AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risks in terms of network activity, shell execution, obfuscation, and credential harvesting. However, the absence of author information and a GitHub repository increases suspicion regarding its legitimacy.
- Missing author information
- Lack of GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some red flags, particularly the missing author information and lack of a GitHub repository, which raises concerns about its legitimacy.
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: accountability.ai>
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 name is missing or very shortAuthor "" 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 agdr-aki
Create a legal compliance tool using the Python package 'agdr-aki'. This tool will serve as a proof generator for AI-driven decisions made by a company's internal systems, ensuring these decisions meet the requirements of the EU AI Act and the Canada Evidence Act. The application should be able to generate court-admissible forensic records known as PPP Triplet (Process, Policy, Proof), which includes cryptographic proof to verify the integrity and authenticity of AI decision-making processes. Step-by-step functionality: 1. **Setup**: Initialize the application with the necessary configurations for connecting to the AI system and setting up the logging mechanism. 2. **AI Decision Recording**: Capture and log the inputs, outputs, and parameters of any AI-driven decision-making process within the company's systems. 3. **PPP Triplet Generation**: For each logged AI decision, generate a PPP Triplet record that includes: - Process: Detailed documentation of the AI process used to make the decision. - Policy: Relevant policies and regulations that were considered during the decision-making process. - Proof: Cryptographic proof that verifies the authenticity and integrity of the decision. 4. **Storage and Retrieval**: Store the PPP Triplet securely and provide an interface for retrieval and presentation of these records when needed. 5. **Audit and Compliance Check**: Implement an audit function that checks if the stored PPP Triplet meets the legal standards set by the EU AI Act and Canada Evidence Act. Suggested Features: - Integration with existing logging frameworks for seamless data capture. - User-friendly interface for generating and viewing PPP Triplet records. - Automated email notifications for new records or compliance issues. - Detailed reporting and analytics on compliance status and trends over time. - Secure storage solution that complies with data protection laws. Utilizing the 'agdr-aki' package: - Use the package's core functions to create cryptographic proofs for the AI decision-making processes. - Leverage the package's capabilities to ensure the PPP Triplet records are court-admissible and comply with legal standards. - Integrate the package's SDK into your application to streamline the generation and validation of forensic records.