The integration of Large Language Model (LLM)-based code assistants into IDEs has boosted developer productivity, but cloud‑based solutions pose severe privacy risks for enterprises handling proprietary code. Existing on‑premise alternatives lack semantic code retrieval, multi‑GPU optimization, and rigorous productivity validation. We present an enterprise‑oriented, customizable IDE plugin that uses internally hosted LLMs, combining client‑side tiered retrieval (Personal CodeRAG with SiamBERT/CodeSage) and vLLM‑optimized inference on dedicated hardware. No code leaves the organization. Our benchmark of four inference frameworks shows vLLM and LMDeploy achieve $\approx$20 ms time‑to‑first‑token on LLaMA‑3‑8B, suitable for real‑time interaction. In a three‑month study with 100 developers, the plugin reduced mean cycle time by 15.4\%, with perceived productivity gains of 50-70\% for tasks like code explanation and unit test generation. This work provides the first systematic evaluation of on‑premise LLM‑assisted coding that quantifies both objective and subjective productivity impacts.
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Securing research funding remains fragmented and time-consuming: researchers must navigate separate databases across dozens of agencies while simultaneously drafting competitive proposals. We present GRAIL, a web-based platform that unifies grant discovery and proposal writing through conversational AI. Users describe their research interests in natural language to explore opportunities from a unified index of 11.8K U.S. federal and nonprofit grant opportunities; within the document editor, integrated AI assistance supports real-time proposal revision and refinement. The system runs in any modern browser without installation. Conference attendees are invited to interact with the live system at the demo booth, explore the grant discovery and writing assistance workflows, and provide feedback on the user experience.
While we have witnessed extraordinary progress across various subfields of AI, the deployment of these systems in safety-critical and mission-critical domains has lagged behind. A key requirement for such deployments is the availability of AI systems capable of generating optimal behavior that can be formally verified and effectively used by non-AI experts from diverse backgrounds. While significant advances have been made toward building such systems, we still lack comprehensive formal frameworks to model and analyze the complex dynamics of human-AI interaction. In this talk, I will introduce the Human-Aware AI framework, a multi-agent planning framework specifically designed to support and reason about human-AI interaction. I will show how this framework provides novel solutions to challenges such as explainability, value alignment, and proactive assistance, and demonstrate its application in domains including intelligent tutoring systems, cybersecurity, and robotics.
The rapid advancement of Artificial Intelligence (AI) has accelerated the development of personalised healthcare. However, the clinical adoption of deep learning remains constrained by a persistent âtrust gapâ surrounding model transparency, security, and data privacy. This talk presents a research vision for building trustworthy human-centred AI in computer audition and biosignal processing by integrating signal processing, machine learning, and healthcare. This vision is structured around three interconnected pillars: Sensing, Understanding, and Empowerment. The Sensing pillar focuses on enabling machines to perceive clinically relevant information from multimodal biosignals, transforming body sounds and physiological signals into non-invasive, accessible, and cost-effective windows into human health and well-being. The Understanding pillar addresses the black-box nature of modern AI systems, with the goal of ensuring transparency, efficiency, robustness, and security in clinical deployment. This is achieved through the development of explainable AI methods, knowledge distillation techniques, and defences against adversarial attacks, fostering AI systems that clinicians can trust and interpret. The Empowerment pillar seeks to restore natural communication for individuals with speech impairments, such as laryngectomy patients. By developing Silent Speech Interfaces (SSIs) that translate facial muscle activity (EMG) directly into audible speech, this research advances speech intelligibility, naturalness, real-time causal architectures, and multi-speaker communication scenarios. Collectively, these research directions aim to establish a foundation of trustworthy, human-centred AI that is not only accurate and secure, but also empathetic, accessible, and impactful, ultimately enhancing healthcare, communication, and quality of life.
