Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented Generation
Wentao Zhang, Yan Zhuang Tencent, ZhuHang Zheng, Mingfei Zhang, Jiawen Deng University of Electronic Science and Technology of China, Fuji Ren University of Electronic Science and Technology of China
Existing jamming attacks on Retrieval-Augmented Generation (RAG) systems typically induce explicit refusals or denial-of-service behaviors, which are conspicuous and easy to detect. In this work, we formalize a subtler availability threat, termed soft failure, which degrades system utility by inducing fluent and coherent yet non-informative responses rather than overt failures. We propose Deceptive Evolutionary Jamming Attack (DEJA), an automated black-box attack framework that generates adversarial documents to trigger such soft failures by exploiting safety-aligned behaviors of large language models. DEJA employs an evolutionary optimization process guided by a fine-grained Answer Utility Score (AUS), computed via an LLM-based evaluator, to systematically undermine the certainty of answers while maintaining high retrieval success.Extensive experiments across multiple RAG configurations and benchmark datasets show that DEJA consistently drives responses toward low-utility soft failures and that the resulting adversarial documents maintain high stealth and effectiveness, proving resilient against common mitigation strategies including perplexity-based detection and input perturbations.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表