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Generative AI Security: Challenges and Countermeasures

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arxiv 2402.12617 v2 pith:D2IP2U34 submitted 2024-02-20 cs.CR cs.AIcs.CLcs.CYcs.LG

Banghua Zhu , Norman Mu , Jiantao Jiao , David Wagner This is my paper
classification cs.CRcs.AIcs.CLcs.CYcs.LG
keywords generativechallengessecurityacrosscountermeasuresdelvesdirectionsexcitement
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Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny. This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection

    cs.CR 2025-08 conditional novelty 5.0 of 10

    RTST, a two-agent moderator with an explainable Behavior ledger and per-prompt weight updates, reduced attack success rate from 12-63% to 0-17% on three jailbreak benchmarks with Gemini 2.5 Flash.

  2. Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models

    cs.CR 2025-08 unverdicted novelty 5.0 of 10

    The abstract claims that 10 poisoned samples can backdoor multiple text-to-image models with over 90% attack success and resistance to defenses, but the supplied body is a different paper.

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