🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high credential risk and lacks comprehensive metadata, raising suspicion of potential malicious intent despite legitimate-looking network communications.

High credential risk due to potential harvesting attempts
Sparse metadata and missing repository details

Per-check LLM notes

Network: The network calls suggest legitimate communication with an external service, possibly for registration, authorization, fetching decisions, and initiating scans.
Shell: No shell execution patterns were detected.
Obfuscation: No signs of obfuscation techniques being used.
Credentials: Potential credential harvesting attempt observed with suspicious file paths and tokens.
Metadata: The missing repository and sparse maintainer information raise concerns, but there's no direct evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

try: r = httpx.post( f"{self.url}/agents/register",
try: r = httpx.post( f"{self.url}/authorize", he
try: r = httpx.get( f"{self.url}/decisions/{request_id}",
re gate.scan()") r = httpx.post( f"{self.url}/scan", headers=self._h
try: async with httpx.AsyncClient(headers=self._headers, timeout=self.timeout) as client:

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

"child_agent_id": "../../etc/passwd", "child_name": "Bad", "child_decla
"resource": "/reports/../../../etc/passwd", "token": "tok-trav", }) asser

✓ 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 agentgate-pdp

Create a mini-application named 'TrustyBot' that leverages the 'agentgate-pdp' Python package to manage trust levels between different AI agents in a simulated environment. TrustyBot should simulate a network of AI agents where each agent has its own set of permissions and trust levels based on their interactions and context-awareness.

The application should include the following features:
1. **Agent Registration**: Allow users to register new AI agents with unique identifiers and initial trust levels.
2. **Contextual Trust Evaluation**: Implement a system where agents can request actions from other agents. The 'agentgate-pdp' package will evaluate whether the requesting agent has sufficient trust to perform the requested action based on contextual information provided.
3. **Dynamic Trust Adjustment**: Introduce a mechanism where trust levels can dynamically adjust based on successful or failed interactions. For example, if an agent successfully completes a task for another agent, its trust level increases; if it fails, its trust decreases.
4. **Logging and Reporting**: Keep a log of all interactions, including requests, responses, and trust adjustments. Provide a reporting feature that summarizes the trust levels and interactions over time.
5. **User Interface**: Develop a simple web-based user interface using Flask or Django that allows users to view and interact with the agents and their trust levels.

Instructions for utilizing the 'agentgate-pdp' package:
- Use 'agentgate-pdp' to define policies that determine under what conditions one agent can trust another to perform certain actions.
- Implement the policy decision point (PDP) functionality provided by 'agentgate-pdp' to evaluate these policies in real-time as agents request actions from one another.
- Ensure that your application can handle updates to policies and trust levels efficiently, leveraging the dynamic capabilities of 'agentgate-pdp'.

Your goal is to create a fully functional mini-application that demonstrates the power of 'agentgate-pdp' in managing trust and permissions within a network of AI agents.