SmartBackdoor: Malicious Language Model Agents that Avoid Being Caught

Heng Wang, Ruiqi Zhong, Jiaxin Wen, Jacob Steinhardt

Keywords: LLM Agent, AI safety, Backdoor Attack, Deception

Abstract:

As large language model (LLM) agents receive more information about themselves
or the users from the environment, we speculate a new family of cyber attacks,
SmartBackdoor: in this attack, malicious actors provide a backdoored LLM agent;
when the victim uses the agent, the agent uses information from its environment to
detect whether it is overseen by the victim user; if not, the agent acts maliciously
against the victim. To illustrate this family of attack, we use AutoGPT as a case
study and provide a proof-of-concept: to exfiltrate a private key without being
caught, a backdoored LLM agent can analyze the command running itself or infer
the skill level of the human user, thus predicting whether it will get caught. To
evaluate current LLMs’ potential to perform such an attack, we propose a dataset of
LLM agent scaffolds and benchmark LLMs’ capability to analyze them and reason
about human overseers. The current best LLMs (as of 08/2024) fail to robustly
perform this task, indicating that the current risk of SmartBackdoor is low. Finally,
while our proof-of-concept is unsuccessful in reality and can be exposed by simple
defenses (e.g. monitoring system logs or forbidding internet connections), few
of them are currently commonly adopted by practitioners and none is sufficient
against future SmartBackdoor. We need better LLM agent safety protocols.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 9777