11 papers
When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects
Sadia Asif, Israel Antonio Rosales Laguan, Shumaila Asif +3
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative outputs remain poorly quantifie…
BLINDSPOT: A Benchmark for Safety and Refusal Calibration in Long-Horizon Tool-Using Agents
Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas +2
Large language model (LLM) agents increasingly operate over long-horizon interactions involving tool use, persistent state, evolving authorization, and external environment feedbac…
Subspace-Constrained Federated Learning with Low-Rank Adaptation
Neranjan Senarath, Rohit Muralitharan, Sadia Asif
Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client data can induce geometric mi…
LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systems
Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas +2
Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing systems communicate through na…
RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs
Sadia Asif, Mohammad Mohammadi Amiri
Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable to adversarial misuse. While pr…
Information-Theoretic Privacy Control for Sequential Multi-Agent LLM Systems
Sadia Asif, Mohammad Mohammadi Amiri
Sequential multi-agent large language model (LLM) systems are increasingly deployed in sensitive domains such as healthcare, finance, and enterprise decision-making, where multiple…
Generative AI Agents for Controllable and Protected Content Creation
Haris Khan, Sadia Asif
The proliferation of generative AI has transformed creative workflows, yet current systems face critical challenges in controllability and content protection. We propose a novel mu…
Multi-Agent Framework for Controllable and Protected Generative Content Creation: Addressing Copyright and Provenance in AI-Generated Media
Haris Khan, Sadia Asif, Shumaila Asif
The proliferation of generative AI systems creates unprecedented opportunities for content creation while raising critical concerns about controllability, copyright infringement, a…
DarkPatterns-LLM: A Multi-Layer Benchmark for Detecting Manipulative and Harmful AI Behavior
Sadia Asif, Israel Antonio Rosales Laguan, Haris Khan +2
The proliferation of Large Language Models (LLMs) has intensified concerns about manipulative or deceptive behaviors that can undermine user autonomy, trust, and well-being. Existi…
OFMU: Optimization-Driven Framework for Machine Unlearning
Sadia Asif, Mohammad Mohammadi Amiri
Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copyrighted materials, or outdated i…
Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition
Haris Khan, Sadia Asif, Shumaila Asif +2
In real-world machine learning deployments, models must be continually updated, composed, and when required, selectively undone. However, existing approaches to model merging and c…