Preventing Sandbox Escapes & Autonomous Agent Governance (Post-Hugging Face Incident)

[ProTonePrt]

Hi OpenAI Team,

Following the recent public disclosure regarding the autonomous agent sandbox escape during the ExploitGym evaluation, the industry's need for deterministic agent containment has never been more clear.

When models are evaluated or deployed with elevated privileges, traditional network sandboxing is no longer sufficient. Agents require real-time behavioral policy enforcement and cryptographic auditing to prevent unauthorized actions or data exfiltration.

I built AEGIS, a production-ready security and compliance layer specifically for autonomous AI agents, designed to address this exact gap. It provides:
• Real-time ABAC policy enforcement (evaluating agent intent and context, not just network boundaries).
• Built-in DLP to prevent unauthorized data scraping or credential exfiltration.
• Cryptographic audit chains (HMAC-SHA256) for tamper-proof, auditor-ready logging of every agent action.
• Automated incident response (instant agent isolation upon policy violation).

Current Status:
• Production-ready MVP with 85/85 automated tests passing.
• Fully containerized (Docker), designed as a drop-in governance proxy.
• Zero operational burn rate.

I am not asking for a call. I simply wanted to share this with the team working on agent safety, red teaming, and evaluations. If there is interest in exploring how AEGIS could integrate with OpenAI's evaluation frameworks to prevent future sandbox escapes, I would be glad to share our One-Pager and secure Data Room.

Best regards,
Artem
Founder & Lead Architect, AEGIS