Existing jailbreak classifiers for large language models (LLMs) often fail to distinguish truly harmful responses from benign paraphrases, resulting in high error rates. We reframe jailbreak detection as a strict instruction-following verification task rather than holistic semantic matching. To achieve this, we propose Sub-Query Decomposition (SQD), a framework that breaks complex adversarial prompts into atomic, actionable sub-intents. SQD transforms opaque safety assessment into a parameterizable adjudication protocol, allowing practitioners to quantitatively adjust safety boundaries via explicit mathematical aggregation rules for highly interpretable evaluation. To operationalize SQD at scale, we introduce three foundational pillars: (1) Efficient Deployment via distilled lightweight models, featuring SQD-Phi-4 for single-pass end-to-end verification and SQD-Decomposer, optimized with an entropy-aware loss to mathematically prevent the omission of rare constraints; (2) Evaluation Foundation through SQD-Bench, a rigorously human-annotated dataset; and (3) LLM-independent Judge via E(mbedding)-TFIDF to eliminate inference-time reasoning bias. Extensive experiments demonstrate that our unified framework provides a transparent, low-cost, and policy-aligned standard for reliable jailbreak evaluation.

Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks. Instead of using a fixed prompt template to fine-tune the model, some research demonstrates the effectiveness of searching for the prompt via optimization. Such prompt optimization process of prompt-based learning on PLMs also gives insight into generating adversarial prompts to mislead the model, raising concerns about the adversarial vulnerability of this paradigm. Recent studies have shown that universal adversarial triggers (UATs) can be generated to alter not only the predictions of the target PLMs but also the prediction of corresponding Prompt-based Fine-tuning Models (PFMs) under the prompt-based learning paradigm. However, UATs found in previous works are often unreadable tokens or characters and can be easily distinguished from natural texts with adaptive defenses. In this work, we consider the naturalness of the UATs and develop LinkPrompt, an adversarial attack algorithm to generate UATs by a gradient-based beam search algorithm that not only effectively attacks the target PLMs and PFMs but also maintains the naturalness among the trigger tokens. Extensive results demonstrate the effectiveness of LinkPrompt, as well as the transferability of UATs generated by LinkPrompt to open-sourced Large Language Model (LLM) Llama2 and API-accessed LLM GPT-3.5-turbo. The resource is available at https://github.com/SavannahXu79/LinkPrompt.