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Gradient-based Adversarial Attacks against Text Transformers

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arxiv 2104.13733 v1 pith:UIMJ67Z3 submitted 2021-04-15 cs.CL cs.AIcs.CRcs.LG

Chuan Guo , Alexandre Sablayrolles , Hervé Jégou , Douwe Kiela This is my paper · ORCID
classification cs.CLcs.AIcs.CRcs.LG
keywords adversarialattackgradient-baseddistributionattacksattainsblack-boxcontinuous-valued
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We propose the first general-purpose gradient-based attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Influence-Guided Concolic Testing of Transformer Robustness

    cs.SE 2025-09 conditional novelty 6.0 of 10

    SHAP-based branch prioritization lets a concolic tester find subtle one-pixel attacks on small Transformer classifiers, but the reported evidence is mixed and the abstract overstates results.

  2. Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations

    cs.AI 2025-06 conditional novelty 6.0 of 10

    SSP adds a learned, sample-specific noise prompt to an LLM input and scores hallucination by the cosine shift in intermediate representations, outperforming output-confidence baselines on QA benchmarks.

  3. VERA: Variational Inference Framework for Jailbreaking Large Language Models

    cs.CR 2025-06 conditional novelty 5.0 of 10

    VERA frames black-box jailbreaking as variational inference, training a LoRA-tuned attacker that samples diverse fluent prompts; reported ASRs are high but several evaluation choices weaken the SOTA claims.

  4. Universal Adversarial Attack on Aligned Multimodal LLMs

    cs.AI 2025-02 conditional novelty 4.0 of 10

    A single optimized image, trained through the vision and language modules, makes aligned multimodal LLMs produce dangerous responses across diverse prompts and some models.

  5. Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning

    cs.CR 2025-06

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