Yinghan Zhou

Zhengxian Wu, Juan Wen, Wanli Peng et al.

Customized Large Language Model (LLM) agents face a critical security threat from black-box instruction backdoors, where malicious behaviors are covertly injected through hidden system instructions. Although existing prompt-based defenses can often detect poisoned inputs, they generally fail to recover correct outputs once the backdoor is activated. In this paper, we first conduct a mechanistic analysis of LLM behavior under instruction backdoors and reveal two pivotal phenomena: (1) cognitive override, in which backdoor triggers dominate the reasoning process and suppress task-relevant context, and (2) abnormal semantic correlation, where triggers establish excessively strong semantic associations with attacker-specified target labels. Based on these insights, we propose a $\textbf{S}$oft $\textbf{L}$abel mechanism and key-extraction-guided CoT-based defense against $\textbf{I}$nstruction backdoors in A$\textbf{P}$Is (SLIP). To counteract the cognitive override, the key-extraction-guided Chain-of-Thought (KCOT) explicitly guides the model to extract task-relevant keywords and phrases rather than only considering the single trigger or overall text semantics. To neutralize the trigger's abnormal semantic correlation, the soft label mechanism (SLM) quantifies semantic correlations and employs statistical clustering to filter anomalous phrases before aggregating reliable keywords and phrases for prediction. Extensive experiments show that SLIP reduces the average attack success rate to 25.13$\%$, improves clean accuracy to 87.15$\%$, and outperforms state-of-the-art black-box defenses.

Yinghan Zhou, Juan Wen, Wanli Peng et al.

The growing popularity of large language models has raised concerns regarding the potential to misuse AI-generated text (AIGT). It becomes increasingly critical to establish an excellent AIGT detection method with high generalization and robustness. However, existing methods either focus on model generalization or concentrate on robustness. The unified mechanism, to simultaneously address the challenges of generalization and robustness, is less explored. In this paper, we argue that robustness can be view as a specific form of domain shift, and empirically reveal an intrinsic mechanism for model generalization of AIGT detection task. Then, we proposed a novel AIGT detection method (DP-Net) via dynamic perturbations introduced by a reinforcement learning with elaborated reward and action. Experimentally, extensive results show that the proposed DP-Net significantly outperforms some state-of-the-art AIGT detection methods for generalization capacity in three cross-domain scenarios. Meanwhile, the DP-Net achieves best robustness under two text adversarial attacks. The code is publicly available at https://github.com/CAU-ISS-Lab/AIGT-Detection-Evade-Detection/tree/main/DP-Net.

Jiaxuan Wu, Yinghan Zhou, Wanli Peng et al.

Training large language models (LLMs) is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs has emerged as a prevalent method for establishing model ownership. However, existing back-door-based methods suffer from limited stealth and efficiency. To simultaneously address these issues, we propose EditMF, a training-free fingerprinting paradigm that achieves highly imperceptible fingerprint embedding with minimal computational overhead. Ownership bits are mapped to compact, semantically coherent triples drawn from an encrypted artificial knowledge base (e.g., virtual author-novel-protagonist facts). Causal tracing localizes the minimal set of layers influencing each triple, and a zero-space update injects the fingerprint without perturbing unrelated knowledge. Verification requires only a single black-box query and succeeds when the model returns the exact pre-embedded protagonist. Empirical results on LLaMA and Qwen families show that EditMF combines high imperceptibility with negligible model's performance loss, while delivering robustness far beyond LoRA-based fingerprinting and approaching that of SFT embeddings. Extensive experiments demonstrate that EditMF is an effective and low-overhead solution for secure LLM ownership verification.

Ziwei Zhang, Juan Wen, Wanli Peng et al.

In-Context Learning (ICL) and efficient fine-tuning methods significantly enhanced the efficiency of applying Large Language Models (LLMs) to downstream tasks. However, they also raise concerns about the imitation and infringement of personal creative data. Current methods for data copyright protection primarily focuses on content security but lacks effectiveness in protecting the copyrights of text styles. In this paper, we introduce a novel implicit zero-watermarking scheme, namely MiZero. This scheme establishes a precise watermark domain to protect the copyrighted style, surpassing traditional watermarking methods that distort the style characteristics. Specifically, we employ LLMs to extract condensed-lists utilizing the designed instance delimitation mechanism. These lists guide MiZero in generating the watermark. Extensive experiments demonstrate that MiZero effectively verifies text style copyright ownership against AI imitation.