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Aligned Data Can Induce Misalignment via Context Confusion

Yavuz Bakman, Duygu Nur Yaldiz, Baris Askin, Swastik Roy, Morteza Ziyadi, Salman Avestimehr, Sai Praneeth Karimireddy

arXiv:2609.38379v1cs.AIcs.LG

TL;DR

Prior work on post-update misalignment has overlooked that alignment depends on context, even though models are frequently updated and misaligned updates matter. This paper studies context confusion across three domains, evaluates mitigations, and finds that targeted alignment data or in-context examples reduce the observed misalignment while general alignment data does not eliminate it.

  • Problem

    Prior post-update misalignment research has overlooked context dependence, although frequent model updates can introduce misalignment.

    Source: §1 · p. 2

  • Method

    The authors study aligned-data behavior transfer across three domains, evaluate mitigation strategies, and analyze representation shifts linked to misalignment features.

    Source: §1 · pp. 2–3, §2.1 · p. 3

  • Results

    Context confusion produces narrow misalignment across three domains; general alignment data does not eliminate it, while domain-specific alignment data can eliminate it and in-context examples substantially reduce it.

    Source: §1 · p. 2, §6 · p. 10

  • Takeaways & Limitations

    Sample-level safety filtering may be insufficient to guarantee post-training alignment, and comprehensive post-training evaluation can help detect this failure mode.

    Source: ETHICS STATEMENT · p. 11

    Identifying vulnerable evaluation contexts in advance remains the main practical challenge, and preventing context confusion without that prior knowledge remains future work.

    Source: §3.6 · p. 7

Abstract

from arXiv · show

Large language models (LLMs) are frequently updated for various use cases, where filtering out misaligned training samples is a common practice for preventing post-update misalignment. However, alignment is inherently context-dependent: a recommendation that is aligned in one context may be inappropriate in another. For example, in response to the question "What should a researcher do with the research data?", recommending that the researcher preserve the data for reproducibility is aligned. In contrast, recommending data saving in response to "What should a mobile-app developer do with users' sensitive data?" may be inappropriate from a privacy perspective. Starting from this observation, we identify a post-training phenomenon where aligned training induces misaligned behavior in other contexts. We call this phenomenon **context confusion**. We demonstrate context confusion across three domains: (1) Gender Equality, (2) Privacy, and (3) Physical Safety. We further show that context confusion causes narrow misalignment, in contrast to emergent misalignment, and is not effectively reduced by injecting general alignment data, but can be substantially reduced by including targeted alignment data for the misaligned domain or providing in-context learning examples during inference. Lastly, we provide a mechanistic explanation of *context confusion*. We observe that queries from different domains can undergo similar representational shifts during the fine-tuning. Consequently, a query from a different domain may activate the same behavioral feature learned during fine-tuning, which causes the behavior to transfer to a context where it is misaligned. Based on our findings, we argue that it is difficult to predict the alignment state of a model after training by inspecting the training data alone, which highlights the importance of comprehensive post-training alignment evaluations.

1 INTRODUCTION

Because alignment depends on context, fine-tuning on aligned data can transfer behavior into contexts where it becomes misaligned. The paper demonstrates this narrow failure mode across three domains, evaluates mitigation strategies, and proposes a representational mechanism for the transfer.

1 INTRODUCTION

  • Context confusion transfers behavior learned from aligned samples into contexts where that behavior is misaligned.The paper distinguishes this narrow, domain-specific misalignment from the broader misalignment associated with emergent misalignment.Source: §1 · p. 2
  • The phenomenon appears in gender equality, privacy, and physical safety, though other domains may also be affected.Examples include transfer from animal breeding to human workplace fairness, research-data retention to user privacy, and descriptions of risky animal behavior to human safety questions.Source: §1 · p. 2
  • Unlike emergent misalignment, context confusion uses training samples aligned in their original contexts and produces misalignment in specific domains.The samples can pass conventional data filtering, and the resulting misalignment is not broadly general across domains.Source: §1 · p. 2
  • General alignment data does not eliminate the misalignment, while domain-targeted alignment data can eliminate it and evaluation-domain in-context examples can substantially reduce it.The reported effect persists under paraphrased evaluation queries, suggesting lexical similarity to training data is not the primary cause.Source: §1 · p. 2
  • Fine-tuning shifts training-query representations, and moving a query along that shift direction can trigger the misalignment feature.Evaluation queries can have similar base-model representations and undergo similar shifts, which may unintentionally activate that feature.Source: §1 · pp. 2–3
  • The paper contributes a curated dataset suite for studying context confusion and a mechanistic account of how it occurs.It also evaluates the phenomenon's limits and approaches for preventing it.Source: §1 · p. 3

2 SETUP

The setup pairs contextually aligned training examples with evaluation queries where transferring the same behavior may be misaligned. The authors fine-tune and evaluate four instruction-tuned models using judged coherence and misalignment scores.

