CSV file for further analysis or sharing.
7. **Security Measures**: Ensure that the application handles sensitive data securely and adheres to best practices for cryptographic operations.
8. **Documentation**: Write comprehensive documentation detailing how to install and use the 'CryptoEvidenceKeeper' application, including setup instructions and examples of how to integrate it with existing AI systems.

The goal of this project is to demonstrate the practical application of cryptographic evidence receipts in ensuring the integrity and transparency of AI agent decision-making processes.