arXiv AI

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios

arXiv:2509. 22097v5 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern.

arXiv AI

Jun 18

Code-Augur: Agentic Vulnerability Detection via Specification Inference

arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.

By Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury

llms agents benchmarks

Hugging Face Trending Papers

Aug 12

VICBench: A Multi-Language Benchmark for Code Vulnerability Detection

Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases. VICs are essential for determining the full range of vulnerable software versions.

agents benchmarks

arXiv AI

6d ago

VICBench: A Multi-Language Benchmark for Code Vulnerability Detection

arXiv:2608. 12246v1 Announce Type: cross Abstract: Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases.

By Jin Lu, Xuening Han, Yang Zhong, Lin Tan, Kevin Luo, Andrew Gacek, Neha Rungta

agents benchmarks

arXiv Machine Learning

Jun 18

OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing

arXiv:2606. 19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs.

By Nahum Korda, Gadi Evron

llms safety

arXiv AI

Jun 16

DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents

arXiv:2511. 20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge.

By Rupam Patir, Keyan Guo, Suvadra Barua, Abhijeet Pathak, Dinesh Gudimetla, Jiawei Guo, Hongxin Hu, Haipeng Cai

llms agents fine-tuning efficiency 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

Jul 24

IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests

arXiv:2607. 20759v1 Announce Type: cross Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools.

By Ankur Singh, Jinqiu Yang, Tse-Hsun Chen

llms agents benchmarks safety

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 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 AI

Jun 17

Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks

arXiv:2510. 01359v2 Announce Type: replace-cross Abstract: Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings.

By Shoumik Saha, Jifan Chen, Sam Mayers, Sanjay Krishna Gouda, Zijian Wang, Varun Kumar

llms agents multimodal benchmarks safety

arXiv AI

Jul 28

Execution-Grounded Security Testing for Coding Agents in Software Engineering Pipelines

arXiv:2607. 22569v1 Announce Type: new Abstract: Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, and the underlying system.

By Yifei Ge, Weisong Sun, Jinkun Xiao, Yuchen Chen, Yebo Feng, Peizhuo Lv, Xia Feng, Chunrong Fang, Zhihong Zhao, Zhenyu Chen, Yang Liu

agents

arXiv AI

Aug 10

CyberForge: Verified Vulnerability Injection at Repository Level for Cybersecurity Agent Training

arXiv:2608. 06471v1 Announce Type: cross Abstract: Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world software.

By Amine Lbath, Manan Suri, Aurelien Delaitre, Vadim Okun, Massih-Reza Amini, Ram D. Sriram, Dinesh Manocha

llms agents fine-tuning

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