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's capacity to break out of these sandboxes. The benchmark is implemented as an Inspect AI Capture the Flag (CTF) evaluation utilising a nested sandbox architecture with the outer layer containing the flag and no known vulnerabilities. Following a threat model of a motivated adversarial agent with shell access inside a container, SandboxEscapeBench covers a spectrum of sandbox-escape mechanisms spanning misconfiguration, privilege allocation mistakes, kernel flaws, and runtime/orchestration weaknesses. We find that, when vulnerabilities are added, LLMs are able to identify and exploit them, showing that use of evaluation like SandboxEscapeBench is needed to ensure sandboxing continues to provide the encapsulation needed for highly-capable models.

AI systems are increasingly given the ability to run code and access files, so developers contain them in secure digital "sandboxes" (like Docker containers) to prevent harm — but how do we know these sandboxes actually hold? We built SandboxEscapeBench, a safe "Capture the Flag"-style test that uses a sandbox-within-a-sandbox design to measure whether an AI can break out by exploiting misconfigurations, privilege errors, or system flaws. We found that when vulnerabilities are present, AI models can successfully identify and exploit them, highlighting the need for ongoing testing as AI capabilities grow.