Juanru Li

Xiwei Wu, Yueyang Feng, Xiaoyang Lu et al.

As software systems increase in size and complexity dramatically, ensuring their correctness, security, and reliability becomes an increasingly formidable challenge. Despite significant advancements in verification techniques and tools, their practical application to complex, real-world systems is often hindered by critical gaps in both automation and expressiveness. To address these difficulties, this paper presents \textbf{Qualified C Programming Verifier (QCP)}, a novel verification tool that integrates annotation-based automatic verification with interactive proving using Rocq. QCP employs symbolic execution and a separation logic entailment solver to automatically discharge many verification obligations, while deferring more complex obligations to Rocq for manual proof. Furthermore, QCP includes a VS Code extension designed to enhance proof efficiency and support a deeper understanding of both the program behavior and verification outcomes.

Baicheng Chen, Yu Wang, Ziheng Zhou et al.

Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports critical tasks such as vulnerability discovery and malware analysis. Despite its importance, RE remains labor-intensive and requires substantial expertise, making large language models (LLMs) a potential solution for automating the process. However, their capabilities for RE remain systematically underexplored. To address this gap, we study the cryptographic binary RE capabilities of LLMs and introduce \textbf{CREBench}, a benchmark comprising 432 challenges built from 48 standard cryptographic algorithms, 3 insecure crypto key usage scenarios, and 3 difficulty levels. Each challenge follows a Capture-the-Flag (CTF) RE challenge, requiring the model to analyze the underlying cryptographic logic and recover the correct input. We design an evaluation framework comprising four sub-tasks, from algorithm identification to correct flag recovery. We evaluate eight frontier LLMs on CREBench. GPT-5.4, the best-performing model, achieves 64.03 out of 100 and recovers the flag in 59\% of challenges. We also establish a strong human expert baseline of 92.19 points, showing that humans maintain an advantage in cryptographic RE tasks. Our code and dataset are available at https://github.com/wangyu-ovo/CREBench.

Ruidong Han, Chao Yang, Siqi Ma et al.

In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a flexibility introduces vulnerabilities. One such vulnerability, referred to as range specification bugs, has been recently identified. The vulnerability originates from the fact that even though each individual parameter receives a value in the recommended value range, certain combinations of parameter values may affect the drone physical stability. In this paper we develop a novel learning-guided search system to find such combinations, that we refer to as incorrect configurations. Our system applies metaheuristic search algorithms mutating configurations to detect the configuration parameters that have values driving the drone to unstable physical states. To guide the mutations, our system leverages a machine learning predictor as the fitness evaluator. Finally, by utilizing multi-objective optimization, our system returns the feasible ranges based on the mutation search results. Because in our system the mutations are guided by a predictor, evaluating the parameter configurations does not require realistic/simulation executions. Therefore, our system supports a comprehensive and yet efficient detection of incorrect configurations. We have carried out an experimental evaluation of our system. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over 85% lead to actual unstable physical states.

Siqi Ma, Juanru Li, Hyoungshick Kim et al.

A fundamental premise of SMS One-Time Password (OTP) is that the used pseudo-random numbers (PRNs) are uniquely unpredictable for each login session. Hence, the process of generating PRNs is the most critical step in the OTP authentication. An improper implementation of the pseudo-random number generator (PRNG) will result in predictable or even static OTP values, making them vulnerable to potential attacks. In this paper, we present a vulnerability study against PRNGs implemented for Android apps. A key challenge is that PRNGs are typically implemented on the server-side, and thus the source code is not accessible. To resolve this issue, we build an analysis tool, \sysname, to assess implementations of the PRNGs in an automated manner without the source code requirement. Through reverse engineering, \sysname identifies the apps using SMS OTP and triggers each app's login functionality to retrieve OTP values. It further assesses the randomness of the OTP values to identify vulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded from \tool{Google Play} and \tool{Tencent Myapp}, \sysname identified 399 vulnerable apps that generate predictable OTP values. Even worse, 194 vulnerable apps use the OTP authentication alone without any additional security mechanisms, leading to insecure authentication against guessing attacks and replay attacks.

Yikun Hu, Yuanyuan Zhang, Juanru Li et al.

Binary code clone analysis is an important technique which has a wide range of applications in software engineering (e.g., plagiarism detection, bug detection). The main challenge of the topic lies in the semantics-equivalent code transformation (e.g., optimization, obfuscation) which would alter representations of binary code tremendously. Another chal- lenge is the trade-off between detection accuracy and coverage. Unfortunately, existing techniques still rely on semantics-less code features which are susceptible to the code transformation. Besides, they adopt merely either a static or a dynamic approach to detect binary code clones, which cannot achieve high accuracy and coverage simultaneously. In this paper, we propose a semantics-based hybrid approach to detect binary clone functions. We execute a template binary function with its test cases, and emulate the execution of every target function for clone comparison with the runtime information migrated from that template function. The semantic signatures are extracted during the execution of the template function and emulation of the target function. Lastly, a similarity score is calculated from their signatures to measure their likeness. We implement the approach in a prototype system designated as BinMatch which analyzes IA-32 binary code on the Linux platform. We evaluate BinMatch with eight real-world projects compiled with different compilation configurations and commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison. The experimental results show that BinMatch is robust to the semantics-equivalent code transformation. Besides, it not only covers all target functions for clone analysis, but also improves the detection accuracy comparing to the state-of-the-art solutions.