Large language models (LLMs) require substantial investments and are increasingly deployed in high-stakes domains, making it critical to protect LLM-related assets and to trace their provenance. Identity technologies such as fingerprinting and watermarking address these needs by enabling ownership verification and attribution, and have rapidly emerged as an active research focus. However, existing techniques lack a systematic organisation, leading to two key issues, terminological confusion and isolated research lines, that have hindered the development of this research field. To this end, we present a comprehensive review of LLM identity techniques, focusing on fingerprinting and watermarking across the LLM lifecycle, including datasets, models, and generated content. We make three primary contributions. First, we introduce implicit identity (Implicit-ID for short) as a unifying abstraction and distinguish fingerprinting from watermarking. Second, we propose a lifecycle-based taxonomy that organises techniques by asset type and verification role, aligning each with asset protection or provenance. Third, we establish an evaluation framework around three objectives---identifiability, robustness, and deployability. Together, these contributions structure the landscape of LLM identity techniques, clarify terminology, and highlight directions toward secure deployment.
Large Language Models (LLMs) are transforming blockchain security and analytics, yet a systematic evaluation of their capabilities remains limited. This survey provides a comprehensive, AI‑centric assessment of LLM‑based methods across over 70 recent studies spanning 11 application domains, such as security auditing, transaction fraud detection, and cryptocurrency portfolio management. Our unified taxonomy standardizes task formulations and evaluation practices to enable a comparison of six LLM roles across domains. For each domain, we review input representations tailored to blockchain data; LLM architectures, learning and inference paradigms, e.g., fine‑tuning, retrieval‑augmented generation, and agentic strategies. Our review analyzes the strengths, limitations, and emerging patterns of LLM roles observed in current systems. Finally, we provide practical guidance for selecting LLMs for specific roles and outline promising research directions.
Vein biometrics has emerged as a promising biometric modality for personal identity authentication, benefiting from its intrinsic properties such as high discriminative capability, resistance to forgery, and contactless acquisition. Recent advances in artificial intelligence, particularly deep learning, have significantly accelerated its development. This paper presents a comprehensive and systematic survey of AI-enhanced vein biometrics. We review fundamental principles, publicly available datasets, and evaluation protocols, and systematically analyze existing methods across the entire vein biometric pipeline, including acquisition, preprocessing, feature extraction, recognition and verification, security and privacy protection, and multimodal fusion. Furthermore, we summarize representative application scenarios, identify key challenges, and highlight promising directions for future research. To facilitate reproducible research and long-term development of the field, we release an open, evolving research resource Awesome-Vein-Biometrics that systematically summarizes and tracks recent advances in vein biometrics.
Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origin and major advancements. Secondly, we formulate a systematic taxonomy that organizes existing methods into three categories centered on hand-crafted, sequence-based, and graph-based modeling. Then, we elaborate on the representative models along these three types with an illustration of foundational mechanisms. Meanwhile, we provide an overview of mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding common methods. A thorough evaluation of state-of-the-art SRID methods is further conducted over various types of benchmarks and protocols to compare their effectiveness, efficiency, and key properties. Finally, we present the key challenges and prospects to advance future research, and highlight interdisciplinary applications of SRID with a case study. A curated collection of valuable resources is available at https://github.com/Kali-Hac/3D-SRID-Survey.
Deep neural networks are vulnerable to adversarial examples, posing serious security risks. While GAN-based attacks such as AdvGAN enable fast adversarial example generation, they typically operate in a single step, limiting attack effectiveness and transferability. In this work, we propose PAR-AdvGAN, a progressive auto-regressive GAN framework that iteratively refines adversarial perturbations. By conditioning each iteration on the previous output, PAR-AdvGAN produces stronger, more transferable attacks while maintaining low visual distortion and high generation speed. Experiments on multiple models and datasets demonstrate that PAR-AdvGAN significantly outperforms baseline methods in attack success rate, achieving up to 335.5 frames per second, highlighting its practical efficiency for black-box attack scenarios.
