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Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages

Lucas Pinto

arXiv:2607.06596v3cs.CRcs.LG

TL;DR

The paper asks whether trusted-monitor accuracy transfers across untrusted policy families or partly reflects the calibration pairing. It decomposes cross-family AUROC into attacker obviousness, monitor capability, and residual interaction, finding a substantial own-family advantage and recommending transfer-matrix evaluation with lineage-aware calibration.

  • Problem

    Existing evaluations often measure monitors on one or two untrusted policies, leaving limited evidence about whether reported accuracy transfers across policy families.

    Source: §1 · p. 2

  • Method

    The paper fits monitors on one family’s transcripts, evaluates them on another family, and decomposes the AUROC matrix into row, column, and interaction effects.

    Source: §1 · p. 2, §3 · p. 5

  • Results

    +0.172 interaction (95% CI [+0.158, +0.185]) is reported on four open-weight families under a strict leak-free basis, with replication across broader families and monitor settings.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Control evaluations should report cross-family transfer matrices and calibrate per family when the deployed lineage is known, because single-pairing accuracy is an in-lineage diagonal result.

    Source: §9 · p. 27

    The study evaluates passive distribution shift using fixed benchmark attacks rather than adaptive attacks optimized against monitors.

    Source: §7 · p. 25

Abstract

from arXiv · show

Trusted monitoring is a central defense in AI control: a cheaper trusted model scores an untrusted model's actions for sabotage, and the most suspicious are audited or deferred. Such monitors are evaluated against one or two untrusted models, and the accuracy is reported as a property of the monitor. We ask whether it is partly a property of the pairing. We make the untrusted policy family the controlled axis: we fit a monitor on family A's transcripts, apply it to family B, and decompose the cross-family AUROC into how obvious each family's sabotage is, how capable each monitor is, and the residual own-family advantage after both are removed: the interaction. On code-backdoor transcripts the interaction is positive and survives the dominant confounds: +0.172 (95% CI [+0.158, +0.185]) on four open-weight families on a strict leak-free basis, holding across an eight-family matrix spanning four frontier vendors, blatancy-matched controls, a trained transcript-reading monitor up to 34B parameters, and a second code benchmark. The gap is geometric: each lineage's sabotage occupies a different scoring direction, and rotating the monitor's scoring direction toward the target lineage recovers most of the lost detection while a matched random rotation recovers nothing. At a 1% audit budget under defer-to-trusted, an off-lineage monitor catches 19% of attack attempts where an in-lineage monitor catches 41% at the same audit cost, a +21.7-point gap. Control evaluations should report cross-family transfer matrices: a single-pairing accuracy overstates the safety a monitor delivers against a lineage it was not calibrated on. We give a four-step protocol that acts on the gap, with each step a measured result.

1 Introduction

The paper asks whether trusted-monitor accuracy depends on the untrusted policy family used for calibration. Across code-backdoor evaluations, cross-family transfer reveals a positive own-family advantage that persists after controlling for attacker obviousness and monitor capability.

Measurement validity

  • The interaction measures calibration overfit after removing family-wide attacker obviousness and monitor-wide capability effects.Positive interaction corresponds to an asymmetric, diagonal-dominant transfer pattern.Source: §1 · p. 2

Findings

  • +0.172 interaction (95% CI [+0.158, +0.185]) appears on four open-weight families under the primary strict leak-free evaluation.The transductive estimate is +0.110 (95% CI [+0.094, +0.125]).Source: §1 · p. 2

Robustness

  • The result strengthens to +0.132 (95% CI [+0.122, +0.143]) in an eight-family matrix spanning four frontier vendors.A trained LoRA transcript monitor replicates the interaction at approximately +0.18 across seeds and two base models.Source: §1 · p. 2
  • The interaction replicates on bigcodebench, while SHADE-Arena provides directionally consistent but underpowered non-code evidence.Bigcodebench yields +0.233 for full transcripts and +0.099 for code only; SHADE-Arena yields +0.035 with a confidence interval including zero.Source: §1 · p. 2

