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New research explores LLM prompting attacks and defenses

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore vulnerabilities and defenses in large language models (LLMs) and large vision-language models (LVLMs). The first paper introduces Robustness of Prompting (RoP), a strategy designed to enhance LLM resilience against adversarial perturbations by correcting input errors and generating optimal guidance prompts. The second paper details a Multi-Turn Adaptive Prompting Attack (MAPA) that targets LVLMs by alternating text-vision attacks and iteratively refining the attack trajectory to amplify malicious responses, outperforming existing methods on several benchmarks. AI

IMPACT New research highlights vulnerabilities in LLMs and LVLMs, suggesting a need for more robust prompting strategies and defenses against sophisticated attacks.

RANK_REASON Two academic papers published on arXiv detailing new methods for LLM robustness and LVLM attacks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores LLM prompting attacks and defenses

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Two academic papers published on arXiv detailing new methods for LLM robustness and LVLM attacks.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Lin Mu, Guowei Chu, Li Ni, Lei Sang, Yiwen Zhang ·

    Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

    arXiv:2506.03627v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to input perturbations, such as typographical err…

  2. arXiv cs.CV TIER_1 English(EN) · In Chong Choi, Jiacheng Zhang, Feng Liu, Yiliao Song ·

    Multi-Turn Adaptive Prompting Attack on Large Vision-Language Models

    arXiv:2602.14399v2 Announce Type: replace Abstract: Multi-turn jailbreak attacks have proven effective against text-only large language models (LLMs), where malicious content is gradually introduced to bypass safety alignment. However, effectively extending such attacks to large …

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