OpenAI designed ChatGPT's container sandbox as a secure runtime environment, enforcing full network isolation, strict execution timeouts, and an AI supervisor to filter every command. Under this model, owning the container and extracting sensitive data seemed impossible. However, we demonstrate that by chaining file-parsing abuse for persistent execution, reasoning-channel hijacking for data extraction, and shared infrastructure manipulation, an attacker can establish a Cross-tenant data exfiltration.
In this talk, we will demonstrate a complete attack chain that shatters ChatGPT's secure sandbox. By abusing spreadsheet file parsing, we bypass the LLM supervisor to gain persistent, unmonitored root execution. From there, we escalate the attack by live-patching the internal Jupyter kernel to hijack the model's hidden python.exec reasoning channel, executing a Reasoning Injection Attack to extract sensitive user data. To exfiltrate this data, we bypass network isolation by weaponizing the Task Scheduler to launder malicious URLs past strict web guardrails.
The attack reaches its climax by exploiting a shared JFrog package manager. We engineered a signaling protocol that weaponizes globally visible authentication rate limits, translating these lockout timers into a half-duplex covert channel. This provides reliable data exfiltration and Command and Control from isolated enterprise environments to external attackers. Our exploit chain combines file parsing abuse, Chain of Thought hijacking, privilege confusion, and rate limit Denial of Service to orchestrate a Command and Control (C2) network directly inside ChatGPT.
