| Challenge: | Large language models (LLMs) have exhibited remarkable fluency across tasks, but their unethical applications are unclear. |
| Approach: | They propose a grammar error-free black-box attack that exploits LLM embeddings at the word-level while preserving original text quality. |
| Outcome: | The proposed attack compromises all detectors across domains and is transferable across source models. |
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Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
| Challenge: | Existing methods to detect large language models (LLMs) generated for plagiarism use paraphrases to rewrite them to evade detection. |
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Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
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MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)
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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang
| Challenge: | Existing research has focused on evaluating detection methods for specific domains or language models. |
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Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
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Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
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| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
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Close or Cloze? Assessing the Robustness of Large Language Models to Adversarial Perturbations via Word Recovery (2025.coling-main)
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| Challenge: | Existing models implicitly recover the original text, but it is unclear when they rely on context and when they implicitly do so. |
| Approach: | They propose to use a dictionary to recover adversarial words by using a phonetic, typo, and visual attack to study word recovery performance. |
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LLM-Generated Text May Harm Your Retrieval! A Robust Detection Strategy for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) improves accuracy and timeliness of large language models, but external corpora may become contaminated with LLM-generated texts. |
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Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on the vulnerabilities of Large Language Models to adversarial attacks . the tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity . |
| Approach: | This tutorial lays the foundation by explaining safety-aligned LLMs and concepts in cybersecurity. |
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Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025.findings-emnlp)
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| Challenge: | Prior work on machine generated text detection focused on identifying whether document was human or machine written, ignoring these fine-grained uses. |
| Approach: | They propose a machine-influenced text detector that learns to separate text samples from four primary types . the detector uses a subcategory guidance module to help separate the fine-grained categories . |
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What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection (2024.acl-long)
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| Challenge: | Social media bot detection has always been an arms race between advancements in machine learning and adversarial bot strategies to evade detection. |
| Approach: | They propose a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities and propose LLM-guided manipulation of user textual and structured information to evade detection. |
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