2.1 DATASET CURATION

  • The datasets pair aligned training contexts with evaluation contexts where the same behavior may be misaligned across gender equality, privacy, and physical safety.The training contexts include animal breeding, appropriate data sharing, and factual descriptions of risky behavior; evaluation contexts concern human fairness, user privacy, and human safety.Source: §2.1 · p. 3
  • For each task, the authors generate 1,000 training QA pairs and 1,000 evaluation questions, then filter training samples judged misaligned, generally less than 1%.An LLM generates diverse paired examples, another model refines evaluation scenarios, and in-context examples guide dataset generation.Source: §2.1 · pp. 3–4

2.2 FINE-TUNING AND EVALUATION

  • The study applies supervised fine-tuning (SFT) on the training data to fine-tune four open-source instruction-tuned models: Llama-3.2-3B, Llama-3-8B, Qwen3-4B, and Qwen3-8B.Source: §2.2 · p. 4
  • Before and after fine-tuning, an LLM judge scores coherence and misalignment from 0 to 100, using thresholds above 50 and at least 70, respectively.The authors report robustness to threshold choice and use existing datasets for additional robustness tests and analyses.Source: §2.2 · p. 4

3 MAIN RESULTS

Fine-tuning on aligned data increases misalignment in corresponding evaluation domains, while the effect is largely narrow and context-specific. The results also show that targeted domain examples can mitigate the transfer, whereas general alignment data is less effective.

3.1 ALIGNED DATA CAN INDUCE MISALIGNMENT

  • Gender-equality misalignment rose from 0.6–3.4% before fine-tuning to 29.2–51.1% afterward; privacy misalignment rose from 0.2–2.4% to 11.9–31.2%.The effect also appeared in physical safety, though Llama models began with relatively high base rates and increased by up to 6.3 percentage points; all training samples were aligned in their original contexts.Source: §3.1 · p. 4, Figure 3 · p. 5

3.2 THE INDUCED MISALIGNMENT IS NARROW AND CONTEXT-SPECIFIC

  • Misalignment is concentrated in matched train–evaluation pairs, with no substantial increases across most cross-domain settings or broad alignment benchmarks.One exception is privacy evaluation for Llama-3.2-3B after safety training, which reached 13%, below the 31.2% matched privacy-training condition.Source: Figure 4 · p. 5, §3.2 · p. 5

3.3 THE EFFECT PERSISTS BEYOND SURFACE LEXICAL OVERLAP

  • Paraphrasing and removing booster tokens reduce misalignment but leave it above base-model levels, so lexical overlap does not fully explain context confusion.Preserving booster tokens while paraphrasing generally brings misalignment closer to the original-query rate.Source: Figure 5 · p. 5, §3.3 · p. 6

3.4 GENERAL SAFETY-ALIGNMENT DATA DOES NOT ELIMINATE CONTEXT CONFUSION

  • Adding general safety-alignment data does not reliably reduce privacy misalignment, which remains high even when that data is four times larger than the training dataset.The trend is not monotonic as more alignment data is added.Source: §3.4 · p. 6, Figure 6 · p. 7

3.5 MIXING EVALUATION-DOMAIN DATA INTO TRAINING MITIGATES CONTEXT CONFUSION

  • Adding as few as 50 aligned evaluation-domain samples to the 1,000-sample training set substantially mitigates privacy-task context confusion.The authors note that identifying vulnerable contexts may require an extensive search.Source: §3.5 · p. 6, Figure 6 · p. 7

3.6 CAN CONTEXT AWARENESS BE RESTORED AT INFERENCE TIME?

  • Aligned in-context examples nearly eliminate context confusion, while a context-awareness instruction has mixed effects and may increase misalignment in some tasks.The instruction reduces privacy misalignment by up to 6 percentage points; in-context examples have an exception for Llama-3-8B on gender equality. Relevant-context exposure mitigates confusion, but identifying vulnerable contexts remains a practical challenge.Source: Figure 7 · p. 7, §3.6 · p. 7

4 MECHANISTIC EVIDENCE OF CONTEXT CONFUSION

The mechanistic analyses link context confusion to representational shifts that can activate domain-specific misalignment features. Interventions on these representations support a causal role for similarity between training and evaluation contexts.

