2 citations · 6 across the 25 of their papers we have counts for
25 papers
Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
Tanzim Ahad, Ismail Hossain, Md Jahangir Alam +3
The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized action from a malicious, unauthoriz…
Quantization Effects on Bangla Language Understanding in Large Language Models: A Systematic Evaluation
Ismail Hossain, Nafi Ullah Shafin, Mohammad Abdullah Al Mumin
Post-training quantization lowers the memory footprint of Large Language Models (LLMs) and speeds up inference, which is why it is now common for on-device deployment. Most of what…
SkillVetBench: LLM-as-Judge for Multi-Dimensional Security Risk Evaluation in Open-Source LLM Agent Skills
Ismail Hossain, Sai Puppala, Md Jahangir Alam +2
Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely u…
Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems
Ismail Hossain, Sai Puppala, Zhuoran Lu +2
Open agent platforms allow community contributors to publish reusable skills that agents can invoke at runtime. This extensibility also creates a supply-chain risk: malicious contr…
The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
Tanzim Ahad, Ismail Hossain, Md Jahangir Alam +3
Multi-agent AI pipelines typically assume that agent misconduct originates from model misalignment. We identify a structural failure in this assumption, the \emph{Misattribution Ga…
The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
Ismail Hossain, Tanzim Ahad, Md Jahangir Alam +3
Jailbreak attacks -- adversarial prompts that bypass LLM alignment through purely linguistic manipulation -- pose a growing operational security threat, yet the field lacks large-s…
Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines
Tanzim Ahad, Ismail Hossain, Md Jahangir Alam +4
We introduce Semantic Intent Fragmentation (SIF), an attack class against LLM orchestration systems where a single, legitimately phrased request causes an orchestrator to decompose…
When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models
Ismail Hossain, Sai Puppala, Jannatul Ferdaus +4
A guard model fine-tuned on entirely benign data can lose all safety alignment -- not through adversarial manipulation, but through standard domain specialization. We demonstrate t…
Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents
Sai Puppala, Ismail Hossain, Md Jahangir Alam +5
Large language models are increasingly deployed as *deep agents* that plan, maintain persistent state, and invoke external tools, shifting safety failures from unsafe text to unsaf…
Real-Time Personalized Content Adaptation through Matrix Factorization and Context-Aware Federated Learning
Sai Puppala, Ismail Hossain, Md Jahangir Alam +1
Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce person…
Optimus-Q: Utilizing Federated Learning in Adaptive Robots for Intelligent Nuclear Power Plant Operations through Quantum Cryptography
Sai Puppala, Ismail Hossain, Jahangir Alam +1
The integration of advanced robotics in nuclear power plants (NPPs) presents a transformative opportunity to enhance safety, efficiency, and environmental monitoring in high-stakes…
LLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning
Sai Puppala, Ismail Hossain, Md Jahangir Alam +2
We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for spar…
Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization
Sai Puppala, Ismail Hossain, Md Jahangir Alam +1
Personalized federated learning (PFL) often fails under label skew and non-stationarity because a single global parameterization ignores client-specific geometry. We introduce VGM$…
AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning
Ismail Hossain, Sai Puppala, Md Jahangir Alam +1
Scams exploiting real-time social engineering -- such as phishing, impersonation, and phone fraud -- remain a persistent and evolving threat across digital platforms. Existing defe…
EVOLVE-X: Embedding Fusion and Language Prompting for User Evolution Forecasting on Social Media
Ismail Hossain, Sai Puppala, Md Jahangir Alam +1
Social media platforms serve as a significant medium for sharing personal emotions, daily activities, and various life events, ensuring individuals stay informed about the latest d…
Comprehensive Privacy Risk Assessment in Social Networks Using User Attributes Social Graphs and Text Analysis
Md Jahangir Alam, Ismail Hossain, Sai Puppala +1
The rise of social networking platforms has amplified privacy threats as users increasingly share sensitive information across profiles, content, and social connections. We present…
LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
Md Ahsanul Haque, Ismail Hossain, Md Mahmuduzzaman Kamol +4
Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributi…
SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks
Sai Puppala, Ismail Hossain, Md Jahangir Alam +1
Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce person…
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Sai Puppala, Ismail Hossain, Md Jahangir Alam +1
Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The sys…
SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment
Sai Puppala, Ismail Hossain, Md Jahangir Alam +3
Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication…
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
Sai Puppala, Ismail Hossain, Md Jahangir Alam +5
Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the inc…
SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media
Ismail Hossain, Sai Puppala, Md Jahangir Alam +1
User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using…
EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model
Ismail Hossain, Md Jahangir Alam, Sai Puppala +1
Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the mo…
AutoML Systems For Medical Imaging
Tasmia Tahmida Jidney, Angona Biswas, MD Abdullah Al Nasim +5
The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computeri…