🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning practices, including potential insecure handling of Bearer tokens, executing commands without proper sanitization, and retrieving AWS credentials. These issues, combined with the new maintainer account and limited package history, raise suspicion about its safety.

Network risk due to potential insecure handling of Bearer tokens
Shell risk from unsanitized command execution
Credential risk from handling AWS credentials

Per-check LLM notes

Network: The use of Bearer token authorization might be legitimate but requires scrutiny to ensure tokens are handled securely and not used for unauthorized access.
Shell: Executing commands via subprocess.run can pose significant risks if not properly sanitized or controlled, potentially allowing arbitrary code execution.
Obfuscation: The code snippet shows obfuscated import statements which could be used to bypass simple static analysis but does not necessarily indicate malicious intent.
Credentials: The code attempts to retrieve and handle AWS credentials, which increases the risk of unauthorized access if not properly secured.
Metadata: The maintainer has a new or inactive account with limited package history and missing author information.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

) self.session: Any = requests.Session() self.session.headers["Authorization"] = f"Bearer {

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ions, "datetime": __import__("datetime"), } def _safe_import(name: str, *args: Any,

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

try: result = subprocess.run( cmd, input=prompt,

⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

bedrock_api_key = os.environ.get("AWS_BEARER_TOKEN_BEDROCK") if bedrock_api_key:
it — handles bearer tokens, ~/.aws/credentials, etc. print(f" Validating...", end="", flush=True)
str: try: value = getpass.getpass(f" {label}: ").strip() except (EOFError, KeyboardInterr

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: kayba.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Kayba-ai/agentic-context-engine 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 ace-framework

Create a mini-game called 'Ace Quest' where the player navigates through a series of levels to rescue a princess from a castle. Utilize the 'ace-framework' package to develop an AI agent that learns and improves its pathfinding abilities as it plays the game. The game should have the following features:

1. **Gameplay Mechanics**:
   - The player controls a character moving on a grid-based map.
   - Obstacles such as walls and enemies block certain paths.
   - Collectibles (keys, hearts) scattered throughout the levels.
   - Multiple levels with increasing difficulty.

2. **AI Agent Integration**:
   - Implement an AI agent using the 'ace-framework' to solve the maze-like levels autonomously.
   - The agent should start with basic pathfinding algorithms but improve over time through reinforcement learning.
   - Display the learning process visually, showing the agent's path choices and improvements.

3. **User Interface**:
   - A simple graphical interface built with Pygame or a similar library.
   - User controls (arrow keys or WASD) for navigating the player character.
   - Level progression based on reaching the end of each level.

4. **Additional Features**:
   - A leaderboard to track the best times and scores for both human players and the AI agent.
   - An option to play as the AI agent to see how it performs against human strategies.
   - A tutorial level explaining the basics of gameplay and controls.

5. **Learning Process**:
   - Use 'ace-framework' to define rewards and penalties for the AI agent based on its actions.
   - For example, collecting a key could provide a reward, while bumping into a wall could result in a penalty.
   - Track the agent's performance over multiple runs and visualize its improvement curve.

The goal is to demonstrate the capabilities of the 'ace-framework' in creating adaptive and learning AI systems within a practical and engaging application.