Papers by Kriti Aggarwal

2 papers
Red Queen: Exposing Latent Multi-Turn Risks in Large Language Models (2025.findings-acl)

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Challenge: Existing jailbreak strategies are single-turn with explicit malicious intent, failing to reflect real-world scenarios where interactions can be multi-turn and users can conceal intents.
Approach: They propose a jailbreak attack that constructs a multi-turn scenario and conceals malicious intent under the guise of preventing harm.
Outcome: The proposed attack achieves 87.6% attack success rate (ASR) on GPT-4o and 77.1% on Llama3-70B compared to previous jailbreak attacks .
DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)

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Challenge: DUBLIN is a pixel-based visual document understanding model that does not rely on OCR.
Approach: They propose a pixel-based visual document understanding model that does not rely on OCR.
Outcome: The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning.

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