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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
Guardian: symbolic validation of orderliness in SGX enclaves
Pedro Antonino, Wojciech Aleksander Wołoszyn, A. W. Roscoe · 2021-05-13 · via cs.SE updates on arXiv.org

Modern processors can offer hardware primitives that allow a process to run in isolation. These primitives implement a trusted execution environment (TEE) in which a program can run such that the integrity and confidentiality of its execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example of such primitives and its isolated processes are called \emph{enclaves}. These guarantees, however, can be easily thwarted if the enclave has not been properly designed. Its interface with the untrusted software stack is arguably the largest attack surface that adversaries can exploit; unintended interactions with untrusted code can expose the enclave to memory corruption attacks, for instance. In this paper, we propose a notion of an \emph{orderly} enclave which splits its behaviour into several execution phases each of which imposes a set of restrictions on accesses to untrusted memory, phase transitions and registers sanitisation. A violation to these restrictions indicates an undesired behaviour which could be harnessed to perpetrate attacks against the enclave. We also introduce \Analyser{}: a tool that uses symbolic execution to carry out the validation of an enclave against our notion of an orderly enclave; in this process, it also looks for some typical memory-corruption vulnerabilities. We discuss how our approach can prevent and flag enclave vulnerabilities that have been identified in the literature. Moreover, we have evaluated how our approach fares in the analysis of some practical enclaves. \Analyser{} was able to identify real vulnerabilities on these enclaves which have been acknowledged and fixed by their maintainers.