Speculative decoding has emerged as a promising paradigm for accelerating large language model inference by leveraging a lightweight draft model to generate multiple candidate tokens.However, existing methods often incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding.To address this challenge, we propose EDSD, an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design.EDSD drives the draft model to progressively align with the target model in an easy-to-hard manner while establishing tokenlevel alignment as a dominant design principle.Extensive experiments on seven LLMs demonstrate that EDSD improves training efficiency by 24.8%, increases the average acceptance length by 4.0%, and achieves a 4.1% speedup compared to state-of-the-art methods.Furthermore, EDSD improves robustness to system prompt variations by more than 5×.Our findings establish entropy-driven alignment as an effective and principled foundation for efficient speculative decoding.We make our draft model weights available at https://github.com/KerwinKai/EDSD.

Taint analysis, i.e., labeling data and propagating the labels through data flows, has been widely used for analyzing program information flows and ensuring system/data security. Due to its important applications, various taint analysis techniques have been proposed, including static and dynamic taint analysis. However, existing taint analysis techniques can be hardly applied to the rising microservice systems for industrial applications. To address such a problem, in this paper, we proposed the first practical non-intrusive dynamic taint analysis technique MirrorTaint for extensively supporting microservice systems on JVMs. In particular, by instrumenting the microservice systems, MirrorTaint constructs a set of data structures with their respective policies for labeling/propagating taints in its mirrored space. Such data structures are essentially non-intrusive, i.e., modifying no program meta-data or runtime system. Then, during program execution, MirrorTaint replicates the stack-based JVM instruction execution in its mirrored space on-the-fly for dynamic taint tracking. We have evaluated MirrorTaint against state-of-the-art dynamic and static taint analysis systems on various popular open-source microservice systems. The results demonstrate that MirrorTaint can achieve better compatibility, quite close precision and higher recall (97.9%/100.0%) than state-of-the-art Phosphor (100.0%/9.9%) and FlowDroid (100%/28.2%). Also, MirrorTaint incurs lower runtime overhead than Phosphor (although both are dynamic techniques). Moreover, we have performed a case study in Ant Group, a global billion-user FinTech company, to compare MirrorTaint and their mature developer-experience-based data checking system for automatically generated fund documents. The result shows that the developer experience can be incomplete, causing the data checking system to only cover 84.0% total data relations, while MirrorTaint can automatically find 99.0% relations with 100.0% precision. Lastly, we also applied MirrorTaint to successfully detect a recently wide-spread Log4j2 security vulnerability.

To address software engineering tasks such as se-curity risk assessment, software change government, and access control in database applications, taint analysis approaches for SQL statements have been commonly adopted for tracking information flows in these applications. However, existing taint analysis approaches cannot track implicit flows (i.e., control dependencies between sources and sinks) for SQL statements, facing the challenges of native/unmanaged code and database management system (DBMS) complexity. To address these chal-lenges, in this paper, we propose TaintSQL, a cell-level dynamic taint analysis (DTA) framework (maintaining a taint tag for each table cell) to track fine-grained implicit flows for SQL statements. Our TaintSQL framework includes two novel techniques, namely MutaIF and MockIF. MutaIF aims to track implicit flows with causal relationships, whereas MockIF aims to dynamically track implicit flows at runtime. We implement the two techniques of TaintSQL and evaluate them on a set of test subjects to assess their effectiveness and efficiency. The evaluation results show that both techniques effectively track fine-grained implicit flows for SQL statements with reasonable runtime overhead. The F1 scores of MutaIF and MockIF are 96.2% and 97.9%, respectively. We also conduct an industrial study of MutaIF in an international IT company (which serves over 1 billion global users and 80 million merchants). The positive feedback from the software engineers also demonstrates the practicability of the TaintSQL framework and the MutaIF technique in industrial settings.