arXiv subjects

Mohaiminul Al Nahian

Publications and source records attributed to Mohaiminul Al Nahian.

7 recordsLinked to original sources

arXiv2026

CacheTrap: Unveiling a Stealthier Gray-Box Trojan against LLMs

The rapid advancement of large language models (LLMs) has sparked growing interest in understanding their security vulnerabilities, particularly Trojan attacks that enable stealthy manipulation of model behavior. Traditional Trojan methods typically alter inputs and/or model weights, relying on white-box assumptions that require access to data or model internal parameters. In this work, we present CacheTrap, the first gray-box Trojan attack targeting the Key-Value (KV) cache of LLMs. This method induces a single-bit flip in the KV cache, serving as a transient trigger. When activated, this trigger causes the model to exhibit targeted actions without changing inputs or model weights. CacheTrap introduces an efficient search algorithm to locate vulnerable positions in the KV cache, independent of model weights or datasets. Extensive experiments on five open-source LLMs show a remarkable near 100% attack success rate (with the trigger) while preserving benign accuracy (without the trigger) by flipping just one bit in the KV cache. Code is available at https://github.com/ML-Security-Research-LAB/CacheTrap

cs.CR↗

arXiv2026

TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge

Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the attack landscape is the distinction between white-box and black-box threat models, as the latter poses challenges that limit attack effectiveness when access to model information is limited. As a result, using Trusted Execution Environments (TEEs) enhances security for machine learning applications by protecting model confidentiality and execution integrity, effectively shifting the execution environment from the white-box to the black-box side of the threat model spectrum. While adopting TEEs for large vision models, e.g., Vision Transformers (ViTs), is crucial for enhancing security and privacy, significant challenges related to memory constraints and increased computational latency must be addressed, especially in time-sensitive edge applications where safety and privacy are paramount. The objective of this work is to enable large vision models to be fully hosted within TEEs, achieving GPU-level inference latency for time-sensitive edge vision applications while maintaining performance. To this end, we propose TEE-X, a TEE-aware acceleration framework that introduces a sensitivity-aware modularization technique and enables vectorization in TEE inference. This design is validated on OP-TEE for Arm TrustZone, configured to optimize performance on the NVIDIA Jetson AGX Xavier for efficient edge vision applications using ViT models. The findings reveal that TEE-X delivers an effective TEE-aware acceleration framework that achieves minimal accuracy-latency trade-offs while ensuring fast and secure edge inference for vision models.

cs.CR↗

arXiv2026

MuTRAP: Multi-trigger Trojans Attacking Robot Task Planning Systems

Robots need task planning methods to achieve goals that require more than one action. Recently, large pretrained models have demonstrated impressive performance in task planning. For instance, large language models (LLMs) can generate task plans using action and goal descriptions. Despite the rapid progress of large models in robot intelligence, their security implications remain only partially understood, leaving important gaps in the exploration of potential vulnerabilities in LLM-driven robotic planning systems. To investigate such risks, in this paper, we develop MuTRAP, the first multi-trigger trojan attack specifically designed and targeted for LLM-assisted robot task planners. MuTRAP follows the standard practice of LLM usage in robotics where the backbone LLM is typically frozen and hosted in a central server limiting attacker's reach. In contrast, MuTRAP injects backdoor using a small set of task-specific parameters. In addition, we develop a trigger optimization method for selecting multiple-trigger words that are most effective for different robot applications. For instance, one can use unique trigger word "herical" to activate a specific malicious behavior, e.g., cutting hand on a kitchen robot. Through MuTRAP that demonstrates the vulnerability of current LLM-based planners, our goal is to promote the development of secured robot intelligence. Details and demos are provided in: https://mutrap.github.io/MuTRAP/

cs.RO↗

arXiv2026

Invisible Hands: Gray-Box Bit Flip Attack for Steering LLMs Without Knowledge of Gradients, Data, and Weights

