arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 02084v1 Announce Type: cross Abstract: Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification.
arXiv:2608. 11766v1 Announce Type: cross Abstract: Binary code representation learning is a fundamental problem in software security and reverse engineering.
The paper introduces LSem2Vec, a two‑stage method that first uses a large language model to extract source code semantics and then applies a sentence embedding model to produce vector representations. This approach removes the need for task‑specific training or fine‑tuning, addressing errors in LLM outputs. Experiments on three datasets across multiple programming languages show that LSem2Vec outperforms five state‑of‑the‑art unsupervised methods.
arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.
The paper surveys 87 influential studies on machine‑learning‑based automated vulnerability detection (ML4AVD), categorizing them by problem formulation, input and detection granularity, target languages, evaluation metrics, datasets, and detection approaches. It identifies twelve self‑reinforcing pain points—such as overreliance on binary classification of C/C++ function‑level vulnerabilities, limited language coverage, and intertwined datasets, baselines, and metrics—that trap the field in a narrow, artificial problem space. The authors propose concrete recommendations to break these feedback loops and evaluate a recent high‑profile effort, AIxCC, against these guidelines, reflecting on ML4AVD’s relevance amid the rise of agentic AI.
arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.