Empirical study of 2,214 MCP servers finds 9.93% of 19,200 description-code pairs inconsistent via a new static-analysis-plus-LLM-prompting framework, with security implications.
LLMs in software security: A survey of vulnerability detection techniques and insights
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
Systematic review of thirteen malicious-code prompt corpora for coding LLM refusal evaluation that catalogs construction methods, surfaces gaps in human baselines, cross-corpus comparability, and malware taxonomies, and proposes methodological improvements.
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.
citing papers explorer
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Description-Code Inconsistency in Real-world MCP Servers: Measurement, Detection, and Security Implications
Empirical study of 2,214 MCP servers finds 9.93% of 19,200 description-code pairs inconsistent via a new static-analysis-plus-LLM-prompting framework, with security implications.
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Refusal Evaluation in Coding LLMs and Code Agents: A Systematic Review of Thirteen Malicious-Code Prompt Corpora (2023-2025)
Systematic review of thirteen malicious-code prompt corpora for coding LLM refusal evaluation that catalogs construction methods, surfaces gaps in human baselines, cross-corpus comparability, and malware taxonomies, and proposes methodological improvements.
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Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
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GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.