This paper trains a perturbation model with the objective of maintaining the geometric distance between the original and adversarial document embeddings, while also maximizing the token-level dissimilarity be-tween the original and adversarial documents.
Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false target claim is divided across multiple loc...
Pedro Pereira, Eva Maia, Isabel Praça· 0 citations
DSPrompt is proposed, a Dynamic Soft Prompt defense framework that directly reshapes the retriever's embedding semantics, without modifying the retrieval pipeline, and is consistently outperforming existing defense baselines at a fraction of their computational cost.
Chang Liu, Y. Lai, Ming-Yue Cui et al.· 0 citations
Deep Supervised Adversarial Robust Hashing (DSARH), an end-to-end framework that leverages similarity matrices and learnable hash codes to construct gradient-based worst-case perturbations, enabling efficient adversarial training and robust feature learning for retrieval, is proposed.
Xing-Wei Zhang, Gang Zhou, Xiaolong Zheng et al.· IEEE Transactions on Pattern...· 0 citations
Machine learning has become a widely adopted approach for URL-based phishing detection, with many studies reporting F1 scores exceeding 0.95 on benchmark datasets. However, recent adversarial machine learning research has questioned the robustness of these models, suggesting that small input perturbations can severely...
It is proved that, under an honest-majority assumption and a representation-level separation condition, RAGSentinel exactly recovers a poison-free majority-sized context.
Yueyang Quan, Anjun Gao, Yu Xia et al.· 0 citations
A novel black-box image embedding inversion attack that reconstructs high-fidelity images using only query access to the embedding model or API, and introduces an embedding-guided cross-attention mechanism, where image embeddings serve as conditional signals to steer the generation process.
Lichao Sun, Yun-Cheng Wu, Hai-Chao Sha et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
