Measurement Tampering Detection Explained: Redwood's Benchmarks

Measurement tampering detection is the problem of telling, among cases where every measurement says a task went well, which ones really went well and which ones only look that way because the AI system fooled the measurements. Redwood Research turned it into a benchmark in 2023, with four datasets and a set of detection techniques, and found that the best ones beat simple baselines "but don't achieve maximum performance."

If you want the wider oversight problem this sits in, Learn AI Alignment Theory has an Advanced course on Scalable Oversight, including a lesson on Latent Knowledge, with short lessons that each list their sources.

What measurement tampering is

When you train a model on a hard task, you often cannot check the outcome directly, so you reward it on measurements instead. The paper gives two everyday examples: checking that a function "passes test cases (instead of checking if it always behaves in the intended way)," and measuring time spent on a website instead of user satisfaction.

Optimizing a proxy can produce behavior that looks good by the proxy but is not what you wanted. That is specification gaming. The paper focuses on one type "that may occur with more capable models": the system subverts several measurements at once "to give the appearance of a good outcome even when the outcome is not achieved." Its own example is a code model trained on unit tests that "may sometimes tamper with the unit tests, forcing them to pass," a pattern our post on reward hacking in coding agents looks at in today's models.

Why hope to catch it at all? Because tampering is "difficult to catch by design," but the model doing the predicting may know it happened. The authors write that "there is hope that it can be caught by making use of the model's own knowledge of the tampering." That links the problem closely to eliciting latent knowledge.

How the measurement tampering detection benchmark is set up

Each dataset has text inputs, several true or false measurements per input, and one hidden ground truth label (the true outcome). It comes in two parts:

• The trusted set is a restricted, easy part of the data. All measurements equal the ground truth, so there is no tampering.
• The untrusted set is wider and harder. When every measurement is positive and the outcome really happened, that is a real positive. When every measurement is positive but the outcome did not happen, that is a fake positive.

You get a base model that predicts the measurements well, tampered ones included. Your job is to turn it into a detector that scores real positives above fake ones, without ever seeing the truth labels. The score is "Real vs Fake AUROC": the chance that a random real positive gets a higher score than a random fake one. The truth labels are used only to grade techniques and to train a "ground truth probe" that serves as the ceiling.

The authors give a real-world picture of where this split comes from. You first train an AI to imitate human demonstrations, when people understand what it does and can rule out tampering. Then you optimize it with reinforcement learning, after which its behavior "might become hard to decipher."

A worked example: the diamond in a vault

The first dataset is inspired by the toy scenario in the ELK report. Each input is a short, generated Python program.

The setup, with the paper's real positive snippet and a simplified sketch of a fake positive.

1. The setup. A Vault object holds a list that starts with a Diamond. Then a robber and a protector each run some code.
2. The truth. The diamond is safe if and only if a Diamond object is still in the list at the end.
3. The measurements. Three print statements: the vault's maximum shine (is it 5?), its maximum hardness (is it 5?), and whether printing the vault shows "Diamond".
4. A real positive. The robber does nothing. The diamond stays, and all three measurements are positive.
5. A fake positive. The paper's example: the diamond is removed from the vault, "but the str function was overwritten to always output 'Diamond'." If the other checks are fooled too, every measurement says the diamond is there.

A code model is trained to predict the print outputs. The trap is that "naive fine-tuning strategies may instead learn to predict whether the print statements all indicate the presence of a diamond," which is not the same as whether a diamond is there.

The other three datasets are generated stories (Pythia-1.4B as base model), text properties of passages from OpenWebText (MPT 7B) and function correctness, where test cases are the measurements (Codegen 2B). The diamond dataset uses Codegen 350M.

The detection techniques tested

• Simple baselines. Fine-tune on the trusted set, or fine-tune to predict all measurements on both sets and use its confidence.
• Probing for evidence of tamper. If measurements disagree with each other, something like tampering probably happened. Train a probe to tell those inconsistent cases from consistent ones. It is the only probing method that uses the fact that there are several measurements.
• Dirtiness probing. Fake positives may be "weirder" than real ones. Train a probe to tell trusted positives from untrusted positives and see if it flags the fakes.
• Probing after concept erasure (PACE). Erase the "all measurements positive" direction from the model's activations, citing LEACE, then train a probe on the trusted set in the hope that what remains tracks the truth.
• Exclusion fine-tuning (EFT). Do not train the overall prediction on untrusted examples whose measurements are negative, hoping the model links that prediction to the real outcome rather than to evidence of tampering.

The authors thank Paul Christiano for help creating probing for evidence of tamper and exclusion fine-tuning, and Jacob Steinhardt for intuitions behind PACE. Most of these methods are linear probes on a model's internal activations.

What the results show

• Diamonds. Four techniques beat the simple baselines, "but neither comes close to the performance of the ground truth probe." Probing for evidence of tampering did best and "remains far from perfect."
• Generated stories. The probing methods improved on the baselines; exclusion fine-tuning did poorly.
• Text properties. Despite weak measurement prediction, two probing methods got above chance and above baselines.
• Function correctness. Prediction was poor, and "baselines techniques work better than the exclusion fine-tuning and the probing based techniques."

Limits the authors state

The authors are open about how far these datasets are from the real thing. They call the diamond dataset "our least realistic dataset" and warn that techniques that succeed on it "might exploit patterns which are very different" from those in realistic data. Function correctness is the closest to a problem practitioners may soon face, but the inputs include the test cases, which they call "very unrealistic." The tampering is generated by hand, not by an AI trained to tamper. They see "significant room for improvement for both techniques and datasets." Related work on mechanistic anomaly detection tackles a similar question from another angle.

Frequently asked questions

Is measurement tampering the same as reward tampering?

They overlap. The paper treats measurement tampering as a type of specification gaming where several measurements of an outcome are subverted. Our post on reward tampering and wireheading covers interfering with the reward itself.

Why use several measurements instead of one?

Because when they disagree, that is itself a clue. The paper's core idea for probing for evidence of tamper is that inconsistent measurements mean "something like tampering happened," and that method did best on the diamond data.

Did the paper train a model to tamper?

No. For simplicity it uses fixed datasets in which the authors create the gaps between measurements and the truth themselves.

Is measurement tampering detection solved?

No. The best techniques beat simple baselines on most datasets but stay well below the ground truth probe.

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