Code LLMs exhibit remarkable capabilities in automated code generation, but the generated code often suffers from low computational efficiency. Existing solutions have two limitations: (1) direct fine-tuning lacks transferability across model scales; (2) The efficiency optimization method for iterative generation-evaluation loops incurs high overhead. We propose CoEffi, which adopts inference-side intervention that directly operates on the decoding process without modifying base model parameters, enabling real-time efficiency improvement across different model scales while avoiding iterative reevaluation. CoEffi uses a small efficiency expert model for proposal and the base model to evaluate its acceptability, additionally introducing a region-aware dynamic acceptance threshold mechanism that maintains a sliding window to record historical acceptance rates and adjusts the dynamic threshold accordingly. We extensively evaluate CoEffi on four mainstream code LLM families and three efficiency benchmarks. Compared with vanilla instruction-tuned models, CoEffi improves average Beyond@1 by 9.12%–38.62% across the evaluated model families. It also improves Pass@1 on Mercury and ENAMEL, while EffiBench exhibits model-dependent correctness gains and regressions. A family specific trained expert can be reused across compatible base model scales within the same model family.

Large Language Models (LLMs) demonstrate strong generalization capabilities but remain vulnerable to jailbreak attacks that induce restricted text or malicious code generation.Recent structured jailbreaks embed adversarial intent into code-like templates and have demonstrated promising effectiveness.However, existing approaches typically operate within a fixed template design and a single programming language, without considering language diversity or adaptive template evolution, thereby limiting the exploration of cross-language jailbreak behaviors.In this paper, we present MultiCodeAttack, a structured jailbreak framework that systematically explores and optimizes multi-language code templates.MultiCodeAttack maintains a diverse template library across programming languages, dynamically selects languages with higher attack effectiveness via a multi-armed bandit strategy, and evolves templates through semantic-preserving mutation guided by response-aware signals.Extensive experiments on 8 LLMs show that MultiCodeAttack outperforms existing jailbreak baselines, achieving 28.23%–832.59% higher harmful text generation.On malicious code generation across 11 LLMs, MultiCodeAttack produces up to 136.22% more malicious outputs than the baseline methods.Our code is available at https://anonymous.4open.science/r/MultiCodeAttack/.