arXiv Machine Learning July 13, 2026 By Xiang Cheng, Sangdon Park, HyungSeok Han, Xiaokuan Zhang, Taesoo Kim

Ruby: Unmasking Unsafe Rust in Stripped Binaries via Machine Learning

Read the original on arXiv Machine Learning →

arXiv:2211. 00111v3 Announce Type: replace-cross Abstract: Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation.

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 Machine Learning.

arXiv AI

Jul 7

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

arXiv:2607. 04729v1 Announce Type: cross Abstract: LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace.

By Tarek Elsayed, Shiping Yang, Eunsong Koh, Sanika Goyal, Vincent Huang, Paul Ngo, Nathan Young, Mohammad Omidvar Tehrani, Alvyn Kang, Arnell Kang, Zeyu Chen, Ang\'elica Moreira, Xuan Feng, Angel X. Chang, Nick Sumner, Steven Y. Ko

llms agents benchmarks safety

arXiv AI

Sep 7

When LLM Decompilers Recompile More and Preserve Less

The paper examines how large‑language‑model (LLM) decompilers, which produce clean, idiomatic C code, are currently evaluated mainly on recompilability and passing shipped tests. It shows that these metrics can mask significant behavioral differences: a decompiled function may recompile and pass all tests yet diverge on other inputs or lose disclosed vulnerabilities. To address this, the authors propose Decompile‑Diverge, a behavioral oracle that synthesizes drivers, fuzzes inputs, and compares the decompiled code’s behavior to the original, revealing divergences in up to 13% of cases and exposing gaps in current evaluation suites.

By Chang Liu, Edward Raff, Kristopher Micinski

llms

arXiv Machine Learning

Jul 17

Agentic Vulnerability Reasoning on COTS Binaries

arXiv:2605. 05000v2 Announce Type: replace-cross Abstract: LLM agents have been increasingly adopted for solving security tasks.

By Hwiwon Lee, Jongseong Kim, Lingming Zhang

llms agents benchmarks

arXiv Machine Learning

Jul 21

SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?

arXiv:2605. 26548v2 Announce Type: replace-cross Abstract: Finding a real vulnerability in complicated systems is a challenging, long-horizon task that demands reasoning across an entire codebase to produce a working proof-of-concept (PoC).

By Hwiwon Lee, Jiawei Liu, Dongjun Kim, Wubing Xia, Ziqi Zhang, Chunqiu Steven Xia, Lingming Zhang

llms agents benchmarks safety

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 Machine Learning

Jul 1

An Empirical Study of Security Calibration in Large Language Models for Code

arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?

By Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos

llms benchmarks

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Related stories

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

When LLM Decompilers Recompile More and Preserve Less

Agentic Vulnerability Reasoning on COTS Binaries

SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?

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

An Empirical Study of Security Calibration in Large Language Models for Code