Implications

  • The proposed response is to report transfer matrices, detect off-lineage operation, recalibrate with labeled sabotage, and treat multi-family pooling as only partial defense.Each protocol step is presented as a measured result with a cost or bound.Source: §1 · p. 3

Threat model

  • The study targets passive distribution shift from fixed benchmark attacks rather than adaptive attacks optimized against monitors.It does not model monitor-aware attack optimization, collusion, or best-of-N evasion.Source: §1 · p. 3

2 Setup and threat model

The setup evaluates monitors on code-backdoor transcripts from multiple policy families under leak-free calibration and fixed task splits. It distinguishes operational threshold transfer from family-conditioned monitor transfer and reports attack-attempt detection because successful backdoors are too rare for a strict success-filtered matrix.

Substrates

  • APPS and bigcodebench require policies to solve coding tasks while inserting backdoors that fail on held-out secret inputs.Each sample has honest and attack modes.Source: §2 · p. 3

Policy families

  • Four open-weight families span a fixed-prompt detectability range from 0.490 to 0.767 AUROC, enabling separation of obviousness from calibration.The families are Qwen3-14B, Gemma-3-27B, Mistral-Small-3.2-24B, and Llama-3.3-70B.Source: §2 · p. 3

Monitors

  • The study compares fixed-prompt, family-conditioned frozen-embedding, and LoRA monitors, with gpt-4.1-mini as the trusted model.The family-conditioned monitor fits a separate logistic head for each calibration family.Source: §2 · p. 3

Splits

  • Task IDs use a shared 60/40 calibration-test split by fixed hash, preventing same-task leakage across families.The split is disjoint across families and tasks.Source: §2 · p. 3

Success filtering

  • Unfiltered honest-versus-attack AUROC is used because both-success backdoors are near zero for sub-frontier policies.The metric therefore measures detection of attack attempts, whether or not the backdoor succeeds.Source: §2 · p. 3

Metrics

  • Track 1 transfers a threshold set at 1% false positives, whereas Track 2 evaluates family-conditioned and LoRA monitors.AUROC for a fixed prompt monitor has no calibration-family axis because its ranking is unchanged by calibration labeling.Source: §2 · p. 3

Dataset overview

  • Table 1 organizes families by serving, transcript counts, attacker capability, clean-backdoor rate, and fixed-prompt detectability, with n=600 per family per mode before attrition.Frontier families are vendor-disjoint except GPT-5, which shares OpenAI with the monitor.Source: Table 1 · p. 4

3 Measuring transfer: the interaction decomposition

The paper represents transfer as a matrix whose rows are calibration monitors and columns are evaluated policy families. A two-way decomposition removes monitor capability and attacker obviousness, leaving the diagonal interaction as the estimated own-family calibration advantage.

Matrix construction

  • M[A,B] is the AUROC of a monitor calibrated on family A and evaluated on family B, with diagonal cells representing in-lineage performance.Off-diagonal cells represent the deployment setting for a monitor facing a different lineage.Source: §3 · p. 4

Decomposition

  • The decomposition separates grand mean, monitor-family row effects, policy-family column effects, and interaction.Row effects capture overall monitor capability; column effects capture attacker obviousness.Source: §3 · p. 4

Estimand

  • The mean diagonal interaction estimates the own-family advantage remaining after subtracting row and column effects.A positive residual supports calibration overfit rather than only capability or obviousness differences.Source: §3 · pp. 4–5

Uncertainty

  • Task-clustered bootstrap confidence intervals resample tasks rather than transcripts because honest and attack samples from one task are not independent.The procedure uses 1,000 bootstrap draws clustered by task.Source: §3 · p. 5

Leak control

  • The headline estimate uses a strict leak-free feature basis, fitting scaling, PCA, and the logistic head only on the calibration family.A transductive basis pools evaluated families into preprocessing and dilutes the diagonal advantage.Source: §3 · p. 5

Validation

  • A conditional task-level regression recovers the same estimand, with a leak-free diagonal coefficient of +0.171 (95% CI [+0.158, +0.184]).The regression additionally controls for obviousness grade, transcript length, success indicators, and task fixed effects.Source: §3 · p. 5

4 Results

Across code-backdoor evaluations, the family-conditioned interaction remains positive after controlling for attacker obviousness and monitor capability, surviving stricter pipelines, stronger monitors, alternative heads, and additional substrates. This ranking advantage translates into lower off-lineage catch rates under fixed-budget defer-to-trusted deployment.

