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cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Automatically Detecting Checked-In Secrets in Android App...
Kevin Li, Lin Ling, Jinqiu Yang, Lili Wei · 2024-12-15 · via cs.SE updates on arXiv.org

Mobile apps are predominantly integrated with cloud services to benefit from enhanced functionalities. Adopting authentication using secrets such as API keys is crucial to ensure secure mobile-cloud interactions. However, developers often overlook the proper storage of such secrets, opting to put them directly into their projects. These secrets are checked into the projects and can be easily extracted and exploited by malicious adversaries. While many researchers investigated the issue of checked-in secret in open-source projects, there is a notable research gap concerning checked-in secrets in Android apps deployed on platforms such as Google Play Store. Unlike open-source projects, the lack of direct access to the source code and the presence of obfuscation complicates the checked-in secret detection for Android apps. This motivates us to conduct an empirical analysis to measure and compare the performance of different checked-in secret detection tools on Android apps. We first conducted a literature review to find all the checked-in secret detection tools that can be applied to Android apps. Then, we evaluate three representative tools on 5,135 Android apps, comparing their performance and analyzing their limitations. Our experiment reveals 2,142 checked-in secrets affecting 2,115 Android apps. We also disclose that the current checked-in secret detection techniques suffer from key limitations. All of the evaluated tools can miss a significant number of checked-in secrets in Android apps. Nevertheless, we observed that the tools are complimentary, suggesting the possibility of developing a more effective checked-in secret detection tool by combining their insights. Additionally, we propose that analyzing string groups within methods containing checked-in secrets may provide a more effective strategy to overcome obfuscation challenges.