@inproceedings{zhang-etal-2026-adversarial,
    title = "Adversarial Decoding: Generating Readable Documents for Adversarial Objectives",
    author = "Zhang, Collin  and
      Zhang, Tingwei  and
      Shmatikov, Vitaly",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.108/",
    pages = "2053--2068",
    ISBN = "979-8-89176-386-9",
    abstract = "We design, implement, and evaluate adversarial decoding, a new, generic text generation technique that produces readable documents for adversarial objectives such as RAG poisoning, jailbreaking, and evasion of defensive filters. Prior generation methods either produce easily detectable gibberish (even methods that optimize for low perplexity), or cannot handle objectives that include embedding similarity. In particular, they cannot produce readable adversarial documents that (1) are retrieved by RAG systems in response to broad classes of queries, and (2) adversarially influence subsequent generation. We measure the effectiveness of adversarial decoding for different objectives and demonstrate that it outperforms existing methods while producing adversarial documents that cannot be automatically distinguished from natural documents by fluency and readability."
}