aegis-atv

v0.7.0 suspicious
4.0
Medium Risk

Aegis ATV — Agent Telemetry Vector. Action firewall + cryptographic audit chain + ContextMemory analytics for AI agents.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some level of obfuscation and has metadata risks due to the maintainer's account status, suggesting potential hidden intentions or lack of transparency.

  • Potential obfuscation through base64 encoding
  • New or inactive maintainer account
Per-check LLM notes
  • Obfuscation: The code snippet shows potential obfuscation through base64 encoding of nonce and ciphertext, which is common but could also indicate an attempt to hide logic or data.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The maintainer has a new or inactive account with limited package history and lacks a proper author name.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • wrong key.""" nonce = base64.b64decode(wrapper["nonce"]) ciphertext = base64.b64decode(wrap
  • nonce"]) ciphertext = base64.b64decode(wrapper["ciphertext"]) aad_fields = { k:
✓ 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: github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository happyikas/Aegis-ATV 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 aegis-atv 
Develop a Python-based mini-application named 'AI-Agent-Sentry' that leverages the 'aegis-atv' package to ensure secure and transparent communication between different AI agents within a network. The application should monitor and log all interactions between these agents, apply real-time security checks using an action firewall, and provide a comprehensive audit trail for each interaction. Additionally, it should analyze the context of these interactions to identify potential security threats or anomalies in behavior patterns.

Steps to develop the application:
1. Install the 'aegis-atv' package via pip.
2. Define the structure of your AI agents, ensuring they adhere to a standard format for communication.
3. Implement an action firewall within 'AI-Agent-Sentry' that filters out any unauthorized commands or data exchanges between agents.
4. Utilize the cryptographic audit chain feature of 'aegis-atv' to create a tamper-proof record of all agent interactions.
5. Integrate ContextMemory analytics from 'aegis-atv' to analyze the context of each interaction, flagging any suspicious activities.
6. Develop a user-friendly interface that allows users to view logs, audit trails, and analysis reports generated by 'AI-Agent-Sentry'.

Suggested Features:
- Real-time alerting system for detected security breaches.
- Customizable rule sets for the action firewall based on specific security policies.
- Historical analysis tools to review past interactions and detect trends or patterns over time.
- Integration with popular logging frameworks like Logstash or Splunk for centralized log management.

The 'aegis-atv' package will be utilized throughout the development process to ensure robust security measures are in place, providing a reliable and secure environment for AI agent interactions.