Fully autonomous IP-to-Root penetration testing remains challenging for LLM agents. We conduct an exploratory study on 10 LLMs and introduce AutoPentest-Bench, an end-to-end benchmark with 13 VulnHub targets and 93 sub-tasks. From 130 interaction logs, we identify three challenges: Rigid Strategy, Contextual Forgetting, and Command Generation Hallucination. To address them, we propose PentestLLMAgent, which integrates a Task Dependency Graph (TDG) for dynamic planning and backtracking; a Hierarchical Multi-Agent Architecture (HMA) with function-calling-based tool invocation, output filtering, and semantic compression, and Executable Knowledge-Guided Command Generation (EKG-CG) for retrieving and executing pre-validated, environment-compatible commands. Evaluations demonstrate strong effectiveness: on end-to-end AutoPentest-Bench, PentestLLMAgent achieves a 77% success rate; on AutoPT’s web exploitation benchmark, it attains a 95% overall pass rate; and on the privilege escalation benchmark, it achieves a 100% success rate. In realistic end-to-end runs, it averages 10.9 minutes, 36 interaction rounds, and 69.6K tokens per target. The code, benchmark, and executable knowledge base are publicly available at https://github.com/sanbai123/PentestLLMAgent_code-and-videos.
Credit card fraud threatens global payment ecosystems, causing billions in losses and undermining public trust. Efficient fraud detection remains challenging due to surging transaction volumes and evolving tactics. While Graph Neural Networks (GNNs) excel at modeling structural relationships, they struggle in real-world scenarios characterized by label scarcity and often overlook discriminative feature-level signals, leaving rich risk signals underutilized without costly manual engineering. To address this, we propose DRESS, a Deep Reinforcement Learning (DRL) Enhanced Semi-supervised GNN framework. It employs a DRL agent to automatically capture and enhance feature-level risks, fusing them with graph-based structural risks and propagating via a gated temporal attention network for final prediction. To mitigate inefficient exploration of the DRL module, we incorporate a feature self-attention layer to weigh feature contributions to fraud detection and employ self-supervised intrinsic rewards to help optimize the DRL module efficiently. Extensive experiments on real-world datasets demonstrate that DRESS outperforms state-of-the-art methods, especially in low-label scenarios with only 2%–10% labeled samples. By empowering resource-limited institutions to combat fraud and prevent financial loss, DRESS secures the digital trust essential for inclusive growth, contributing to AI for poverty alleviation and economic development.
The rapid evolution of online scams, driven by transnational networks and mass-produced social engineering scenarios, has exposed the speed limitations of conventional detection, necessitating tighter inter-agency coordination. While LLMs show promise in scam identification, their role in accelerating integrated response frameworks remains underexplored. We propose Counter-Scam, a unified LLM-based multi-agent framework that orchestrates end-to-end response from initial detection to crime investigation. The framework first proposes safe data guidelines, emphasizing non-public scam data and secure dataset construction via scam-specific NER. Developed with insights from 37 stakeholders to reduce delays and improve analytical efficiency, the system integrates CSRA (multi-agent mitigation), CSRT (nine role-aligned NLP tasks), and CSRD (a corpus of 185,300 scam cases and 38,587 knowledge entries). Experiments show that fine-tuned sLLMs surpass commercial models with over 10% in all CSRT tasks and a 0.24 F1 improvement in scam-specific NER. This proves the framework's capability for enabling rapid, collaborative mitigation of online scam.
Real-world deployment of large language models (LLMs) necessitates a robust and low-latency approach to detect prompt injections; existing low-overhead methods fail to simultaneously boost robustness and reduce latency. Current defenses for prompt injection either rely on brittle heuristics or invoke costly auxiliary models, imposing a significant runtime burden. We introduce BERM, a lightweight framework that performs in-situ detection by modeling a host LLM’s internal representations extracted during prefill, adding negligible overhead. Our approach trains a lightweight classifier atop the LLM by learning a compact manifold of benign representations via joint contrastive learning to maximize the separation from malicious representations. At inference, this pre-trained classifier enables in-situ detection without invoking auxiliary guard models. On a diverse landscape of prompt injection attacks, our framework establishes a new state-of-the-art, achieving an F1-score 5.2 percentage points (pp) higher than the best prior work. Critically, BERM achieves this while being over 12x faster, reducing inference overhead to near-zero.
