arXiv AI

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

arXiv Machine Learning

Aug 4

Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context

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.

By Samuel Valenzuela, Johannes Kinder

rag benchmarks

arXiv AI

6d ago

Instruction Alignment for Binary Code Representation Learning

arXiv:2608. 11766v1 Announce Type: cross Abstract: Binary code representation learning is a fundamental problem in software security and reverse engineering.

By Huaijin Wang, Shuai Wang

rag safety

arXiv AI

Jul 3

XSearch: Explainable Code Search via Concept-to-Code Alignment

arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.

By Yiming Liu, Ruofan Liu, Yun Lin, Zicong Zhang, Weiyu Kong, Pengnian Qi, Xiao Cheng, Weinan Zhang, Qianxiang Wang, Linpeng Huang

rag benchmarks safety

arXiv Machine Learning

Aug 11

LLM-Based Embeddings for Program Analysis and Optimization

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.

By Calvin Higgins, Marco Alvarez

llms rag benchmarks

arXiv AI

6d ago

Program Semantic Inequivalence Game with Large Language Models

arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.

By Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani

llms agents fine-tuning benchmarks

arXiv AI

Jun 3

Large Byte Model: Teaching Language Models About Compiled Code

arXiv:2606. 02834v1 Announce Type: cross Abstract: Malware analysis starts with the raw bytes of an executable program, and tools to "lift" these to higher-level representations, such as assembly, are expensive and subject to error.

By Florian St\"ortz, Catalin-Andrei Stan, Alexandru Dinu, Sandra Servia-Rodr\'iguez, Mihaela Gaman, Calin Miron, Edward Raff

llms nlp

arXiv AI

Jun 16

SPARK: Security Knowledge Priming and Representation-Guided Knowledge Activation for LLM-based Secure Code Generation

arXiv:2606. 16244v1 Announce Type: cross Abstract: Large language models routinely generate code with exploitable security flaws.

By Xiaoyun Xu, Lichao Wu, Jona te Lintelo, Siyu Zhang, Stjepan Picek

llms rag fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv AI

Jun 8

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

arXiv:2506. 11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging.

By Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray

benchmarks

arXiv AI

Aug 5

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

arXiv:2601. 19138v2 Announce Type: replace-cross Abstract: Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, delaying feedback and increasing remediation costs.

By Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam, Hong Yi Lin, Minwoo Jeong, Ming Wu

llms agents

arXiv AI

Jul 13

Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning

arXiv:2607. 09452v1 Announce Type: cross Abstract: We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning.

By Charles Edward Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms

arXiv Machine Learning

Jul 10

Functional and Secure Code Generation with Task Vectors

arXiv:2607. 07881v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities.

By Felix Wang, Anudeep Das, Mei Nagappan, N. Asokan

llms benchmarks safety

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