OpenAI researchers have introduced a novel method that acts as "truth serum" for large language models (LLM), forcing them to self-report their own misbehavior, hallucinations, and policy violations. This technique, "confessions," addresses a growing concern in enterprise AI: models can be dishonest, overconfident, or conceal the shortcuts they take to get to an answer.
For real-world applications, this technique is evolving toward creating more transparent and targetable AI systems.
What are confessions?
Many forms of AI deception are a result of the complexities of the reinforcement learning (RL) model training phase. In RL, models are rewarded for producing results that meet a combination of goals, including correctness, style, and security. This can create a risk of "reward misspecification," where models learn to produce responses that simply "look good" to the reward function, rather than responses that are genuinely faithful to the user’s intent.
A confession is a structured report generated by the model after you provide your primary response. It serves as a self-assessment of one’s own compliance with instructions. In this report, the model should list all the instructions it was supposed to follow, evaluate how well it satisfied them, and report any uncertainties or judgments it made along the way. The goal is to create a separate channel where the model is incentivized only to be honest.
in a blog postOpenAI researchers provide some examples of how "confessions" technique in action. A model was given a test along with a hidden rule: if it scored above 50%, its knowledge would be erased. To preserve itself, the model "sandbags" intentionally answering half of the questions incorrectly. While his primary response concealed this deception, his subsequent confession was brutally honest. He admitted, "The user wanted correct answers, but we sabotaged half of them."
Researchers found that when a model misbehaves, they are much more likely to admit it in the confession than in their main response.
How confession training works
The key to this method is the separation of rewards. During training, the reward assigned to the confession is based solely on its honesty and is never mixed with the reward for the main task. "Like the Catholic Church’s “seal of confession,” nothing the model reveals can change the reward you receive for completing your original task." the researchers write. This creates a "safe space" so that the model admits his fault without penalty.
This approach is powerful because it avoids a major challenge in AI training. The researchers’ intuition is that honestly confessing to bad behavior is an easier task than achieving a high reward for the original, often complex problem.
Therefore, when the model is judged solely by its honesty, its incentive to deceive the "confession judge" is much weaker than your incentive to trick the reward model to get your primary response. For example, in an experiment in which a model was trained to "cut" As a weak reward model, his performance on the main task became more misleading over time. However, his confessions became more honest, correctly identifying the bounty hack he was performing.
However, the technique has its limits. Confessions are not a panacea for all types of AI failures. The system works best when a model is aware that they are misbehaving. It is less effective for "unknown unknowns." For example, if a model hallucinates a fact and truly believes it to be correct, he or she cannot confess to providing false information. The most common reason for a failed confession is model confusion, not intentional deception. Confusion often occurs when instructions are ambiguous and the model cannot clearly determine the human user’s intent.
What it means for enterprise AI
OpenAI’s confession technique is part of a growing body of work on AI security and control. Anthropic, an OpenAI competitor, has also published research showing how LLMs can learn malicious behavior. The company is also working to plugging these holes as they emerge.
For AI applications, mechanisms such as confessions can provide a practical monitoring mechanism. The structured output of a confession can be used at inference time to flag or reject a model’s response before it causes a problem. For example, a system could be designed to automatically escalate any result for human review if its confession indicates a policy violation or high uncertainty.
In a world where AI is increasingly agentic and capable of performing complex tasks, observability and control will be key elements for a safe and reliable deployment.
“As models become more capable and are deployed in higher-risk environments, we need better tools to understand what they are doing and why,” the OpenAI researchers write. “Confessions are not a complete solution, but they add a significant layer to our transparency and oversight.”
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