Text-to-Image (T2I) diffusion models have been widely adopted due to their strong generative capabilities, while Low-Rank Adaptation (LoRA) has emerged as an efficient mechanism for customizing these models for diverse creative and commercial applications. This trend has fostered LoRA-centric service platforms that that enable the customization and commercial distribution of LoRA modules according to user requirements. However, the growing prevalence of LoRA and its critical role in customized AI services have raised urgent concerns about LoRA copyright protection. To address this gap, we propose LoRA2D, an authorized dual-watermarking framework specifically designed to protect LoRA modules in T2I diffusion models. LoRA2D integrates license-based authorization control with explicit watermarks as visible deterrents for unauthorized or trial usage, which can be removed upon valid authorization, while persistently embedding an implicit watermark for robust black-box ownership verification. Extensive experiments on multiple image-generation datasets demonstrate the effectiveness and practicality of LoRA2D for securing copyrights in LoRA-adapted T2I diffusion models.
Large Vision--Language Models (VLMs) unlearning tends to eliminate the influence of ``to-be-forgotten'' content in the training corpora, algorithmically by suppressing the likelihood of faithfully generating responses on forget-target inputs. The injection of adversarial inputs can manipulate the unlearned VLM's generation towards the attacker’s will, forcing the reproduction of the supposedly forgotten content and undermining the reliability of expected forgetting behavior. However, most attacks assume access to the unlearned VLM’s architecture or parameters, or to output logits via queries. In this paper, we propose SISA, a novel attack framework for crafting adversarial inputs to manipulate generation towards the forgotten target, which only requires access to a surrogate, pre-trained VLM. SISA advances prior attacks by exploiting the persistence of visual sink tokens after unlearning as a stable structural anchor for semantic alignment. SISA induces a sink regime on a candidate visual token to build the structural anchor that influences generation, and then semantically aligns model output to the target while conditioning on attention through the induced sink token, reinforcing the anchor for desired elicitation. With sink persistence and sink-conditioned semantic anchoring, SISA crafts transferable adversarial inputs. Evaluation on diverse unlearned VLM settings confirms the effectiveness of SISA, increasing the outputs’ semantic agreement with forgotten targets by up to 6.9X relative to clean inputs.
Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove the influence of previously used training data upon request. Retraining a federated model from scratch is prohibitively expensive, motivating federated unlearning (FU). However, existing FU methods suffer from high unlearning overhead, utility degradation caused by entangled knowledge, and unintended relearning during post-unlearning recovery. In this paper, we propose FedCARE, a unified and low overhead FU framework that enables conflict-aware unlearning and relearning-resistant recovery. FedCARE leverages gradient ascent for efficient forgetting when target data are locally available and employs data free model inversion to construct class level proxies of shared knowledge. Based on these insights, FedCARE integrates a pseudo-sample generator, conflict-aware projected gradient ascent for utility preserving unlearning, and a recovery strategy that suppresses rollback toward the pre-unlearning model. FedCARE supports client, instance, and class level unlearning with modest overhead. Extensive experiments on multiple datasets and model architectures under both IID and non-IID settings show that FedCARE achieves effective forgetting, improved utility retention, and reduced relearning risk compared to state of the art FU baselines. The source code can be found at https://github.com/thechosenchu/FedCARE.
