arXiv AI June 16, 2026 By Yuyang Dai, Yushun Dong

Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot

arXiv:2606. 15810v1 Announce Type: cross Abstract: Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or degrade utility for legitimate users.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jun 16

Cordyceps: Covert Control Attacks on LLMs via Data Poisoning

arXiv:2605. 26595v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison.

By Zedian Shao, Charles Fleming, Teodora Baluta

llms fine-tuning

arXiv AI

Sep 30

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

arXiv:2406.00083v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases t...

By Jiaqi Xue, Mengxin Zheng, Yebowen Hu, Fei Liu, Xun Chen, Qian Lou

llms rag robotics safety

arXiv AI

Jun 9

Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

arXiv:2604. 08304v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conflates with inherent LLM flaws.

By Yuming Xu, Mingtao Zhang, Zhuohan Ge, Haoyang Li, Nicole Hu, Yongqi Zhang, Zhiyuan Wen, Jason Chen Zhang, Qing Li, Lei Chen

llms rag agents robotics multimodal

arXiv Computation and Language

Aug 28

An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic

The paper presents a simple detector for model extraction attacks on large language model APIs. It frames detection as a benign‑calibrated traffic‑window distribution test, embedding queries into a semantic space and using maximum mean discrepancy (MMD) to compare against historical benign traffic. Evaluated on fourteen attacker‑normal query pairs across four extraction scenarios, MMD achieves near‑perfect true‑positive rates while maintaining a 0.3% false‑positive rate, outperforming several existing baselines.

By Shuze Liu, Qianwen Guo, Yushun Dong

llms

arXiv AI

Aug 6

Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness

arXiv:2608. 04519v1 Announce Type: new Abstract: Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not.

By Haoting Qian, Qingjie Zhang, Zhicong Huang, Cheng Hong, Han Qiu

llms benchmarks

arXiv AI

Jun 3

Inference Cost Attacks for Retrieval-Augmented Large Language Models

arXiv:2606. 02643v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamically retrieves and synthesizes information from external knowledge sources.

By Chengliang Liu, Liangbo Ning, Yujuan Ding, Wenqi Fan

llms rag agents reinforcement-learning robotics safety

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Related stories

Cordyceps: Covert Control Attacks on LLMs via Data Poisoning

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic

Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness

Inference Cost Attacks for Retrieval-Augmented Large Language Models