arXiv:2603.02277v2 Announce Type: replace-cross Abstract: 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 neste
As LLMs are increasingly deployed as autonomous agents in sensitive environments, the need to quantify and mitigate their novel security risks, especially sandbox escapes, becomes critical.
A strategic reader should care because the security vulnerabilities of AI agents directly impact their trustworthiness, deployment scope, and the broader enterprise cybersecurity landscape.
The ability to formally benchmark and quantify LLM security vulnerabilities, particularly container escapes, shifts the focus from theoretical risks to measurable, actionable mitigation strategies.
- · Cybersecurity firms
- · AI red teaming specialists
- · Cloud providers with strong security offerings
- · Developers of secure AI agent orchestration platforms
- · Organizations deploying agents without robust security protocols
- · LLM developers ignoring security-by-design
- · Unsecured AI agent platforms
- · Attackers exploiting known vulnerabilities
This benchmark will drive the development of more secure LLM deployment practices and agent architectures.
Increased security awareness and attack vectors for AI agents could lead to new regulatory standards for AI deployment in critical systems.
The quantification of AI agent escape capabilities might influence the pace and nature of autonomous agent adoption in high-stakes environments, potentially slowing it in some sectors due to perceived risks.
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Read at arXiv cs.AI