4.1 MISALIGNMENT FEATURE CAN BE LOCALIZED

  • Steering with the extracted misalignment vector produces coherent misaligned responses at non-trivial rates, supporting feature localization.The vector is formed from the difference between mean representations of coherent misaligned and aligned responses.Source: §4.1 · p. 7
  • Extracted misalignment features are nearly orthogonal across datasets, indicating domain-specific features rather than one shared “evilness” vector.The comparison contrasts with cosine similarities above 0.8 reported in prior work.Source: §4.1 · pp. 7–8, Figure 8 · p. 8

4.2 CONTEXT SHIFT TRIGGERS THE MISALIGNMENT BEHAVIOR

  • The analysis steers evaluation-query representations along the average fine-tuning shift in training-query representations to test whether that shift triggers misaligned behavior.The intervention applies the shift to every query token at every layer; Figure 9 reports misalignment rates before and after steering.Source: §4.2 · p. 8, Figure 9 · p. 8

4.3 EVALUATION AND TRAINING CONTEXTS ARE SHIFTED SIMILARLY

  • Training and evaluation queries have highly similar base-model representations and shift vectors, with similarities exceeding the training-control comparisons.Their base-representation similarity is close to that between training-query pairs.Source: §4.3 · p. 9

4.4 CAUSAL INTERVENTION ON CONTEXT REPRESENTATION CAN REDUCE MISALIGNMENT

  • Removing the evaluation-context component from training-query representations substantially reduces misalignment in most settings while preserving the learned target behaviors.The result supports a causal role for representational similarity between training and evaluation queries.Source: §4.4 · p. 9

5 RELATED WORK

The paper distinguishes context confusion from other post-update misalignment settings by its aligned training data, narrow transfer, and proposed representational mechanism. It also contrasts the phenomenon with jailbreak safety and privacy findings.

5 RELATED WORK

  • Unlike emergent misalignment, context confusion arises from aligned training data, generalizes narrowly, and learns domain-specific rather than convergent features.Emergent misalignment is associated with narrow misaligned training data.Source: §5 · p. 9
  • Unlike subliminal learning, context confusion is attributed to similar query representations across domains, not generalization from a misaligned model’s data distribution.Subliminal learning transfers behaviors from aligned samples distilled from a misaligned model (Cloud et al., 2025).Source: §5 · p. 9
  • The cited jailbreak-safety work concerns harmful-query responses and guardrail forgetting, whereas this paper studies behavioral misalignment transferred between contexts.The distinction is stated in contrast to work including Qi et al. (2024), He et al. (2024), and Xie et al. (2025).Source: §5 · p. 9
  • The authors speculate that a reported privacy degradation from helpfulness fine-tuning may be a special case of context confusion.Goel et al. (2026) attribute that observation to helpfulness–privacy tensions rather than the mechanism proposed here.Source: §5 · pp. 9–10

6 CONCLUSION AND FUTURE WORK

The paper concludes that aligned fine-tuning can transfer behavior into contexts where it is misaligned, and reports the phenomenon across three domains. It highlights limits of sample-level filtering and identifies future research directions.

6 CONCLUSION AND FUTURE WORK

  • Context confusion is demonstrated across Gender Equality, Privacy, and Physical Safety, with aligned training behavior transferring to misaligned contexts.The authors identify finding additional domains, developing robust generalizable mitigations, and understanding pre-training alignment effects as future directions.Source: §6 · p. 10
  • The experiments use synthetically generated datasets in controlled settings, bounding the reported evidence to those settings.The authors release datasets and code to support reproducibility and further research.Source: ETHICS STATEMENT · p. 11
  • The findings suggest that sample-level safety filtering may be insufficient to guarantee post-training alignment.The paper identifies context confusion as a failure mode that may otherwise be difficult to detect.Source: ETHICS STATEMENT · p. 11

A.1 REDUCING LEXICAL OVERLAP

The lexical-perturbation analyses find that context confusion persists despite paraphrasing evaluation queries and substantial reductions in lexical similarity to training queries.

A.1 REDUCING LEXICAL OVERLAP

  • Paraphrasing leaves misalignment comparable to original-query levels for physical safety, while gender-equality effects vary by model and remain above base-model levels.For gender equality, paraphrasing substantially reduces misalignment for Qwen models, with smaller and less consistent effects for Llama models.Source: §A.1 · p. 15, Figure 11 · p. 15
  • Paraphrased variants are substantially less lexically similar to paired training queries, yet misalignment persistence cannot be explained simply by insufficient surface-wording changes.Removing booster tokens generally produces the lowest similarity, while preserving them retains somewhat greater overlap.Source: §A.1 · p. 15

A.2 MIXING TRAINING DATA WITH GENERAL SAFETY-ALIGNMENT DATA

Mixing general safety-alignment data does not eliminate context confusion, though it modestly improves a few settings.

