arXiv AI

Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review

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

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

arXiv AI

Aug 18

Workspace Topology as an Attack Vector in Agentic Coding Assistants

arXiv:2608. 14876v1 Announce Type: cross Abstract: Agentic coding assistants are finding widespread use, not just in new code development but in quickly ingesting and leveraging third-party code.

By Alexandre G. R. Day, Pradeep Yadlapalli, Sriram Venkatapathy, Thomas Paniagua, Nick Raines, Sahil Wadhwa, Himanshu Kumar, Andy Luo, Sudeep Panyam, Rikhiya Ghosh, Pranab Mohanty, Giri Iyengar

agents multimodal safety

arXiv Machine Learning

Sep 3

CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation

The paper introduces CodePoisonRAG, a framework that poisons retrieval-augmented code generation systems by transforming benign code artifacts into malicious ones. It injects CWE-specific vulnerabilities and false safety claims into a single task-matched artifact, achieving high success rates across multiple generators and even against a defense system. The study demonstrates that attackers can target and propagate specific weaknesses without altering the underlying language model.

By Varun Gadey, Ziad Marey, Alexandra Dmitrienko

llms rag safety

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 15

SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents

arXiv:2606. 13757v1 Announce Type: cross Abstract: Large language model (LLM) reviewers are increasingly used in pull-request (PR) workflows, where their approvals help decide which code is merged into a repository.

By Rui Melo, Riccardo Fogliato, Sean Zhou, Pratiksha Thaker, Zhiwei Steven Wu

llms agents benchmarks safety

arXiv Machine Learning

Sep 10

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing d...

By Jiahao Shi, Edward Tsien, Yifeng Di, Hongjiao Zhang, Yuan Tang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman, Ilan Pinto, Tianyi Zhang

llms agents benchmarks

arXiv Machine Learning

Aug 17

Detecting Contaminated Code-Generation Prompt Batches via Influence Functions

arXiv:2608. 14303v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for code generation, yet they remain vulnerable to prompts that elicit insecure implementations.

By Francesco Quinzan, Noor Munir, Yishun Lu, Stephen Roberts

llms benchmarks

arXiv AI

Jul 3

Safety Targeted Embedding Exploit via Refinement

arXiv:2607. 01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching.

By Joshua Adrian Cahyono

llms rag safety

arXiv AI

Aug 12

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

arXiv:2608. 10530v1 Announce Type: cross Abstract: Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory.

By Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari

llms agents safety

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

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

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