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VulnAgent-R2: Evidence-Calibrated Multi-Agent Auditing for Repository-Level Vulnerability Detection

Announce Type: replace Abstract: Software vulnerabilities often depend on cross-file data flow, build options, framework conventions, and runtime guards, so isolated function classifiers produce fragile and poorly calibrated warnings. Repository-level LLM agents can gather richer evidence, but prior variants under-specify reproducibility, verifier behavior, baseline fairness, and statistical uncertainty. We present VulnAgent-R2, a budget-aware agentic auditing framework with three additional...

arXiv CS 7d ago

VulnAgent-R2: Evidence-Calibrated Multi-Agent Auditing for Repository-Level Vulnerability Detection

Announce Type: replace Abstract: Software vulnerabilities often depend on cross-file data flow, build options, framework conventions, and runtime guards, so isolated function classifiers produce fragile and poorly calibrated warnings. Repository-level LLM agents can gather richer evidence, but prior variants under-specify reproducibility, verifier behavior, baseline fairness, and statistical uncertainty. We present VulnAgent-R2, a budget-aware agentic auditing framework with three additional...

arXiv CS 6d ago

FusionVul: A Multimodal Feature Fusion Framework for Source Code Vulnerability Detection

arXiv:2606.08553v1 Announce Type: new Abstract: Source code vulnerability detection remains a long-standing challenge due to the increasing scale, structural complexity, and semantic diversity of modern codebases. Conventional static-analysis or rule-based approaches often fail to capture subtle execution dependencies, while single-modality learning models tend to overlook critical structural information embedded beyond the lexical surface of source code. To improve robustness across...

arXiv CS 1d ago