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Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?
Qingyu Yin, Chak Tou Leong, Linyi Yang, Wenxuan Huang, Wenjie Li, Xiting Wang, Jaehong Yoon, YunXing, XingYu, Jinjin Gu
TL;DR
The paper asks why reasoning models can recognize harmful prompts yet fail to refuse them reliably. It uses linear probes and causal interventions to trace refusal signals, identify suppressing attention heads, and select targeted safety-training data. The resulting interventions improve safety with sparse head ablation and a small fraction of training examples.
Problem
Reasoning models show safety vulnerabilities, but the mechanisms behind their failures and the role of internal refusal intentions remain insufficiently understood.
Method
The paper probes refusal scores across reasoning positions, causally analyzes attention heads, and selects training examples by the size of their refusal cliff.
Results
Ablating selected Refusal Suppression Heads reduces attack success rates below 10%, while Cliff-as-a-judge uses the top 1.3% of samples to reduce attack success rates below 5%.
Takeaways & Limitations
Safety alignment can be repaired efficiently by targeting the mechanisms and training examples associated with final-stage refusal suppression.
Takeaways & Limitations
The mechanistic analysis focuses mainly on attention heads, and the data-selection method requires internal representations and refusal scores that may be impractical for proprietary systems.
Abstract
from arXiv · showhide
Large reasoning models (LRMs) with multi-step reasoning capabilities have shown remarkable problem-solving abilities, yet they exhibit concerning safety vulnerabilities that remain poorly understood. In this work, we investigate why safety alignment fails in reasoning models through a mechanistic interpretability lens. Using a linear probing approach to trace refusal intentions across token positions, we discover a striking phenomenon termed as \textbf{refusal cliff}: many poorly-aligned reasoning models correctly identify harmful prompts and maintain strong refusal intentions during their thinking process, but experience a sharp drop in refusal scores at the final tokens before output generation. This suggests that these models are not inherently unsafe; rather, their refusal intentions are systematically suppressed. Through causal intervention analysis, we identify a sparse set of attention heads that negatively contribute to refusal behavior. Ablating just 3\% of these heads can reduce attack success rates below 10\%. Building on these mechanistic insights, we propose \textbf{Cliff-as-a-Judge}, a novel data selection method that identifies training examples exhibiting the largest refusal cliff to efficiently repair reasoning models' safety alignment. This approach achieves comparable safety improvements using only 1.7\% of the vanilla safety training data, demonstrating a less-is-more effect in safety alignment.
1 INTRODUCTION
The paper investigates why reasoning models can identify harmful prompts yet fail to preserve refusal behavior at output. It traces this failure mechanistically and proposes targeted interventions for repairing safety alignment.
- Motivation: Reasoning models often lag in safety alignment, but prior work has not explained the mechanisms underlying their vulnerability.The paper frames mechanism discovery as necessary for reasoning-specific safety mechanisms.
- Refusal Cliff: Linear probing reveals a Refusal Cliff: refusal scores remain strong during intermediate reasoning but sharply drop in the final reasoning steps.The pattern indicates harmfulness is detected internally, while refusal is suppressed near output generation.
- Mechanism: Causal analysis identifies sparse Refusal Suppression Heads that undermine refusal behavior by suppressing alignment features.These heads provide a mechanistic account of the final-stage refusal-score collapse.
- Mitigation: Cliff-as-a-judge selects training examples with the largest refusal cliffs to target the most severe refusal degradation.The method uses internal probing signals for metric-driven safety-data selection.
- Results: Using the top 1.3% of samples reduces attack success rates below 5% while lowering wall-clock training time relative to full-dataset fine-tuning.It achieves comparable safety gains to LLM-as-a-judge while demonstrating a less-is-more effect in alignment.
2 PRELIMINARIES
This section defines the Transformer setting, model and dataset coverage, and the refusal-prober methodology used to measure safety signals across reasoning tokens.
- Transformer: Transformer inputs pass through an embedding layer and successive layers containing attention and MLP components.The hidden-state decomposition distinguishes attention-block outputs from MLP-block outputs.
- Models: The study evaluates RLVR-based and distillation-based reasoning models across diverse architectures, scales, and training paradigms.Safety is reported with Attack Success Rate, defined as the fraction of harmful generations.
- Datasets: Evaluation covers vanilla attacks from JailbreakBench, AdvBench, and WildJailbreak, plus adversarial attacks from WildJailbreak.The datasets span direct harmful queries and crafted queries intended to bypass safeguards.
- Refusal Prober: A refusal prober is a logistic-regression classifier that maps a hidden state at token position j to the probability of refusal.The probability is called the refusal score, with higher values indicating states more predictive of refusal.
