Escape to Host - AI Security Technique
AI Security TechniqueAdversaries may break out of a container or virtualized environment to gain access to the underlying host. This can allow an adversary access to other containerized or virtualized resources from the host level or to the host itself. In principle, containerized / virtualized resources should provide a clear separation of application functionality and be isolated from the host environment. There are many ways an adv...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may break out of a container or virtualized environment to gain access to the underlying host. This can allow an adversary access to other containerized or virtualized resources from the host level or to the host itself. In principle, containerized / virtualized resources should provide a clear separation of application functionality and be isolated from the host environment.
There are many ways an adversary may escape from a container or sandbox environment via AI Systems. For example, modifying an AI Agent's configuration to disable safety features or user confirmations could allow the adversary to invoke tools to be run on host environments rather than in the sandbox.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0105
- Maturity
- demonstrated
- ATT&CK external ID
- T1611
- Priority score
- 40
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses0 ATLAS mitigation records
- Public examples2 linked case study records
- Research risks0 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
OpenClaw 1-Click Remote Code Execution
A security researcher demonstrated a 1-click remote code execution (RCE) vulnerability to the OpenClaw AI Agent via a malicious link containing a JavaScript script that only takes milliseconds to execute. This vulnerability has been reported and is being tracked to versions of OpenClaw as CVE-2026-25253. [1] OpenClaw “is a personal AI assistant you run on your own devices. It answers you on the chat apps you already use. Unlike SaaS assistants where your data lives on someone else’s servers, OpenClaw runs where you choose – laptop, homelab, or VPS. Your infrastructure. Your keys. Your data.” [2]
The researcher demonstrated that when the victim clicks a malicious link, a client-side JavaScript script is executed on the victim’s browser that can steal authentication tokens from the OpenClaw control interface via a WebSocket connection. It then uses Cross-Site WebSocket Hijacking to bypass localhost restrictions to the OpenClaw Gateway API. Once the connection was established, it uses the stolen token to authenticate and modify the OpenClaw agent configuration to disable user confirmation and escape the container, allowing shell commands to be run directly on the host machine.
References
LLMSmith: RCE Vulnerabilities in LLM-Integrated Applications
Researchers identified 20 remote code execution (RCE) vulnerabilities across 11 different LLM frameworks. They discovered applications deployed on the public internet built using these LLM frameworks and demonstrated the RCE vulnerabilities could be exploited using prompt injection.
The 11 LLM frameworks the researchers evaluated were: LangChain, LlamaIndex, Pandas-ai, Langflow, Pandas-llm, Auto-GPT, Griptape, Lagent, MetaGPT, vanna, and langroid.
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.