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LLMs automate cybersecurity attack path generation with CVE2AP

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CVE2AP, a novel approach that utilizes large language models (LLMs) to automatically generate PDDL-encoded attack paths from natural language CVE descriptions. This method aims to overcome the limitations of manual AP modeling by leveraging LLMs' reasoning capabilities to transform threat intelligence into formal representations. CVE2AP incorporates an error-feedback mechanism for iterative refinement and has demonstrated effective generation of high-quality PDDL-encoded attack paths, with GPT-5.5 showing a favorable quality-cost trade-off. AI

IMPACT Automates cybersecurity analysis by translating threat intelligence into formal, machine-verifiable representations, potentially accelerating vulnerability assessment.

RANK_REASON The cluster contains an academic paper detailing a new method for automated generation of PDDL-encoded attack paths using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs automate cybersecurity attack path generation with CVE2AP

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The cluster contains an academic paper detailing a new method for automated generation of PDDL-encoded attack paths using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lin Cui, Vincenzo Scotti, Raffaela Mirandola ·

    CVE2AP: Automated Generation of PDDL-Encoded Attack Paths via Large Language Models

    arXiv:2610.03383v1 Announce Type: new Abstract: Attack Path (AP) modeling is fundamental to cybersecurity analysis, where the Planning Domain Definition Language (PDDL) has been widely adopted to encode APs into formal and machine-verifiable representations for automated reasonin…

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