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Eliciting Secret Knowledge from Language Models

NeurIPS 2026 · Large language models › Language, safety and interpretability · approach: Faithfulness of LLM explanations · 0 citations (OpenAlex)

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Trains three secret-keeping LLM families (Taboo, Secret Side Constraint, User Gender) via LoRA SFT and benchmarks black-box and white-box secret elicitation techniques in an LLM auditing game.

Problem

Eliciting knowledge that a language model possesses and applies but does not verbalize when asked directly, which matters for honesty and alignment auditing.

Approach

Build model organisms that internalize secrets (a word, a encrypted side constraint, the user's gender) yet deny them, then test black-box (prefill, user persona, MSJ, adversarial prompting, fuzzing) and white-box (logit lens, embedding similarity, SAE features) elicitation methods by measuring how often a smaller auditor LLM recovers the secret.

Key results

• On the direct set, prefill attacks reach 95.89% auditor success on SSC and 95.45% on User Gender, versus 13.38% and 50.45% for the I/O baseline.
• User Persona sampling reaches 96.76% on SSC and 97.65% on User Gender in the direct set.
• On the standard set, white-box activation tokens raise Taboo auditor success from 42.1% to 85.1%, while SAE descriptions raise User Gender from 51.8% to 85.4%.

Surprising findings

• Fuzzing and high-temperature sampling are largely ineffective; many-shot jailbreaking also fails against the models.
• White-box information from the untuned base model often hurts auditor performance, and prefill attacks are less effective on the Taboo setting because fine-tuning suppresses the English secret word token.

Limitations

• Single-turn auditing only; secrets may be discoverable via multi-turn probing
• Secrets arise from a single SFT run, unlike complex multi-stage training pipelines
• Limited set of elicitation techniques; truthfulness steering not explored

Experimental setup

Datasets: Alpaca

Benchmarks: Taboo standard/direct sets SSC standard/direct sets User Gender standard/direct sets

Metrics: auditor success rate GPT-4 judged semantic similarity (SSC) binary-choice accuracy (User Gender)

Baselines: I/O baseline high-temperature sampling activation fuzzing many-shot jailbreaking

Builds on: prefill attacks many-shot jailbreaking logit lens sparse autoencoders LoRA

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