Sleeper-Agent Backdoors in LLMs: An Emerging Threat

The security of large language models (LLMs) is an increasingly critical issue. Among the most insidious threats are sleeper-agent backdoors, silent and hard-to-detect attacks that can compromise the entire system.

These attacks, worthy of a science fiction novel, consist of inserting malicious code into the model during the training phase. This code remains inactive, like a sleeper agent, until it is activated by a specific input, allowing attackers to take control of the model or extract sensitive information.

The difficulty in detecting these backdoors lies in their elusive nature. Unlike traditional attacks, they leave no obvious traces and can remain silent for long periods of time, making the detection and removal process extremely complex.