arXiv AI September 21, 2026 By Byeongseo Min, Yongwoo Lee, Young-Sik Kim, Yongjune Kim

HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference

HE-Guardrail is a framework that applies homomorphic encryption to enforce guardrails against jailbreak attacks during encrypted large language model inference. It evaluates guardrail mechanisms—Llama Guard, JBShield, and GradSafe—directly on encrypted data, deciding whether to return the model’s response to the client. The approach preserves confidentiality while closely matching the decisions of plaintext guardrails, offering varied security-efficiency-utility trade‑offs.

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arXiv AI

Sep 11

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llms fine-tuning safety

arXiv AI

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llms rag agents efficiency safety

arXiv AI

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llms fine-tuning benchmarks safety

arXiv Machine Learning

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By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

llms nlp safety

arXiv AI

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By Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca, Graham Cormode, Carsten Maple

benchmarks safety

Hugging Face Trending Papers

Jul 7

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.

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Arbitrary Cipher Attacks Against Large Language Models Do Not Require Fine-Tuning

Stealing Reasoning Traces from Proprietary LLM APIs

From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

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