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LLMs power new adversarial attacks on neural ranking models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called CRAFT to attack neural ranking models used in information retrieval. This framework utilizes large language models to generate adversarial content, which is then used to fine-tune and optimize the ranking models. Experiments demonstrated that CRAFT significantly improves adversarial promotion rates and rank boosts across various ranking architectures, highlighting potential vulnerabilities in real-world retrieval systems. AI

IMPACT Highlights potential vulnerabilities in information retrieval systems due to generative AI, prompting the need for more robust defenses.

RANK_REASON This is a research paper detailing a new framework for attacking neural ranking models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLMs power new adversarial attacks on neural ranking models

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This is a research paper detailing a new framework for attacking neural ranking models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Amin Bigdeli, Amir Khosrojerdi, Radin Hamidi Rad, Morteza Zihayat, Charles L. A. Clarke, Ebrahim Bagheri ·

    Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models

    arXiv:2605.01591v1 Announce Type: cross Abstract: Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks often rely on heuristics or surrogate models, limiting effectiveness and transfera…

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