1 citations · 1 across the 14 of their papers we have counts for
19 papers
SafeEvo: Deciphering the Safety Alignment Mechanism and Evolution in Language Models
Miao Yu, Hao Huang, Lu Yuan +3
Safety interpretability advances the study of Large Language Model (LLM) alignment from behavioral constraints driven by data or algorithms towards a deeper understanding of intern…
Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models
Qiankun Li, Yuechen Zhang, Bowen Chen +4
Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whether a particular adversarial f…
In-Place Instruction Following in Diffusion Language Models
Zheng Nie, Zherui Li, Jiaming Zhang +3
Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored at arbitrary output positions,…
TINA+: Probing Residual Visual Knowledge in Unlearned Diffusion Models via Diffusion-Consistent Text-Free Inversion
Qianlong Xiang, Miao Zhang, Kun Wang +3
Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for preventing harmful content. Existing adversarial probes ev…
From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs
Yuanhe Zhang, Weiliu Wang, Jie Ren +7
Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency signals that are inaudible to…
Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
Jiaming Zhang, Boyang Chen, Zherui Li +14
Once visual content enters an AI pipeline, its owner often retains little technical control over how it is used. Legal and regulatory remedies can address misuse, but many technica…
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
Leitao Yuan, Qinghua Mao, Daizong Liu +5
Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to clo…
Large Vision-Language Models Get Lost in Attention
Gongli Xi, Ye Tian, Mengyu Yang +5
Despite the rapid evolution of training paradigms, the decoder backbone of large vision--language models (LVLMs) remains fundamentally rooted in the residual-connection Transformer…
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
Kaiwen Luo, Zhenhong Zhou, Leyan Wang +32
Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…
Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models
Junyao Yang, Chen Qian, Kun Wang +4
The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-step ``slow thinking'' reason…
ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety
Kun Wang, Cheng Qian, Miao Yu +6
Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threatened by critical safety vulnera…
SafeSeek: Universal Attribution of Safety Circuits in Language Models
Miao Yu, Siyuan Fu, Moayad Aloqaily +6
Mechanistic interpretability reveals that safety-critical behaviors (e.g., alignment, jailbreak, backdoor) in Large Language Models (LLMs) are grounded in specialized functional co…
STEP: Detecting Audio Backdoor Attacks via Stability-based Trigger Exposure Profiling
Kun Wang, Meng Chen, Junhao Wang +6
With the widespread deployment of deep-learning-based speech models in security-critical applications, backdoor attacks have emerged as a serious threat: an adversary who poisons a…
TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models
Qianlong Xiang, Miao Zhang, Haoyu Zhang +3
Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for their safe deployment to prevent the creation of harmful c…
HearSay Benchmark: Do Audio LLMs Leak What They Hear?
Jin Wang, Liang Lin, Kaiwen Luo +8
While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications remain largely unexplored. This pa…
ChronosAudio: A Comprehensive Long-Audio Benchmark for Evaluating Audio-Large Language Models
Kaiwen Luo, Liang Lin, Yibo Zhang +8
Although Audio Large Language Models (ALLMs) have witnessed substantial advancements, their long audio understanding capabilities remain unexplored. A plethora of benchmarks have b…
Backdoor Collapse: Eliminating Unknown Threats via Known Backdoor Aggregation in Language Models
Liang Lin, Miao Yu, Moayad Aloqaily +5
Backdoor attacks are a significant threat to large language models (LLMs), often embedded via public checkpoints, yet existing defenses rely on impractical assumptions about trigge…
Backdoor Attribution: Elucidating and Controlling Backdoor in Language Models
Miao Yu, Zhenhong Zhou, Moayad Aloqaily +5
Fine-tuned Large Language Models (LLMs) are vulnerable to backdoor attacks through data poisoning, yet the internal mechanisms governing these attacks remain a black box. Previous…
DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models
Zherui Li, Zheng Nie, Zhenhong Zhou +7
The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from Autoregressive LLMs, stemming from th…