Antea Group (France)

As mobile applications become increasingly complex and privacy regulations continue to evolve, the task of accurately identifying app violations in compliance detection has become a major challenge. Prior works mainly relied on taint analysis and dynamic monitoring to address this issue. However, taint analysis requires specifying sources and sinks for each violation, leading to multiple analysis rounds and inefficiency. Meanwhile, dynamic monitoring suffers from incomplete coverage, resulting in high false negatives. In this paper, we propose a novel graph structure, called Behavior Property Graph (BPG), for detecting non-compliant behaviors in Android applications. BPG integrates the features from various graph representations, including Abstract Syntax Tree (AST), Control Flow Graph (CFG), Call Graph (CG), Program Dependency Graph (PDG), and Pointer Assignment Graph (PAG), enabling comprehensive modeling of complex app behaviors. Violations are identified by querying the BPG using behavioral patterns extracted from real-world apps. We develop a prototype system called BPGᴇɴ to generate BPGs and evaluated its performance by testing seven types of non-compliant behaviors on a dataset of 200 real-world apps. Notably, BPGᴇɴ detects 14 violations within 13 previously unreported non-compliant applications. The results show that BPGᴇɴ can efficiently and effectively detect app compliance violations.

Binary analysis plays a critical role in software comprehension and security analysis, especially when source code is unavailable or difficult to analyze. Lifting binaries to LLVM IR enables reuse of the rich LLVM toolchain for downstream binary analyses. However, existing binary lifters often fail to produce syntactically valid LLVM IR or to restore sufficient semantics, making downstream analyses unreliable or unfeasible. This paper introduces BIT, a novel binary lifter designed to ensure syntactic compliance as well as semantic adequacy BIT achieves this through a multistage approach that includes anchor variable identification, analysis context collection, and IR refinement. In the evaluation, BIT achieved excellent results across multiple downstream analyses when compared with various lifters In static analysis, the F1 score of bug detection is 0.85, which is better than Plankton’s 0.81; in symbolic execution, it outperforms McSema by 3,049× in path exploration and by 1.36× in test case generation, respectively; in reanalysis, BIT can complete all tasks and is consistent with the advanced work McSema. These results highlight BIT’s ability to bridge the gap between binary-level analysis and the LLVM toolchain.

The swift evolution of Large Language Models (LLMs) like the GPT family, LLaMA, ChatGLM, and Qwen represents significant progress in artificial intelligence research. Despite their remarkable capabilities in generating content, these models encounter substantial challenges when producing structured outputs and engaging in dynamic interactions, particularly when they need to retrieve external information in real time. To address these limitations, researchers have developed the ”Function Calling” paradigm. This approach enables language models to analyze user inquiries and engage with defined functions, thereby facilitating precise responses through connections to external sources, including databases, programming interfaces, and live data streams. This functionality has been successfully implemented across numerous sectors such as finance analytics, healthcare systems, and service operations. The implementation of function calling comprises three essential phases: preparation, execution, and processing. The preparation phase encompasses query analysis and function identification. During execution, the system evaluates whether a function is necessary, extracts relevant parameters, and oversees the operation. The processing phase concentrates on analyzing outcomes and crafting appropriate responses. Each phase presents unique difficulties, ranging from accurately selecting functions to managing complex parameter extraction and ensuring reliable execution. Researchers have established various evaluation frameworks and metrics to assess function calling performance, including success rates, computational efficiency, parameter extraction accuracy, and response quality indicators such as ROUGE-L evaluation scores. This survey systematically reviews the current landscape of function calling in LLMs, analyzing technical challenges, examining existing solutions, and discussing evaluation methodologies. We particularly focus on practical implementations and industrial applications, providing insights into both current achievements and future directions in this rapidly evolving field. For a comprehensive collection of related research papers and the Appendix file, please refer to our repository at GitHub.

Informatika je u hrvatskom obrazovnom sustavu prisutna od 1970-ih godina, i to kao redovni ili izborni nastavni predmet, kao nastavni sadržaj u nekom drugom nastavnom predmetu ili kao neki drugi oblik informatičke aktivnosti u osnovnim školama. U ovom radu analiziran je njezin status, nastavni programski sadržaji i potrebne stručne kvalifikacije za radno mjesto učitelja informatike u razdoblju od početka 1970-ih godina do 2020. godine. Provedena je kvalitativna analiza primarnih izvora, odnosno svih temeljnih obrazovnih dokumenata, nastavnih planova i programa za osnovne škole i nastavnog kurikula Informatike te pravilnika o stručnoj spremi učitelja informatike. Ovaj komparativan rad propituje jesu li u promatranom razdoblju prosvjetne vlasti na vrijeme prepoznale važnost pripremanja novih generacija učenika za život u digitalnom dobu. Ujedno se opisuje povijesni kontekst uvođenja računala u hrvatske osnovne škole te etimološko značenje pojma informatika.

Democracy is crucial to a cryptocurrency ecosystem, as the diversity of miners (farms, personal computers, web clients, or even cloud functions) underlays the credibility of the cryptocurrency. Among miners, web clients used to be the vast majority, e.g., 50M+ as of March 2018. As time went on, however, cryptomining was gradually monopolized by mining farms with dedicated hardware (e.g., ASICs), and web clients scaled down to ∼0.1M. To suppress mining farms, certain cryptocurrencies (like Monero) adopted new mining algorithms such as RandomX whose execution relies on general-purpose hardware architectures. Unfortunately, this further impairs web-based cryptomining as web clients cannot provide the desired architecture support to these algorithms. This paper explores how to revive software democracy of efficient web-based crypto-mining, using a novel program transformation technique termed Vectra. Vectra employs just-in-time (JIT) transformations of mining programs for web architectures; it effectively identifies and merges isomorphic instructions upon execution. Vectra ensures correct transformations based on symbolic constraints of the instructions. Real-world deployments show that Vectra reduces WASM instructions by about 7× and achieves a 3× –16× speedup for web cryptomining in diverse execution environments like PCs, mobile phones, and serverless platforms, which translates to a high (69%–274%) return-on-investment (ROI) for common users.

Split learning is a prominent framework for vertical federated learning, where multiple clients collaborate with a central server for model training by exchanging intermediate embeddings. Recently, it is shown that an adversarial server can exploit the intermediate embeddings to train surrogate models to replace the bottom models on the clients (i.e., model stealing). The surrogate models can also be used to reconstruct private training data of the clients (i.e., data stealing). To defend against these stealing attacks, we propose Model Rake (i.e., Rake), which runs two bottom models on each client and differentiates their output spaces to make the two models distinct. Rake hinders the stealing attacks because it is difficult for a surrogate model to approximate two distinct bottom models. We prove that, under some assumptions, the surrogate model converges to the average of the two bottom models and thus will be inaccurate. Extensive experiments show that Rake is much more effective than existing methods in defending against both model and data stealing attacks, and the accuracy of normal model training is not affected.