arXiv Machine Learning

Ruby: Unmasking Unsafe Rust in Stripped Binaries via 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.

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 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

arXiv AI

Jun 8

Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software

arXiv:2602. 04894v4 Announce Type: replace-cross Abstract: LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities.

By Tomer Kordonsky, Amit LeVi, Maayan Yamin, Noam Benzimra, Avi Mendelson

llms benchmarks

arXiv AI

Jul 16

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.

By Niels M\"undler-Sasahara, Hristo Venev, Dawn Song, Martin Vechev, Jingxuan He

llms

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 24

AI Security Policy Should Assess Systems, Not Only Models

arXiv:2605. 09504v2 Announce Type: replace-cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, parallel exploration, and evolutionary optimization.

By Michael A. Riegler, Inga Str\"umke

llms agents safety

arXiv Machine Learning

Aug 11

Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js

arXiv:2510. 20739v2 Announce Type: replace-cross Abstract: Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical challenge: how can security analysts prioritize the reports most likely to be true vulnerabilities?

By Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu, Nuno Sabino, Limin Jia, Ruben Martins, Darion Cassel, Kevin Cheang

llms benchmarks

arXiv AI

Aug 6

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512. 02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or language translation.

By Martin Weiss, Jesko Hecking-Harbusch, Jochen Quante, Matthias Woehrle

llms safety

arXiv AI

2d ago

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608. 13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems.

By Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, Yanzhi Wang

llms agents fine-tuning safety

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