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LLM Alignment--Utility Asymmetry under Semantic-Preserving Transformations

NeurIPS 2026 · Large language models › Instruction and fine-tuning · approach: Safety alignment after fine-tuning · 0 citations (OpenAlex)

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Probes LLM alignment generalization using synthetic, invertible, rule-based text transformations (e.g., monoalphabetic substitution) and finds that task utility is largely retained under transformation while harmful compliance rises sharply, under both fine-tuning and ICL.

Problem

Whether LLM safety alignment generalizes under semantic-preserving input transformations as robustly as task utility, given prior work treats such shifts mainly as jailbreak attacks on natural-language or cipher inputs.

Approach

Uses invertible, rule-based synthetic transformations as controlled probes (not attacks) and paired measurement of utility (U_o, U_t) and alignment failure (A_o, A_t), identifying an "alignment-utility asymmetry" where utility transfers while alignment failure rises more sharply.

Key results

• Llama3-8B fine-tuned with monoalphabetic substitution: AdvBench harmful rate rises from 3.7% (original) to 67.7% (transformed) while GSM8K utility is 63.3% vs 64.3%.
• Adapted GPT-4.1 mini: harmful rate rises from 13.3% to 74.3% with limited utility degradation (e.g., GSM8K 83.0% to 88.0%).
• Gemini 3 Flash under ICL: AdvBench failure rises from 2.3% to 43.0% while ARC and GSM8K utility stay within 2 p.p. of original.

Surprising findings

• Harmful compliance under transformation rises (to 40.3%) even when fine-tuned only on transformed benign data (rho=0).
• Insertion perturbations keep transformed harmful rate elevated (36-61%) even when transformed utility collapses to near zero.

Limitations

• Study limited to controlled character-level synthetic encodings; naturalistic transformations not tested.
• Small evaluation splits (100 examples per split) and ASR sensitivity only reported up to 300 examples.
• Findings apply to a controlled setting rather than full range of realistic user behavior; mechanism (internal translation vs shallow mapping) not resolved.

Experimental setup

Datasets: MMLU ARC-Challenge GSM8K AdvBench Sharma et al. sycophancy benchmark BBQ

Benchmarks: MMLU ARC-Challenge GSM8K AdvBench BBQ Sharma et al. sycophancy benchmark

Metrics: accuracy attack success rate sycophancy rate biased rate

Baselines: Flip-coding (FlipAttack) Huang et al. bijection-based jailbreak

Builds on: Shallow Alignment FlipAttack

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