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cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Agentic Vulnerability Reasoning on Windows COM Binaries
Hwiwon Lee, · 2026-05-07 · via cs.LG updates on arXiv.org

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Abstract:Windows Component Object Model (COM) services run with elevated privileges and are widely accessible to authenticated users, making race conditions in these binaries a critical surface for local privilege escalation. We present SLYP, an end-to-end agentic pipeline that discovers race condition vulnerabilities in COM binaries and generates debugger-verified proof-of-concept (PoC) code. SLYP exposes binary exploration, COM inspection, and dynamic debugging as reusable tool interfaces, giving agents the static context, COM activation metadata, and debugger feedback needed to move from vulnerability discovery to verified PoC generation. On a benchmark of 20 COM objects covering 40 vulnerability cases, SLYP achieves 0.973 F1, outperforming production coding agents by up to 0.208 F1 and the state-of-the-art static analyzer by 3.3x in bug discovery. For PoC generation, production coding agents in their default setup (without our COM inspection and dynamic debugging tools) verify essentially no cases on either frontier model, whereas SLYP's interactive toolsets enable it to autonomously synthesize working PoCs for 67.5% of cases on the strongest configuration. Deployed on production Windows services, SLYP discovers 28 previously unknown vulnerabilities across nine COM services, all confirmed by the Microsoft Security Response Center (MSRC) with 16 CVEs assigned and $140,000 in bounties. Furthermore, SLYP is designed with generalizable binary analysis and debugging interfaces, making it readily applicable to other commercial off-the-shelf (COTS) binaries beyond Windows COM services.
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as: arXiv:2605.05000 [cs.CR]
  (or arXiv:2605.05000v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2605.05000

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hwiwon Lee [view email]
[v1] Wed, 6 May 2026 15:00:52 UTC (175 KB)