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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
ConsiDroid: A Concolic-based Tool for Detecting SQL Injec...
Ehsan Edalat, Babak Sadeghiyan, Fatemeh Ghassemi · 2018-11-26 · via cs.SE updates on arXiv.org

In this paper, we present a concolic execution technique for detecting SQL injection vulnerabilities in Android apps, with a new tool we called ConsiDroid. We extend the source code of apps with mocking technique, such that the execution of original source code is not affected. The extended source code can be treated as Java applications and may be executed by SPF with concolic execution. We automatically produce a DummyMain class out of static analysis such that the essential functions are called sequentially and, the events leading to vulnerable functions are triggered. We extend SPF with taint analysis in ConsiDroid. For making taint analysis possible, we introduce a new technique of symbolic mock classes in order to ease the propagation of tainted values in the code. An SQL injection vulnerability is detected through receiving a tainted value by a vulnerable function. Besides, ConsiDroid takes advantage of static analysis to adjust SPF in order to inspect only suspicious paths. To illustrate the applicability of ConsiDroid, we have inspected randomly selected 140 apps from F-Droid repository. From these apps, we found three apps vulnerable to SQL injection. To verify their vulnerability, we analyzed the apps manually based on ConsiDroid's reports by using Robolectric.