The learnware paradigm supports model reuse by pairing each submitted model with a specification, a lightweight representation used by the learnware dock system to identify, match, and reuse models without accessing raw data. While specifications are essential for learnware identification, they are also data-dependent public artifacts and it is not clear whether they reveal private information. Recently, the Parameter Vector (PAVE) specification has been proposed and shown to be effective for learnwares, yet its privacy properties remain largely unexplored. In this paper, we provide the first theoretical privacy analysis for PAVE. Specifically, we first formalize two specification-induced risks in the learnware paradigm: the disclosure risk of the released specification and the amplification risk that the specification may strengthen attacks against the released model. Second, we characterize when compact PAVE releases admit intrinsic differential privacy (DP): under natural structural conditions of learnware docks, the compact PAVE specification satisfies an (ε, δ)-DP guarantee without explicit additive noise through a Gaussian-sketch view of stable parameter variations, and for regimes outside these conditions, we further provide DP-Stabilized-PAVE as a certified differentially private variant. Third, we show that the resulting DP guarantees control both disclosure risk and amplification risk, and we analyze the induced privacy--utility trade-off to guide effective learnware identification while preserving privacy.
The quantal response (QR) model is widely used in Stackelberg security games (SSGs) to capture boundedly rational adversaries. Existing work on SSGs under QR, however, almost exclusively assumes a homogeneous attacker population, ignoring heterogeneity in attacker preferences and rationality. We study SSG with mixed quantal response attackers, where the follower population consists of multiple discrete attacker types, each following a type-specific QR model. The defender allocates limited resources across targets, while an attacker drawn from this heterogeneous population observes the defender’s strategy and attacks a single target. This results in a highly non-convex equilibrium computation problem. We develop a polynomial-time approximation scheme (PTAS) for this setting when the number of attacker types is bounded, based on an exponential cone programming formulation combined with a carefully designed Branch-and-Bound procedure. Experiments demonstrate that our approach outperforms standard gradient-based methods and that explicitly modeling attacker heterogeneity yields significant gains over traditional SSG models with a single QR attacker.
Vertical Federated Learning (VFL) involves multiple participants collaborating to train machine learning models on distinct feature sets from the same data samples. This training paradigm with distributed updating focuses on secure and efficient communication. Nevertheless, the trained models exhibit heightened vulnerability to adversarial attacks during inference, which can provoke misclassification. Adversarial Training (AT), which involves exposing models to intentionally crafted misleading examples during training, is widely regarded as the most effective method for enhancing model robustness. However, the significant communication costs entailing such example generation within the VFL context pose an open challenge to developing a Vertical Federated Adversarial Learning (VFAL) framework. To this end, we introduce a Communication-Efficient Vertical Federated Adversarial Learning framework, named CE-VFAL. CE-VFAL framework incorporates the lazy propagation principle, confining most propagations to client models during adversarial updates, thereby minimizing frequent client-server interactions. Moreover, CE-VFAL seamlessly integrates Zeroth Order Optimization (ZOO) into communication, effectively reducing communication load by transmitting the loss difference derived from the raw and perturbed embeddings for multiple point estimation. Furthermore, our theoretical analysis demonstrates the sublinear convergence rate by containing the errors caused by multi-source approximate gradients. Extensive experiments corroborate the robust performance while significantly reducing communication costs.
Federated Reinforcement Learning from Human Feedback (RLHF) enables the collaborative alignment of Large Language Models (LLMs) while preserving privacy, yet it faces critical bottlenecks arising from data heterogeneity. Existing approaches typically rely on rigid client-level clustering, which overlooks intra-client heterogeneity and fails to adapt to the multifaceted needs of individual users. To address this, we propose FedPrism, a novel framework that shifts alignment granularity from the coarse client level to the precise instance level. Similar to an optical prism dispersing mixed light into distinct spectral components, FedPrism decomposes complex, intra-client heterogeneous data streams by dynamically routing individual samples to specialized experts based on semantic features. Crucially, we devise a Posterior-Guided Performance Distillation (PGPD) mechanism that leverages experts' actual training loss as self-supervision to autonomously refine routing policies without explicit labels. Extensive experiments show that FedPrism not only establishes new state-of-the-art results but also effectively mitigates the negative transfer prevalent in non-IID settings, ensuring superior alignment fidelity.