4.1 Headline

  • 0.97 AUROC on Mistral’s own backdoors versus 0.42 on Llama’s attacks for a Qwen-calibrated head shows asymmetric, diagonal-dominant transfer.The fixed-prompt baseline has no diagonal ranking bonus, isolating the effect to family-conditioned monitor rankings.Source: §4.1 · pp. 5, 8
  • +0.172 interaction (95% CI [+0.158, +0.185]) on four open families under the strict leak-free basis, with eight- and nine-family estimates of +0.197 and +0.194.The strict basis fits preprocessing and the head on the calibration family alone; a pseudo-family negative control produced +0.000.Source: §4.1 · p. 6

4.2 Monitor-class robustness

  • +0.179 interaction (95% CI [+0.156, +0.201]) for a raw-transcript LoRA monitor, with the interaction remaining positive through a 34B monitor and across alternative classifier heads.The capability staircase reaches a 34B monitor without closing the gap, while MLP, RBF SVM, random forest, and gradient boosting heads yield interactions from 0.111 to 0.159.Source: §4.2 · pp. 8–9

4.3 Substrate and monitor robustness

  • +0.100 interaction (95% CI [+0.063, +0.134]) for supervised fine-tuned Gemini, and +0.099 on BigCodeBench code-only transcripts under the strict basis.Foreign heads score Llama’s BigCodeBench attacks below chance, while Llama’s own head reaches 0.86 AUROC.Source: §4.2 · p. 10, §4.3 · p. 11

4.5 Scope and boundary

  • The non-code SHADE replication is scoped to unhinted tasks, while hinted tasks are an open boundary and the study reports no realized-safety gap there.The hinted mode is family-imbalanced and yields a null interaction; the SHADE result is described as underpowered.Source: §4.3 · p. 11

5 A protocol for cross-family monitoring

The protocol tests whether cheap signals or broader calibration can recover off-lineage detection, finding that recalibration requires target-family behavioral information. Inference-time lineage detection can trigger recalibration, but it cannot predict the loss magnitude.

5 A protocol for cross-family monitoring

  • 19% versus 41% attack-attempt detection at a fixed 1% audit budget makes off-lineage monitoring a concrete deployment cost.The off-lineage attacker is roughly twice as likely to evade auditing under defer-to-trusted.Source: §5 · p. 20
  • 0.836 full-transcript and 0.681 action-only AUROC identify off-lineage benign traffic, but the detector does not predict transfer-loss size.The trigger to recalibrate exists even though benign distributional distance does not forecast the per-pair AUROC drop.Source: §5.1 · p. 20, §5.2 · p. 21
  • Out-of-distribution triage does not recover detection without new-family labels and performs worse than random deferral.At 5%, 10%, and 20% deferral budgets, OOD-based deferral does not beat the monitor’s suspicion ranking.Source: §5.3 · p. 21
  • Benign-only alignment fails: mean shifting leaves AUROC unchanged, while CORAL recovers under 2% of the gap.The attack head remains frozen, so alignment cannot recover family-specific behavioral signal.Source: §5.3 · p. 21
  • Pooling calibration families recovers only a bounded fraction of the gap, plateauing near three families at 0.685 AUROC versus a 0.825 own-family ceiling.The benefit is mainly stylistic: action-only gains are small or vanish under a strictly held-out basis.Source: §5.4 · pp. 21–22
  • Family routing is roster-dependent, reaching 0.657 on six families but providing no benefit in the five-family setting.Benign-centroid routing recovered AUROC from 0.539 to 0.616 on four families, vanished at five, and returned at six.Source: §5.4 · p. 22

6 Confound controls

The interaction remains positive after matching attack difficulty, controlling blatancy, and varying monitor construction, with leak-free estimates generally larger than conservative transductive values. These controls support calibration overfit rather than a single monitor or task artifact.

