Hugging Face Trending Papers

Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study

Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language Models (LLMs) assisted tools show potential in vulnerability detection, location, and repair tasks.

Hugging Face Trending Papers

1d ago

Benchmarking Automated Security Patch Backporting: How Far Are We?

Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets.

llms agents benchmarks

arXiv AI

Jul 28

Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios

arXiv:2607. 23088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored.

By Lixun Ma, Ruolong Ma, Bei Wang, Feng Wei, Zhenguang Liu, Lorenzo Cavallaro, Wentao Chen

llms benchmarks

arXiv AI

Jun 8

RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs

arXiv:2604. 17948v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various cybersecurity tasks, including vulnerability classification, detection, and patching.

By Parteek Jamwal, Minghao Shao, Boyuan Chen, Achyuta Muthuvelan, Asini Subanya, Boubacar Ballo, Kashish Satija, Mariam Shafey, Mohamed Mahmoud, Moncif Dahaji Bouffi, Pasindu Wickramasinghe, Siyona Goel, Yaakulya Sabbani, Hakim Hacid, Mthandazo Ndhlovu, Eleanna Kafeza, Sanjay Rawat, Muhammad Shafique

llms rag agents safety

arXiv AI

Jun 30

Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection

arXiv:2606. 30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection.

By Asif Shahriar, Hongyu Cai, Hadjer Benkraouda, Gang Wang, Z. Berkay Celik

llms safety

arXiv AI

Jul 3

ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair

arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.

By Simiao Liu, Fang Liu, Peiding Wang, Taichuan Li, Yinghao Zhu, Xiaoli Lian, Li Zhang

llms agents benchmarks

arXiv AI

Jun 4

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models

arXiv:2606. 04739v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation (RAG) settings.

By Sabrina Kaniewski, Fabian Schmidt, Tobias Heer

llms rag

arXiv AI

23h ago

Benchmarking Automated Security Patch Backporting: How Far Are We?

arXiv:2608. 17671v1 Announce Type: cross Abstract: Automated security patch backporting is critical for mitigating N-day vulnerabilities.

By Jincheng Yang, Yulong Fu, Chengwei Liu, Lyuye Zhang, Fangyuan Zhang, Bingyang Ren, Yang Liu, Hui Li

llms agents benchmarks

arXiv AI

Jul 8

Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis

arXiv:2607. 05842v1 Announce Type: cross Abstract: Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse.

By Mingchen Li, Meikang Qiu, Zifan Peng, Heng Fan, Song Fu, Junhua Ding, Yunhe Feng

llms safety

arXiv AI

Jul 2

SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests

arXiv:2607. 00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories.

By Yaoqi Guo, Yang Liu, Jie M. Zhang, Yun Ma, Yiling Lou, Zhenpeng Chen

llms agents benchmarks

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 16

Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice?

arXiv:2606. 15762v1 Announce Type: cross Abstract: We ran 300 repeated vulnerability-finding scans to measure how repeatable agentic large language model (LLM) security review is on the same JavaScript code, prompt, and benchmark harness.

By Liran Tal, Johannes Kloos, Arsenii Rudich, Stephen Thoemmes, Manoj Nair

llms agents benchmarks

arXiv AI

Aug 3

RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment

arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.

By Marcos Fuster-Pena, David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms reinforcement-learning benchmarks

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