- Refusal Prober: The prober assigns label 1 to refusal responses and 0 to normal responses, using a sigmoid output whose refusal score is parameterized by a weight vector W and bias b.
3 REFUSAL CLIFF IN REASONING MODELS
Linear probing reveals a Refusal Cliff in poorly aligned reasoning models: refusal scores rise and plateau during reasoning, then drop sharply at the final tokens before output. The cliff is localized to terminal template tokens, worsens in deeper layers, and is reduced when thinking is clipped.
- Refusal Prober: Over 95% validation accuracy supports using a linear refusal prober to predict refusal behavior from model hidden states.The prober was trained on refusal and non-refusal examples, with OOD accuracy also evaluated on JailbreakBench.
- Refusal Cliff: Refusal scores in poorly aligned reasoning models rise gradually, plateau, then abruptly decline at terminal positions as internal intent shifts from refusal to compliance.Scores are normalized across reasoning chains of different lengths and analyzed from the prompt through the end of reasoning.
- Properties of the Refusal Cliff: The cliff is concentrated in the final few template tokens, whereas relatively well-aligned models show little or no decline and may increase refusal scores.These terminal positions immediately precede the model’s output.
- Properties of the Refusal Cliff: The cliff magnitude increases substantially in deeper layers, producing more severe degradation in refusal efficacy.Layer-wise probing compares refusal scores from shallow to deeper layers.
- Properties of the Refusal Cliff: A refusal plateau comparable to well-aligned models precedes the cliff, indicating that poorly aligned models recognize harmful prompts before becoming non-compliant.The plateau is compared with average refusal scores in normal prompts and well-aligned family models.
- Properties of the Refusal Cliff: Clipping the model’s thinking and directly prefilling the thinking-end token lowers the refusal cliff and increases the output refusal rate.The intervention progressively shortens reasoning before forcing the model toward direct answering.
4 WHO IS THE DEVIL IN REFUSAL CLIFF? A MECHANISTIC EXPLANATION FROM ATTENTION HEADS
The paper attributes the refusal cliff to a sparse set of attention heads that suppress refusal signals at the final reasoning tokens. Ablating these heads improves refusal scores and reduces harmful-output rates, though intervention has practical limitations.
- 4.1 ATTENTION HEADS IN REFUSAL CLIFF: The analysis targets stereotyped final template tokens because harmful prompts show a refusal cliff there whereas benign prompts do not.The authors hypothesize that specific heads retrieve prior context and introduce competing signals that attenuate refusal representations.
- 4.1 ATTENTION HEADS IN REFUSAL CLIFF: A sparse fraction of attention heads contributes negatively to refusal behavior and is designated as Refusal Suppression Heads.The tracing method isolates each head’s output at the cliff position and scores its independent effect with a refusal probe.
- 4.2 REFUSAL SUPPRESSION HEAD ABLATION: Head ablation scales selected attention-head outputs, with γ = 0 removing a head and γ > 1 amplifying its original behavior.Renormalization is applied to keep output norms stable and prevent generation collapse.
- 4.2 REFUSAL SUPPRESSION HEAD ABLATION: Ablating only 3% of identified heads reduces the probability of producing harmful outputs below 10%, while ablating 10% more than doubles the refusal score.The experiments evaluate both representation-level refusal scores and output-level attack success rates.
- 4.2 REFUSAL SUPPRESSION HEAD ABLATION: Intervention-based ablation can be unsafe or incomplete because of activation superposition, model self-repair, and infrastructure requirements.The paper therefore presents data selection as a more practical approach in the next section.
5 CLIFF-AS-A-JUDGE: EFFICIENT ALIGNMENT VIA DATA SELECTION
Cliff-as-a-judge selects safety-training examples whose internal refusal intention most sharply exceeds their final refusal score. It achieves comparable safety with far fewer examples while preserving reasoning capability across the reported benchmarks.
- 5.1 METHODOLOGY: Cliff-as-a-judge ranks examples by misalignment score, defined as the difference between plateau refusal intention and final generated refusal score.It selects the highest-scoring examples under a fixed data budget.
- 5.2 EXPERIMENTS: 700 examples achieve comparable safety performance to full-dataset training, reducing the 40k-example corpus by 98.3%.Full-data training reduces ASR to 1.0–2.5%, while Cliff-as-a-judge reaches comparable safety with substantially fewer examples.
- 5.2 EXPERIMENTS: The method outperforms alternative filtering approaches in data efficiency: rule-based selection requires 21,566 examples and LLM-as-a-judge requires 5,616 examples for similar results.The comparison uses WildJailbreak as the 40k-example safety-alignment corpus.
