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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
The Linux IOCTL Census: A Source-Derived Database of the ...
Michael J. Bommarito · 2026-06-09 · via cs.SE updates on arXiv.org

The ioctl system call is Linux's catch-all device-control interface. A userspace program opens a device node and hands the driver a numeric command code and an argument buffer, and the driver does whatever that code means, whether configuring hardware, reading back state, or moving data into and out of the kernel. Drivers define these commands themselves, by the thousand, and parse their arguments in kernel context, which makes ioctl handlers one of the broadest and least uniform local attack surfaces in the kernel. A handler that trusts an argument length it never validates can read or write kernel memory out of bounds, and the command space is catalogued in no central place. We present the Linux IOCTL Census, a source-derived and queryable inventory of that surface. An allmodconfig build compiles 878 modules across 169 subtrees, and over them a single deterministic libclang pass over the kernel source recovers 586 ioctl dispatch entry points, 1,289 decoded _IOC command codes, 3,583 controlled-input sinks, and 1,298 permission gates. A second pass encodes the kernel's own documented threat model as a queryable column, separating the capability-ungated ioctl surface, an upper bound on unprivileged reach rather than proven reach, from the part a hard capability gate puts out of scope. We backtest the census against 22 recent in-tree ioctl CVEs and release the structural tier as open data, on a schema shared with the companion Windows IOCTL Census so a single query spans both operating systems.