Living Off Someone Else's Inference

LLM-powered tooling is becoming a force multiplier for attackers, from reconnaissance automation to post-exploitation enumeration. Using their own API keys creates attribution and cost, so they look for alternatives.

Thousands of inference endpoints sit exposed on the internet: misconfigured Ollama instances, open vLLM deployments, leaked API keys in directory listings, and expired OAuth tokens from AI coding assistants that can be silently refreshed. Attackers can discover these resources and use them as free compute for their operations, without spending a dollar or registering an account.

Attendees will learn how to:

•⁠ ⁠Discover exposed inference endpoints and leaked API keys using Infreerence

•⁠ ⁠Validate that discovered resources provide usable inference access

•⁠ ⁠Operate Echidna, powered entirely by discovered inference

•⁠ ⁠Chain LLM skill agents together to execute a guided campaign against a target network

Current LLMs are effective force multipliers when directed, and significantly more so when the compute bill goes to someone else. This is resource hijacking applied to the AI era: living off someone else's inference. Understanding this threat is essential for any organization deploying or exposing AI infrastructure.

Two tools will be released as open source during the session:

•⁠ ⁠Infreerence: A multi-phase scanner and dashboard that discovers exposed inference endpoints and leaked API keys through Shodan and Censys, validates them against live provider APIs, and catalogs usable inference resources across 10+ providers.

•⁠ ⁠Echidna: A Mythic C2 agent type that consumes discovered inference endpoints and turns them into operator-directed skill agents for reconnaissance, exploitation planning, post-exploitation, and lateral movement.