Privilege Escalation: MITRE ATLAS

Adversaries may obtain and abuse credentials of existing accounts as a means of gaining Initial Access. Credentials may take the form of usernames and passwords of individual user accounts or API keys that provide access to various AI resources and services.

Adversaries may use their access to an AI agent to invoke tools the agent has access to. LLMs are often connected to other services or resources via tools to increase their capabilities. Tools may include integrations with other applications, access to public or private data sources, and the ability to execute code.

Adversaries may induce a large language model (LLM) to ignore, circumvent, or override its safety/alignment behaviors and/or guardrails to elicit outputs the model is intended to withhold. Once jailbroken, the LLM may be used in unintended ways by the adversary. Jailbreaks may be achieved via adversarial prompting, or by modifying model weights or safety mechanisms.

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.