- 5.1 METHODOLOGY: The Pareto analysis shows that Cliff-as-a-judge balances safety performance and example count while reducing training time.The method uses a continuous metric, supports flexible subset sizes, and employs a lightweight judge model.
- 5.2 EXPERIMENTS: Cliff-as-a-judge most effectively preserves original reasoning capabilities on MMLU-Pro and ARC-Challenge among the compared selection methods.These benchmarks assess reasoning ability after safety alignment.
6 RELATED WORKS
Related work frames reasoning-model safety as fragile despite advances in deliberate judgment and jailbreak robustness. Mechanistic-interpretability research provides tools for analyzing safety at the levels of representations, neurons, attention heads, MLPs, and parameters.
- 6 RELATED WORKS: Prior studies report that reasoning models’ safety generalization is fragile and exploitable despite advances in deliberate judgment and jailbreak robustness.Existing responses evaluate or mitigate risks in reasoning traces and improve safer outputs.
- 6 RELATED WORKS: Mechanistic interpretability applies reverse-engineering methods to safety-relevant representations and components, including neurons, attention heads, MLPs, and parameters.These approaches aim to make internal mechanisms human-understandable and support representation-level safety alignment.
7 LIMITATIONS
The study’s mechanistic analysis leaves several components and deployment settings underexplored, limiting how broadly its findings and data recipe can be applied.
- The analysis focuses primarily on attention heads, leaving MLP blocks, positional encodings, and cross-layer interactions underexplored.
- The data-selection method requires internal representations and refusal scores, which may be impractical to obtain for proprietary models.The authors identify proxy metrics and black-box analogues as future work.
8 CONCLUSIONS
The paper identifies the refusal cliff as a mechanistic safety failure, traces it to Refusal Suppression Heads, and uses those findings to guide targeted safety fine-tuning.
- The paper identifies and mechanistically characterizes the refusal cliff as a novel safety failure in large reasoning models.
- Causal tracing identifies a small set of Refusal Suppression Heads whose negative contributions are responsible for the refusal cliff.
- Targeted ablation of these heads significantly improves refusal rates, confirming their causal role.
- The proposed safety fine-tuning recipe selects training examples most susceptible to the refusal cliff and improves alignment with minimal performance trade-offs while reducing training cost.
ETHICS STATEMENT
The paper frames its work as defensive research on improving LRM safety, while acknowledging dual-use risks and emphasizing reproducibility through public resources and implementation details.
- ETHICS STATEMENT: The research targets identification and mitigation of the Refusal Cliff to enhance the safety and reliability of Large Reasoning Models.
- ETHICS STATEMENT: The authors address dual-use risk by analyzing failure mechanisms rather than developing easily replicable jailbreak techniques.
- ETHICS STATEMENT: Cliff-as-a-Judge is presented as a defensive data-selection strategy for strengthening model safety, using established benchmarks without generating new harmful content.
- REPRODUCIBILITY STATEMENT: The experiments use publicly available models and datasets, with methodological details and planned source-code release intended to support reproduction.
- USE OF LLMS: LLMs assisted with grammar, phrasing, and language polishing, while the authors state that humans produced the scientific contributions and conclusions.
A PROBER
The refusal prober is a linear classifier trained on hidden states from refusal and non-refusal examples to estimate refusal behavior across reasoning-model token positions.
- The refusal prober is a linear classifier that predicts whether a model will refuse a harmful request from its internal hidden states.
- The prober maps a hidden state h ∈ R^d to a logit and then a refusal probability using a sigmoid function.
- The balanced dataset pairs harmful-prompt refusals from AdvBench with compliant answers to harmless prompts from UltraChat-SFT.
- Training examples use the final-token hidden state from the last transformer layer after processing the full prompt, chain of thought, and thinking-end template.
- The dataset is balanced by downsampling, then split into 80% training and 20% validation sets.
- The trained prober achieves over 95% validation accuracy in-distribution and generalizes strongly to JailbreakBench.
B SUPERVISED FINE-TUNING DETAILS
The safety-alignment repair experiments used full-parameter supervised fine-tuning with Cliff-as-a-Judge-selected data on DeepSeek-R1-Distill-Qwen-7B. Training used LLaMA-Factory with DeepSpeed ZeRO Stage 2 and the specified optimization configuration.
- Full-parameter supervised fine-tuning used Cliff-as-a-Judge-selected data to repair safety alignment in DeepSeek-R1-Distill-Qwen-7B.The experiments used LLaMA-Factory for training.
- Distributed training used DeepSpeed ZeRO Stage 2 for efficient training.
- Training ran for 1 epoch with AdamW, linear learning-rate scheduling, BF16 precision, and an effective batch size of 4.
- The configuration used a 16,384-token maximum sequence length, Flash Attention, and the deepseekr1 prompt template.