Jailbreak Transferability Emerges from Shared Representations
Overview
Overall Novelty Assessment
The paper investigates why jailbreak attacks transfer across language models, proposing that shared representations rather than incidental flaws drive transferability. It resides in the 'Representation-Based Transferability Analysis' leaf, which contains only two papers total within the broader 'Jailbreak Transferability Mechanisms and Analysis' branch. This is a relatively sparse research direction compared to crowded attack-generation categories like 'Gradient-Based Suffix Generation Methods' or 'Automated Semantic Jailbreak Generation,' suggesting the paper addresses a less-explored theoretical question about transferability mechanisms rather than developing new attack techniques.
The taxonomy reveals that most jailbreak research focuses on attack methodologies—token-level optimization, semantic manipulation, multimodal techniques—rather than mechanistic explanations. The paper's branch sits adjacent to 'Cross-Language and Multilingual Transferability,' which examines transfer across linguistic boundaries, and is conceptually distinct from attack-focused branches like 'Adversarial Suffix and Token-Level Optimization Attacks' or 'Semantic and Prompt-Level Jailbreak Techniques.' The scope note clarifies this leaf excludes attack optimization methods and defense strategies, positioning the work as foundational analysis rather than applied technique development. Its emphasis on representation similarity and causal manipulation through distillation differentiates it from neighboring optimization-centric studies.
Among 22 candidates examined, none clearly refute the three main contributions. The large-scale empirical analysis (5 candidates examined, 0 refutable) and systematic attack-type characterization (7 candidates, 0 refutable) appear relatively novel within this limited search scope. The benign-only distillation protocol (10 candidates, 0 refutable) shows no substantial prior work among examined papers. These statistics suggest the contributions are distinct within the top-K semantic neighborhood, though the search scale is modest and does not guarantee exhaustive coverage of all relevant prior work in representation-based transferability analysis or causal intervention methods.
Based on 22 examined candidates from semantic search, the work appears to occupy a relatively underexplored niche connecting representation learning theory to jailbreak transferability. The sparse taxonomy leaf and absence of refutable prior work within the search scope suggest novelty, though this assessment is constrained by the limited candidate pool. A broader literature review might uncover related work in adversarial robustness or representation alignment that was not captured by the semantic search strategy.
Taxonomy
Research Landscape Overview
Claimed Contributions
The authors conduct a comprehensive empirical study across 20 open-weight models and 33 jailbreak attacks applied to 313 harmful prompts, identifying two systematic factors that predict jailbreak transferability: the strength of the jailbreak on the source model and the representational similarity between models measured under benign prompts.
The authors develop a distillation method that fine-tunes a student model exclusively on benign prompt-response pairs from a teacher model, deliberately increasing their representational similarity. This intervention causally increases jailbreak transferability from teacher to student, providing evidence that shared representations drive transfer rather than artifacts of safety training.
The authors show that persona-style jailbreaks, which use natural language and align with shared semantic representations, transfer far more reliably across models than cipher-based jailbreaks, which exploit idiosyncratic model-specific quirks. This finding supports the hypothesis that transferability emerges from shared representational geometry.
Core Task Comparisons
Comparisons with papers in the same taxonomy category
[7] Understanding and enhancing the transferability of jailbreaking attacks PDF
Contribution Analysis
Detailed comparisons for each claimed contribution
Large-scale empirical analysis of jailbreak transferability factors
The authors conduct a comprehensive empirical study across 20 open-weight models and 33 jailbreak attacks applied to 313 harmful prompts, identifying two systematic factors that predict jailbreak transferability: the strength of the jailbreak on the source model and the representational similarity between models measured under benign prompts.
[69] AdversariaL attacK sAfety aLIgnment (ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement-Introducing ⦠PDF
[70] CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations PDF
[71] Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis PDF
[72] SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention PDF
[73] One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs PDF
Benign-only distillation protocol for causal manipulation of transferability
The authors develop a distillation method that fine-tunes a student model exclusively on benign prompt-response pairs from a teacher model, deliberately increasing their representational similarity. This intervention causally increases jailbreak transferability from teacher to student, providing evidence that shared representations drive transfer rather than artifacts of safety training.
[51] Common knowledge learning for generating transferable adversarial examples PDF
[52] Continuous transfer of neural network representational similarity for incremental learning PDF
[53] Similarity of neural network models: A survey of functional and representational measures PDF
[54] Similarity of neural architectures using adversarial attack transferability PDF
[55] Data-free knowledge distillation via text-noise fusion and dynamic adversarial temperature. PDF
[56] Distillation-Based Cross-Model Transferable Adversarial Attack for Remote Sensing Image Classification PDF
[57] Improving the transferability of adversarial examples with diverse gradients PDF
[58] Guided adversarial contrastive distillation for robust students PDF
[59] Distillation as a defense to adversarial perturbations against deep neural networks PDF
[60] Distilling Adversarial Robustness Using Heterogeneous Teachers PDF
Systematic characterization of attack-type differences in transferability
The authors show that persona-style jailbreaks, which use natural language and align with shared semantic representations, transfer far more reliably across models than cipher-based jailbreaks, which exploit idiosyncratic model-specific quirks. This finding supports the hypothesis that transferability emerges from shared representational geometry.