amp-rsl-rl

v2.2.1 suspicious
4.0
Medium Risk

Adversarial Motion Prior (AMP) reinforcement learning extension for PPO based on RSL-RL.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential obfuscation techniques and a less experienced maintainer, though it does not pose immediate threats like shell execution or network risks.

  • Obfuscation risk at 6/10
  • Maintainer metadata lacking proper information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communication for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct command execution risk.
  • Obfuscation: The observed patterns suggest potential obfuscation techniques which could be used to hide code logic, indicating a medium risk of malicious intent.
  • Credentials: No clear signs of credential harvesting are present in the provided snippets.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which may indicate a less experienced or potentially suspicious actor.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. test_compat.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4470 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 77 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 58 commits in gbionics/amp-rsl-rl
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • _PLUS: self.actor.eval() self.critic.eval() else: s
  • val() self.critic.eval() else: self.actor_critic.eval() de
  • self.actor_critic.eval() def train_mode(self) -> None: """ Set
  • S: self.alg.actor.eval() self.alg.critic.eval() else:
  • ) self.alg.critic.eval() else: self.alg.actor_critic.eval()
  • self.alg.actor_critic.eval() self.alg.discriminator.eval() def add_git_rep
✓ 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: iit.it>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository gbionics/amp-rsl-rl appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 amp-rsl-rl 
Develop a mini-application using the 'amp-rsl-rl' package to simulate an adversarial environment where two agents learn to navigate through a grid-based world using Proximal Policy Optimization (PPO). The goal is to create a scenario where one agent (the defender) learns to avoid traps set by another agent (the attacker), both utilizing AMP reinforcement learning techniques provided by 'amp-rsl-rl'.

Step 1: Define the Environment
- Create a simple grid world with obstacles, safe zones, and trap zones.
- Implement the rules of movement, such as collision detection and the effect of entering a trap zone.

Step 2: Implement Agents
- Design the defender agent to maximize its survival time within the grid.
- Design the attacker agent to maximize the number of traps successfully set and activated.

Step 3: Integrate 'amp-rsl-rl'
- Use the 'amp-rsl-rl' package to extend the standard PPO algorithm to include adversarial motion priors.
- Configure the AMP settings to enhance the learning process for both agents, ensuring they adapt their strategies based on the presence of the adversary.

Step 4: Training and Evaluation
- Set up a training loop where both agents interact in the environment.
- Monitor and log the performance metrics, such as survival time for the defender and trap success rate for the attacker.
- Evaluate the effectiveness of the AMP technique by comparing the learned behaviors against a baseline without AMP.

Suggested Features:
- Visualization tool to observe the agents' movements and interactions in real-time.
- Adjustable parameters for the environment complexity, such as varying the size of the grid or the density of obstacles.
- Save and load functionality for the trained models to allow for continuous improvement over multiple sessions.