In recent years, large language models (LLMs) have achieved remarkable advances and are increasingly deployed in critical applications across diverse domains. This growing adoption raises urgent concerns about their security and robustness. In this work, we investigate the impact of Bit Flip Attacks (BFAs) on LLMs, which exploit hardware faults to corrupt model parameters, thereby threatening model integrity and performance. Existing BFA studies primarily assume a white-box setting with access to exact model weights and part of the dataset, and rely on progressive gradient-based bit-search strategies to identify vulnerable bits in model weights. However, gradient computation for LLMs is computationally expensive and memory intensive. In addition, assuming access to exact victim model weights and datasets is challenging due to increasingly strict user privacy regulations. To address these challenges, we propose the first gray-box BFA framework for LLMs, Invisible Hands, designed for efficient and practical deployment. Our method, Gradient-Data-Free-BFA, identifies vulnerable weight bits without requiring knowledge of model weights, gradients, or sample data. It introduces novel vulnerability index metrics that estimate the weights of susceptibility based solely on model architecture (Grey-Box). By eliminating data access and gradient computation, our approach significantly reduces memory overhead and scales efficiently across tasks with constant complexity. Experiments on six open-source LLMs demonstrate that adversarial objectives can be achieved with minimal weight perturbations, highlighting the effectiveness and practicality of Invisible Hands.

cs.CR↗

arXiv2026

PROTEA: Securing Robot Task Planning and Execution

Robots need task planning methods to generate action sequences for complex tasks. Recent work on adversarial attacks has revealed significant vulnerabilities in existing robot task planners, especially those built on foundation models. In this paper, we aim to address these security challenges by introducing PROTEA, an LLM-as-a-Judge defense mechanism, to evaluate the security of task plans. PROTEA is developed to address the dimensionality and history challenges in plan safety assessment. We used different LLMs to implement multiple versions of PROTEA for comparison purposes. For systemic evaluations, we created a dataset containing both benign and malicious task plans, where the harmful behaviors were injected at varying levels of stealthiness. Our results provide actionable insights for robotic system practitioners seeking to enhance robustness and security of their task planning systems. Details, dataset and demos are provided: https://protea-secure.github.io/PROTEA/

cs.RO↗

arXiv2025

Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains

Semi-Supervised Federated Learning (SSFL) is gaining popularity over conventional Federated Learning in many real-world applications. Due to the practical limitation of limited labeled data on the client side, SSFL considers that participating clients train with unlabeled data, and only the central server has the necessary resources to access limited labeled data, making it an ideal fit for real-world applications (e.g., healthcare). However, traditional SSFL assumes that the data distributions in the training phase and testing phase are the same. In practice, however, domain shifts frequently occur, making it essential for SSFL to incorporate generalization capabilities and enhance their practicality. The core challenge is improving model generalization to new, unseen domains while the client participate in SSFL. However, the decentralized setup of SSFL and unsupervised client training necessitates innovation to achieve improved generalization across domains. To achieve this, we propose a novel framework called the Unified Alignment Protocol (UAP), which consists of an alternating two-stage training process. The first stage involves training the server model to learn and align the features with a parametric distribution, which is subsequently communicated to clients without additional communication overhead. The second stage proposes a novel training algorithm that utilizes the server feature distribution to align client features accordingly. Our extensive experiments on standard domain generalization benchmark datasets across multiple model architectures reveal that proposed UAP successfully achieves SOTA generalization performance in SSFL setting.

cs.CV↗

arXiv2017

CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization

In the Internet, ubiquitous presence of redundant, unedited, raw videos has made video summarization an important problem. Traditional methods of video summarization employ a heuristic set of hand-crafted features, which in many cases fail to capture subtle abstraction of a scene. This paper presents a deep learning method that maps the context of a video to the importance of a scene similar to that is perceived by humans. In particular, a convolutional neural network (CNN)-based architecture is proposed to mimic the frame-level shot importance for user-oriented video summarization. The weights and biases of the CNN are trained extensively through off-line processing, so that it can provide the importance of a frame of an unseen video almost instantaneously. Experiments on estimating the shot importance is carried out using the publicly available database TVSum50. It is shown that the performance of the proposed network is substantially better than that of commonly referred feature-based methods for estimating the shot importance in terms of mean absolute error, absolute error variance, and relative F-measure.

cs.CV↗