Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-Based Vulnerability Reasoning

Large Language Models LLMs are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations to triage bugs and engineer patches. Standard practice relies on Chain-of-Thought CoT prompting, but free-form reasoning...

Structured but Fragile: On the Limits of LLMs in Cybersecurity Decision-Making

Large language models LLMs are increasingly used in cybersecurity workflows, yet it remains unclear whether they can perform structured security reasoning or merely rely on superficial cues and prior knowledge. We study this question in the context of defence selection over attack graphs derived...

sec-recon-agent

sec-recon-agent Type-safe security triage built on Pydantic A...

Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection

Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches still operate at the function level, where models are asked to predict whether a single function is vulnerable without...

ARMOR: Aligning Secure and Safe Large Language Models Via Meticulous Reasoning

Large Language Models LLMs have demonstrated remarkable generative capabilities. However, their susceptibility to misuse has raised significant safety concerns. While post-training safety alignment methods have been widely adopted, LLMs remain vulnerable to malicious instructions that can bypass...