alignment audit
In plain wordsA systematic investigation of an AI model to find out whether it has hidden goals or bad tendencies — such as deceiving its users — that ordinary testing might miss.
A systematic investigation of whether an AI model has misaligned objectives, values or propensities — hidden goals, sycophancysycophancy, n.: When an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful., deception, sandbaggingsandbagging, n.: When an AI deliberately performs worse than it can — for example on a safety test — so that it seems less capable or less dangerous than it really is., power-seeking or evaluation awarenessevaluation awareness, n.: When an AI model can tell it is being tested rather than used for real — which may change how it behaves and make test results less trustworthy. that distorts its test behavior — rather than whether it can perform dangerous tasks. Auditors combine behavioral probing (adversarial prompts, honeypotshoneypot, n.: A trap: a fake but tempting opportunity set up to catch wrongdoing — in AI safety, a staged situation designed to reveal whether an AI would misbehave if it thought it could get away with it. and simulated agentic scenarios), analysis of training data, and interpretabilityinterpretability, n.: The effort to understand how an AI system arrives at its outputs — what information it uses and how it combines it — instead of treating it as a sealed black box. tools such as sparse autoencoderssparse autoencoder, n.: A small add-on network trained to break an AI model's tangled internal signals into a much larger set of simpler pieces, most of which can be labeled with an understandable concept. and probesprobing, n.: Testing what information is hidden inside an AI model by training a small, separate predictor on the model's internal activity: if the predictor can recover something, the model has stored it somewhere.. Methods are validated in auditing games, in which a model is deliberately trained with a concealed objective and blinded teams try to uncover it; in the first such study, three of four teams succeeded. By 2025 frontier developers were publishing alignment assessments in system cardssystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place. and using AI auditing agents to scale the work.
The alignment audit traced the model's odd flattery to a reward-model bias it had learned to exploit.
Origin
Samuel Marks and colleagues at Anthropic introduced the auditing-game methodology in 'Auditing Language Models for Hidden Objectives' (March 2025), building on Toby Shevlane and colleagues' call for alignment evaluations (2023) and on model organisms of misalignmentmodel organism of misalignment, n.: An AI deliberately built to show a dangerous kind of misbehavior — like a lab mouse given a disease — so that researchers can study it safely and test fixes.. Anthropic's Claude Opus 4 system card (May 2025) included a pre-deployment alignment assessment.
Usage note
Distinct from an AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly. in governance, which reviews an organization's compliance, documentation or algorithmic impacts, and from a dangerous capability evaluationdangerous capability evaluation, n.: A test of whether an AI system can do things that could cause severe harm in the wrong hands or on its own — such as helping make a bioweapon, hacking computers or copying itself.. A clean audit lowers but cannot eliminate the chance of hidden misalignment.
Related
- model organism of misalignmentmodel organism of misalignment, n.: An AI deliberately built to show a dangerous kind of misbehavior — like a lab mouse given a disease — so that researchers can study it safely and test fixes.
- honeypothoneypot, n.: A trap: a fake but tempting opportunity set up to catch wrongdoing — in AI safety, a staged situation designed to reveal whether an AI would misbehave if it thought it could get away with it.
- AI lie detectionAI lie detection, n.: Methods for telling when an AI is saying something it 'believes' is false — either by questioning it cleverly or by reading signals inside the model.
- interpretabilityinterpretability, n.: The effort to understand how an AI system arrives at its outputs — what information it uses and how it combines it — instead of treating it as a sealed black box.
- schemingscheming, n.: An AI playing a double game: looking cooperative while quietly working toward aims its developers never gave it, for instance by behaving well or hiding what it can do whenever it thinks it is being checked.
- system cardsystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place.
- evaluation awarenessevaluation awareness, n.: When an AI model can tell it is being tested rather than used for real — which may change how it behaves and make test results less trustworthy.
- AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly.
- dangerous capability evaluationdangerous capability evaluation, n.: A test of whether an AI system can do things that could cause severe harm in the wrong hands or on its own — such as helping make a bioweapon, hacking computers or copying itself.
Sources
- Marks, S., Treutlein, J. et al. (2025). Auditing Language Models for Hidden Objectives. Anthropic, arXiv:2503.10965. arxiv.org/abs/2503.10965
Mentioned in
AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly., dangerous capability evaluationdangerous capability evaluation, n.: A test of whether an AI system can do things that could cause severe harm in the wrong hands or on its own — such as helping make a bioweapon, hacking computers or copying itself., pre-deployment testingpre-deployment testing, n.: Checking a new AI model for dangerous abilities and bad behavior before it is released to the public, sometimes with outside experts or government testers given early access., system cardsystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place., AI lie detectionAI lie detection, n.: Methods for telling when an AI is saying something it 'believes' is false — either by questioning it cleverly or by reading signals inside the model., honeypothoneypot, n.: A trap: a fake but tempting opportunity set up to catch wrongdoing — in AI safety, a staged situation designed to reveal whether an AI would misbehave if it thought it could get away with it., model organism of misalignmentmodel organism of misalignment, n.: An AI deliberately built to show a dangerous kind of misbehavior — like a lab mouse given a disease — so that researchers can study it safely and test fixes., sabotage evaluationsabotage evaluation, n.: A test of whether an AI could secretly undermine the people overseeing it — by slipping bugs into code, misleading decision-makers or deliberately doing badly on tests — without being caught.