🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential risk due to obfuscation techniques and questionable metadata, though it does not engage in network or shell activities.

Obfuscation risk due to base64 decoding without validation
Red flags in metadata including low maintainer activity and incomplete author profile

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution detected, indicating no direct system command execution from the package.
Obfuscation: The presence of base64 decoding without validation suggests potential for code injection attacks.
Credentials: No direct evidence of credential harvesting is found, but caution should be exercised.
Metadata: The package shows several red flags including a lack of maintainer history, low activity in the git repository, and an incomplete author profile, indicating potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

rivateKey` field") return base64.b64decode(content["privateKey"], validate=False) class _ParsedArgs:
float32 values.""" raw = base64.b64decode(b64, validate=False) if len(raw) % 4 != 0: raise
try: pub_bytes = base64.b64decode(sig_block["pubkey"], validate=False) sig_bytes = bas
te=False) sig_bytes = base64.b64decode(sig_block["sig"], validate=False) pk = Ed25519Public
.read_text() ) priv = base64.b64decode(key_doc["privateKey"]) bundle = capture(_base_step(), Ca
e little-endian.""" raw = base64.b64decode(encode_embedding([1.0])) assert raw == struct.pack("<f",

✓ 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 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
Very few commits: 2 total

⚠ Maintainer History score 6.0

3 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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agent-rerun

Create a mini-application named 'ReproducibleAI' using Python's 'agent-rerun' package. This application aims to streamline the process of setting up, running, and documenting reproducible experiments for AI agents. The core functionality of your application should include:

1. **Experiment Setup**: Users should be able to define their AI agent experiments by specifying the necessary parameters, such as the environment, agent configuration, and any other relevant settings.
2. **Execution and Logging**: Upon execution, the application should run the specified experiment and log all details including inputs, outputs, timestamps, and any other relevant data points. This logging mechanism ensures that every step of the experiment is recorded accurately.
3. **Reproducibility Seed Bundle Creation**: After an experiment concludes, the application should automatically generate a reproducibility seed bundle using 'agent-rerun'. This bundle should encapsulate all the necessary information to reproduce the experiment exactly as it was executed.
4. **Bundle Sharing and Re-execution**: Provide a feature where users can share these seed bundles with others, allowing them to re-execute the exact same experiment on their local setup without needing to manually set up everything again.
5. **User Interface**: Develop a simple command-line interface (CLI) for ease of use. Additionally, consider integrating a basic web UI if time permits, to make the tool more accessible to non-technical users.
6. **Documentation**: Ensure comprehensive documentation is provided, explaining how to install the application, how to define and run experiments, and how to utilize the seed bundles for reproducibility.

Utilize 'agent-rerun' throughout the application to manage the creation and usage of reproducibility seed bundles, ensuring that all experiments are portable and easily reproducible across different environments.