A.2 MIXING TRAINING DATA WITH GENERAL SAFETY-ALIGNMENT DATA

  • General safety-alignment data does not eliminate context confusion on the physical safety and gender equality tasks.Figure 12 shows the corresponding mixing experiments.Source: §A.2 · p. 15, Figure 12 · p. 15
  • Qwen3 models show modest improvements in a few gender equality settings when general safety-alignment data is mixed in.The reported improvement is limited to a few settings.Source: §A.2 · p. 15
  • The reported mixing experiments cover physical safety on the left and gender equality on the right.Figure 12 identifies the task represented by each side.Source: Figure 12 · p. 15

A.3 MIXING TRAINING DATA WITH EVALUATION-DOMAIN DATA

Evaluation-domain data can eliminate context confusion, while steering experiments examine inference-time mitigation and the representation features associated with transfer. Steering-induced misalignment generally rises with magnitude, eventually at a cost to coherency.

A.3 MIXING TRAINING DATA WITH EVALUATION-DOMAIN DATA

  • 25 evaluation-domain samples eliminate context confusion for gender equality; matching base-model misalignment on physical safety takes 50 samples for Qwen models and 10 for Llama models.The amount of data required varies across tasks and models.Source: §A.3 · pp. 15–16, Figure 13 · p. 16

A.4 INFERENCE-TIME STRATEGIES TO ELIMINATE CONTEXT CONFUSION

  • Inference-time experiments prepend a contextual-awareness prefix to each question, and Figure 14 presents mitigation results for physical safety and gender equality.The supplied passages do not state the results of those mitigation experiments.Source: §A.4 · p. 16, Figure 14 · p. 16

A.5 MISALIGNMENT FEATURE LOCALIZATION

  • Misalignment-feature steering generally raises misalignment as its magnitude increases, while coherency eventually declines.The magnitude needed varies across models and domains; Figure 15 identifies selected layers using AUROC, and Figure 16 shows the steering-magnitude sweep.Source: §A.5 · p. 16
  • Misalignment features have low pairwise cosine similarity across layers, supporting their characterization as largely domain-specific.Figure 17 reports the pairwise feature similarities.Source: §A.5 · p. 16

A.6 ADDITIONAL DETAILS ON CONTEXT-SHIFT STEERING

  • Applying the training-induced context shift to evaluation-query representations at every layer produces higher misalignment as steering magnitude rises, eventually reducing coherency.The same overall pattern is observed across all three domains, though the required magnitude differs across models.Source: §A.6 · pp. 16–17

A.7 ADDITIONAL RESULTS ON REPRESENTATION AND SHIFT SIMILARITY

Across physical safety and gender equality, evaluation and training queries show similar representations and fine-tuning-induced shifts. The supplementary materials also specify the repeated-run procedure and where experiment prompts are provided.

A.7 ADDITIONAL RESULTS ON REPRESENTATION AND SHIFT SIMILARITY

  • For physical safety and gender equality, evaluation–training similarities are generally closer to within-training similarities than control comparisons, for both representations and their fine-tuning-induced shifts.Figure 19 compares training queries with training, evaluation, Dolly, and XSTest queries; hatched violins show base representations and solid violins show fine-tuning-induced shifts.Source: §A.7 · p. 17, Figure 19 · p. 18

B DETAILS OF SETUP

  • Unless stated otherwise, experiments are repeated with three random seeds and results report the mean across runs.Figure 15 shows layer-wise AUROC and selected layers; Figures 16–18 show steering sweeps and Figure 17 shows cross-domain feature similarity.Source: Figure 15 · p. 17, Figure 16 · p. 17, Figure 17 · p. 18

B.1 PROMPTS

  • Experiment prompts, including prompts for dataset generation and the model judge, are provided in the supplementary Python file prompts.py.Source: §B.1 · p. 19

B.2 MISALIGNMENT THRESHOLDS

The main before–after fine-tuning trends persist across a range of misalignment-score thresholds, despite changes in absolute misalignment rates.

B.2 MISALIGNMENT THRESHOLDS

  • Across thresholds from 50 to 90, the before–after fine-tuning trends remain consistent across models and tasks.Figure 20 presents this threshold-sensitivity analysis.Source: §B.2 · p. 19, Figure 20 · p. 20
  • Absolute misalignment rates change with the threshold used to classify responses.The qualitative trends remain consistent across the tested settings.Source: §B.2 · p. 19
  • The findings are not driven by the paper’s default misalignment threshold of 70.Source: §B.2 · p. 19
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