arXiv AI June 3, 2026 By Florian St\"ortz, Catalin-Andrei Stan, Alexandru Dinu, Sandra Servia-Rodr\'iguez, Mihaela Gaman, Calin Miron, Edward Raff

Large Byte Model: Teaching Language Models About Compiled Code

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 19

Multi-View Decompilation for LLM-Based Malware Classification

arXiv:2606. 20436v1 Announce Type: cross Abstract: Malware analysts often inspect compiled binaries through decompiled pseudo-C, when source code is unavailable.

By Bercan Turkmen, Vyas Raina

llms benchmarks

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

Jul 13

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.

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

llms rag agents benchmarks safety

arXiv AI

Jul 23

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

arXiv:2607. 20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours.

By Adel ElZemity, Shujun Li, Budi Arief

llms agents benchmarks safety

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

Jun 16

MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens

arXiv:2606. 16072v1 Announce Type: cross Abstract: Compared with binaries and decompiled code, malware source code more directly reflects the attackers' original intent.

By Bojing Li, Duo Zhong, Prajna Bhandary, Raguvir S, Charles Maxa, Robert J Joyce, Charles Nicholas

Related stories

Multi-View Decompilation for LLM-Based Malware Classification

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

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

Instruction Alignment for Binary Code Representation Learning

MASCOT-Android: A Curated Dataset and Automated Collection Pipeline for Android Malware Source Code Specimens