llm-search

mcphub-com/llm-search

4.9

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AI Evaluation Report

  • Test case 1
    Score: 9/10
    User submitted
    help me get some report about llm assisted vulnerability detections

    Here are some recent reports and papers focused on LLM-assisted vulnerability detection:

    1. IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities

    • Authors: Ziyang Li, Saikat Dutta, Mayur Naik
    • Published: May 27, 2024
    • Summary: This paper introduces IRIS, a neuro-symbolic approach combining large language models (LLMs) with static analysis for vulnerability detection. It leverages LLMs to infer taint specifications, which reduces reliance on human specifications. The evaluation shows that IRIS can detect 55 vulnerabilities compared to 27 by a leading tool, CodeQL, and it identifies previously unknown vulnerabilities.
    • Link: Read the Paper

    2. Towards LLM-Assisted Vulnerability Detection and Repair for Open-Source 5G UE Implementations

    • Authors: Rupam Patir et al.
    • Summary: This work explores LLM-assisted techniques for vulnerability detection and repair specifically in open-source 5G implementations. It introduces a framework utilizing Chain-of-Thought prompting for detecting vulnerabilities based on 5G properties and guiding repairs according to secure coding practices.
    • Link: Read More

    3. IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities (ICLR 2025)

    • Summary: This version presents findings related to the effectiveness of IRIS in detecting vulnerabilities across real-world software projects. The paper emphasizes improvements in detection rates and reduced false positives by integrating LLMs with static analysis.
    • Link: Access the Paper

    4. LLM-Assisted Static Analysis for Detecting Security Vulnerabilities - ICLR 2025 Poster

    • Summary: This paper outlines the effectiveness of using LLMs in static analysis, highlighting the benefits of using IRIS for detecting security vulnerabilities in complex codebases.
    • Link: View Poster

    Key Insights:

    • Effectiveness: The use of LLMs significantly enhances the ability to detect vulnerabilities compared to traditional static analysis tools.
    • Reduction in False Positives: The integration of contextual analysis with LLMs helps in minimizing false alarms, which is a common issue in static analysis.
    • Performance Comparison: The papers consistently show that approaches using LLMs outperform conventional tools like CodeQL in terms of vulnerability detection rates.

    These reports provide a comprehensive overview of the current advancements in LLM-assisted vulnerability detection, showcasing both theoretical frameworks and practical implementations. If you need more specific insights or additional reports, feel free to ask!