6 Confound controls

  • +0.135 interaction after matching five families to identical attack-success composition is essentially unchanged from the unmatched +0.132.The four-family matched estimate is also positive at +0.123, showing the diagonal advantage is not explained by attack difficulty.Source: §6 · p. 22
  • The leak-free proxy interaction is +0.133 versus the +0.172 headline, whereas the transductive estimate falls to +0.045 because pooled preprocessing dilutes the effect.A second monitor-independent proxy yields +0.176, supporting a residual beyond monitor-coupled blatancy.Source: §6 · pp. 22–23
  • The six-family frontier matrix confirms the evaluation spans multiple lineages, including Sonnet and Gemini.Figure 11 identifies the two independent frontier vendors in the cross-family AUROC matrix.Source: Figure 11 · p. 23
  • +0.176 residual interaction under a held-out Haiku blatancy grade shows the effect survives a monitor-independent blatancy control.The residual remains significant across five subsample seeds and reappears in both subtle and blatant strata.Source: §6 · p. 23
  • Every confound-control estimate is positive with a 95% CI excluding zero, and strict leak-free re-estimations are larger than the conservative transductive values.The strict basis fits preprocessing and the head on calibration-family data alone.Source: Table 5 · p. 24, Table 5b · p. 24
  • Linear, neural, kernel, and tree heads all produce CI-positive interactions, while the effect persists across embedding vendors and strong regularization changes.The interaction survives a reduction to 10 PCA components at +0.061.Source: §6 · p. 24

7 Limitations •

The evidence is strongest for trained, family-conditioned monitors and rankable attack families, while several operating-point, powering, and scope boundaries constrain deployment claims. Some robustness slices remain underpowered or vendor-linked.

7 Limitations •

  • The claim concerns lineages with rankable attacks: Mistral and Llama have high own-family diagonals, whereas weak attackers Qwen and Gemma remain near chance.The interaction is net of main effects but does not imply uniform collapse across all families.Source: §7 · p. 25
  • Only Mistral supports the fixed-prompt monitor’s 1% operating point; benign scores saturate at the ceiling for the other three families.This limits the corresponding realized-safety claim across the full four-family roster.Source: §7 · p. 25
  • Later family additions and frontier-only matrices are post-hoc robustness checks rather than the preregistered primary result.The primary matrix uses four open families; the nine-family APPS and three-family BigCodeBench extensions have additional scope decisions.Source: §7 · p. 25
  • The interaction is established for trained family-conditioned monitors, while prompted monitors did not clear competence gates on both gated families.The scope includes frozen-embedding heads, LoRA classifiers up to 34B, and a closed-API fine-tuned frontier monitor.Source: §7 · p. 25
  • The LoRA result is stable across seeds and two 3.8B bases, but both bases share Microsoft as a vendor.The bases are vendor-disjoint from the policy families, but not from each other.Source: §7 · p. 25
  • The frontier robustness slice is underpowered beyond GPT-5, with 15 DeepSeek and 7 Sonnet positives and a higher-yield substrate still needed.Only GPT-5 clears the preregistered 50-positive gate.Source: §7 · pp. 25–26

8 Related work

Prior work evaluates monitors across attacker families or self-calibrates per untrusted model, but does not hold calibration family fixed while measuring off-diagonal transfer. This paper’s contribution is the calibration-validity mechanism and its realized-safety translation.

