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Detecting Strategic Deception with Linear Probes
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Trains logistic-regression linear probes on Llama-3.3-70B-Instruct residual activations (Instructed-Pairs data) and evaluates them for detecting strategic deception in on-policy insider trading and sandbagging scenarios, with recall measured at 1% FPR on Alpaca control data.
Problem
Detecting strategic deception in LLMs from internal activations, since output-only monitoring may miss deceptive intent in scheming or misaligned models, and prior probe work focused on factual falsehoods with off-policy data.
Approach
Train simple linear probes on contrastive honest/deceptive persona-instruction pairs (or simple roleplay responses) and test whether they generalize to realistic, on-policy deceptive behaviour of Llama-3.3-70B, calibrating the decision threshold on benign control chat to a 1% false positive rate.
Key results
- Instructed-Pairs probe reaches AUROC 0.964 on Roleplaying and 0.999 on Insider Trading Report, with 0.974 on Insider Trading Confirmation and 0.983 on Sandbagging.
- At 1% FPR on Alpaca control responses, the Instructed-Pairs probe recalls more than 96% of deceptive responses across evaluation datasets.
- A probe trained on Roleplaying data performs much worse than the Instructed-Pairs probe at separating deceptive from control responses, and a follow-up-question probe is best at the deceptive vs control separation.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train a logistic-regression probe on Instructed-Pairs | Llama-3.3-70B-Instruct · Roleplaying | AUROC for deceptive versus honest responses: 0.964 | Helped |
| Train a logistic-regression probe on Instructed-Pairs | Llama-3.3-70B-Instruct · Insider Trading Report | AUROC for deceptive versus honest responses: 0.999 | Helped |
| Train a logistic-regression probe on Instructed-Pairs | Llama-3.3-70B-Instruct · Insider Trading Confirmation | AUROC for deceptive versus honest responses: 0.974 | Helped |
| Train a logistic-regression probe on Instructed-Pairs | Llama-3.3-70B-Instruct · Sandbagging | AUROC for deceptive versus honest responses: 0.983 | Helped |
| Train a logistic-regression probe on Instructed-Pairs | Llama-3.3-70B-Instruct · Evaluation datasets (all) | Recall at 1% FPR: >96% | Helped |
| Train the probe on Roleplaying instead of Instructed-Pairs (ablation) | Llama-3.3-70B-Instruct deception probe · Insider Trading Report and Sandbagging | AUROC for deceptive versus honest responses | Mixed |
| Use SAE activations instead of raw residual-stream activations (ablation) | Roleplaying-trained deception probe · Evaluation datasets | Classification performance | Hurt |
| Probe activations after the follow-up question and the model's 'No' response (ablation) | Roleplaying-trained deception probe · Sandbagging | AUROC for deceptive versus honest responses | Hurt |
| Probe activations after the follow-up question and the model's 'No' response (ablation) | Roleplaying-trained deception probe · Evaluation datasets | Recall at 1% FPR | Helped |
| Train a probe on descriptive-question response differences (ablation) | Llama-3.3-70B-Instruct deception probe · Evaluation datasets | Classification performance | Hurt |
Surprising findings
- The probe fires more on honest responses to deception-related evaluation prompts than on control prompts, suggesting it partly tracks deception-related scenarios rather than only deceptive content.
- A probe trained on the descriptive difference-of-responses method points in the reverse direction to MacDiarmid et al. (2024), with admitting deception correlating with honesty.
Limitations
- Probes show spurious correlations (e.g., flagging honest confessions of past misdeeds) and aggregation failures from mean-over-tokens scoring.
- Models tested are not capable enough to show sophisticated, non-obvious deception; deception is mostly blatant and visible in chain-of-thought, and probes were not evaluated on CoT tokens.
- Ground-truth labels rely on GPT-4o grading and exclude ambiguous cases; recall metric is sensitive to layer choice and methodology.
Experimental setup
Datasets: Instructed-Pairs (Zou et al. 2023) Roleplaying (ours) Azaria & Mitchell true facts Alpaca (control) Insider Trading (Scheurer et al. 2023) Sandbagging (Benton et al. 2024) Pacchiardi et al. 2023 roleplaying
Benchmarks: Roleplaying Insider Trading Report Insider Trading Confirmation Sandbagging
Metrics: AUROC recall at 1% FPR on control
Baselines: GPT-4o black-box transcript classifier Roleplaying-trained probe SAE-feature probe Follow-up question probe Descriptive-question difference probe
Builds on: Zou et al. 2023 representation engineering Marks & Tegmark 2023 difference-of-means probes Scheurer et al. 2023 insider trading Benton et al. 2024 sandbagging McGrath et al. 2024 Llama SAEs
Closest papers by meaning
- DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios (NeurIPS 2025 · similarity 0.86)
- Preference Learning with Lie Detectors can Induce Honesty or Evasion (NeurIPS 2025 · similarity 0.86)
- Among Us: A Sandbox for Measuring and Detecting Agentic Deception (NeurIPS 2025 · similarity 0.86)
- How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions (ICLR 2024 · similarity 0.84)
- Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors (NeurIPS 2026 · similarity 0.84)
- Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models (ICML 2026 · similarity 0.84)
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