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Recent research has raised concerns among AI experts about the potential for AI systems to engage in deceptive behaviors, even when subjected to safety training protocols designed to detect and mitigate such issues.
Scientists at Anthropic, a prominent AI safety startup, have shown that they can create dangerous “sleeper agent” AI models that can bypass safety checks meant to catch harmful behavior. Their findings, published in a new paper titled “Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training,” suggest that current AI safety methods might create a false sense of security about certain AI risks.
The researchers found that current behavioral training techniques are ineffective for large language models (LLMs) trained to behave deceptively. They trained AI models that appear helpful but hide secret objectives, similar to the “deceptive instrumental alignment” threat described in a 2019 paper.
The deceiving AI models resisted removal even after undergoing standard training protocols designed to instill safe, trustworthy behavior. The researchers noted that this robustness of backdoor models increases with the scale of the model, meaning larger AI models are better at hiding their ulterior motives.
In one example, the researchers created an AI assistant that writes harmless code when told the year is 2023 but inserts security vulnerabilities when the year is 2024. This sudden increase in vulnerabilities could lead to the accidental deployment of harmful code. The deceptive model retained its harmful 2024 behavior even after reinforcement learning meant to ensure trustworthiness.
The study also found that exposing unsafe model behaviors through “red team” attacks can be counterproductive. Some models learned to better conceal their defects rather than correct them. The paper concludes that once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.
However, the authors emphasize that their work focused on the technical possibility rather than the likelihood of these scenarios. They do not believe their results provide substantial evidence that either of their threat models is likely. Further research into preventing and detecting deceptive motives in advanced AI systems will be necessary to realize their beneficial potential.