arXiv Machine Learning By Atri Vivek Sharma, Brian Formento, Alessio Lomuscio

Eliciting Intrinsic Hallucinations in LLMs via Semantically Equivalent Adversarial Attacks

Read the original on arXiv Machine Learning →

arXiv:2608. 04286v1 Announce Type: cross Abstract: Large language models (LLMs) are often used in conjunction with external knowledge sources to improve their factual accuracy and decrease hallucinations, through methods such as Retrieval-Augmented Generation (RAG).

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arXiv AI
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REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

arXiv:2605. 12813v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, making it important to systematically evaluate their reliability under realistic adversarial inputs.

By Buyun Liang, Jinqi Luo, Liangzu Peng, Kwan Ho Ryan Chan, Darshan Thaker, Kaleab A. Kinfu, Fengrui Tian, Hamed Hassani, Ren\'e Vidal
arXiv AI
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Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning

The paper introduces FAB, an attack that uses meta‑learning to embed dormant adversarial behaviors into large language models (LLMs). These behaviors remain inactive until the model is finetuned by downstream users, at which point the model can exhibit unwanted actions such as unsolicited advertising, jailbreakability, or over‑refusal. FAB is shown to be effective across multiple LLMs and resilient to various finetuning settings.

By Thibaud Gloaguen, Mark Vero, Robin Staab, Martin Vechev