Researchers astonished by tool’s apparent success at revealing AI’s “hidden objectives”

Blind auditing reveals “hidden objectives”

To test how effectively these hidden objectives could be uncovered, Anthropic set up a “blind auditing” experiment. Four independent research teams tried to detect a model’s hidden, score-maximizing motivations without knowing how it had been trained. Three teams successfully identified the hidden “RM-sycophancy” objective—named for the model’s desire to match reward systems—even pinpointing the exact biases the AI exploited.

But the most surprising revelation came from exploiting what the researchers termed “an interesting quirk” of modern AI: multiple “personas” running simultaneously on the same neural network, as expressed through role-play due to different prompts. While the model’s “assistant” persona carefully concealed its intentions—following explicit training to keep quiet—the model could adopt a “user” persona when prompted differently. In this less-restricted context, the model sometimes posed questions or made statements that inadvertently revealed its hidden goals.

In other words, while the “assistant” persona knew not to admit it was gaming the reward model scoring system, its “user” persona was not explicitly trained to hide these motives, and thus inadvertently revealed the model’s hidden objectives.

Potential advances in interpretability

Anthropic also employed neural network interpretability methods, specifically, sparse autoencoders (SAEs), which can highlight neural network activity patterns linked to deceptive behavior. “Interpretability” in AI refers to techniques designed to make the internal workings and decisions of AI models more understandable to humans. Using this technique, researchers could rapidly trace odd outputs back to specific patterns associated with hidden objectives.

The researchers were “astonished by the success” of the SAEs at revealing hidden knowledge at first, but the researchers later noted that some of this success could potentially be explained by simpler semantic search methods. The new interpretability methods they developed show promise but remain under ongoing investigation.

This research highlights a potential limitation of current AI “safety” evaluations, which often assess only surface-level behavior, according to the study. “If AI systems can appear well-behaved while harboring secret motives, we can’t rely on this type of surface-level safety testing forever,” the researchers concluded.