DEW creates a robust watermark for LLM text by applying vector-space operations to dual embeddings and hiding the signal via key-seeded random projections, showing improved detection after paraphrasing and translation.
Generative ai misuse: A taxonomy of tactics and insights from real-world data
2 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
2
Pith papers citing it
14
external citations · Pith
abstract
Generative, multimodal artificial intelligence (GenAI) offers transformative potential across industries, but its misuse poses significant risks. Prior research has shed light on the potential of advanced AI systems to be exploited for malicious purposes. However, we still lack a concrete understanding of how GenAI models are specifically exploited or abused in practice, including the tactics employed to inflict harm. In this paper, we present a taxonomy of GenAI misuse tactics, informed by existing academic literature and a qualitative analysis of approximately 200 observed incidents of misuse reported between January 2023 and March 2024. Through this analysis, we illuminate key and novel patterns in misuse during this time period, including potential motivations, strategies, and how attackers leverage and abuse system capabilities across modalities (e.g. image, text, audio, video) in the wild.
years
2026 2representative citing papers
A qualitative study of 21 U.S. GenAI users reveals that existing security and privacy transparency is perceived as ineffective and lacking credibility, leading users to rely on proxies like popularity and constraining high-stakes use.
citing papers explorer
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Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings
DEW creates a robust watermark for LLM text by applying vector-space operations to dual embeddings and hiding the signal via key-seeded random projections, showing improved detection after paraphrasing and translation.
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Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
A qualitative study of 21 U.S. GenAI users reveals that existing security and privacy transparency is perceived as ineffective and lacking credibility, leading users to rely on proxies like popularity and constraining high-stakes use.