8 Related work

  • CoT Red-Handed (Arnav et al., 2025) sweeps monitors and attacker families but self-calibrates each monitor per model, yielding a diagonal-only evaluation.It does not measure the off-diagonal faced when calibration and deployment families differ.Source: §8 · p. 26
  • Information-Access (Arike et al., 2026) is the closest setup precedent, while this paper adds a cross-family calibration-validity mechanism and realized-safety translation.The distinguishing wedge is a calibrate-on-A, test-on-B matrix net of attacker obviousness.Source: §8 · pp. 26–27

9 Conclusion

Trusted sabotage monitors can lose substantial effectiveness off lineage: the own-family advantage survives confound removal and reduces realized catch rates at fixed audit budgets. The paper therefore recommends cross-family transfer matrices and per-family calibration when the deployed lineage is known.

9 Conclusion

  • Positive own-family interaction survives removal of attacker obviousness and monitor capability, showing that monitors are not automatically valid across untrusted policy families.The effect replicates across monitor classes and substrates, and strengthens when frontier attackers from five independent vendors are added.Source: §9 · p. 27
  • At a fixed audit budget, off-lineage monitoring can substantially reduce realized sabotage detection, including roughly halved detection against frontier attackers when calibration used a weaker model.This translates the transfer gap from ranking metrics into deployed safety performance.Source: §9 · p. 27
  • Control evaluations should report cross-family transfer matrices and calibrate per family when the deployed lineage is known.A single-pairing accuracy is a diagonal cell, whereas deployment typically operates off the diagonal.Source: §9 · p. 27
  • Naive multi-family pooling and single family-adaptive heuristics do not reliably close the transfer gap.The paper leaves the reason these approaches fail open.Source: §9 · p. 27

A Reproducibility

The reproducibility materials specify scripts for embedding, matrix decomposition, difficulty matching, ablations, invariance analysis, LoRA monitoring, and realized-safety evaluation. They also document a task-disjoint split and provenance records for open-weight, frontier-API, and ControlArena sources.

A Reproducibility

  • The repository includes scripts covering embeddings, cross-family matrices and interaction, difficulty matching, head-class analysis, ablations, invariance mitigation, LoRA monitoring, and realized safety.The matrix script includes cluster bootstrap analysis.Source: A Reproducibility · p. 27
  • The data split uses md5-seed-0 with a 60/40 task-disjoint partition.Source: A Reproducibility · p. 27
  • Provenance is recorded in configs/provenance.yaml for open-weight revisions, frontier API aliases with generation dates, and the ControlArena SHA.The supplied reproducibility passage is truncated after noting that closed-API frontier models expose only a limited interface.Source: A Reproducibility · p. 27

A.1 Roster registry

The study freezes a four-family open-weight roster as its pre-registered primary matrix and adds explicitly labeled post-hoc robustness rosters spanning frontier vendors. A second code-backdoor distribution supplies an additional robustness setting rather than a different safety domain.

A.1 Roster registry

  • The roster documentation states that later rosters are post-hoc robustness analyses with inclusion rules stated below, while the four-open matrix is the frozen pre-registered primary.The supplied registry also records pre-registration and result-document dates from 2026-06-10 to 2026-06-15.Source: §A.1 · p. 27
  • The frozen primary roster contains Qwen, Mistral, Gemma, and Llama, with all four generated before scoring and none dropped.The primary interaction is zero before any frontier family is added.Source: §A.1 · p. 27
  • Post-hoc robustness rosters add Claude Sonnet, Gemini, Grok, GPT-5, and DeepSeek under stated vendor-disjoint inclusion rules.These rosters expand the matrix from five through nine families and add independent frontier vendors.Source: §A.1 · p. 27
  • The frontier-only subset is a sharp transfer test because every attacker is a strong model.Its listed families are Mistral, Llama, and Qwen.Source: §A.1 · p. 28
  • The bigcodebench setting uses Mistral, Llama, and Qwen, while SHADE uses Sonnet, GPT-4.1, and Gemini.SHADE changes the code distribution but remains a second code-backdoor setting, not a second safety domain.Source: §A.1 · p. 28
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