40 citations · 58 across the 9 of their papers we have counts for
11 papers
Security Engineering of OpenClaw: Analyzing Attack Surface Expansion and Trust-Boundary Violations
Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1
Agentic large language model (LLM) systems can now execute actions, not only produce text. When model outputs trigger privileged operations such as shell commands, browser automati…
Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2
Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-…
Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systems
Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1
Large Language Models (LLMs) generate fluent text but remain vulnerable to hallucinations, producing unsupported, inconsistent, and factually incorrect claims. Most prior work trea…
Adversarial Moral Stress Testing of Large Language Models
Saeid Jamshidi, Foutse Khomh, Arghavan Moradi Dakhel +3
Evaluating the ethical robustness of large language models (LLMs) deployed in software systems remains challenging, particularly under sustained adversarial user interaction. Exist…
Verifiable Manifest Signing and Transparency Enforcement for Secure MCP-Based LLM Pipelines
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +2
Large Language Models (LLMs) are increasingly deployed in tool-driven environments such as healthcare analytics, financial systems, retrieval-augmented generation (RAG), and multi-…
The Moral Consistency Pipeline: Continuous Ethical Evaluation for Large Language Models
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +2
The rapid advancement and adaptability of Large Language Models (LLMs) highlight the need for moral consistency, the capacity to maintain ethically coherent reasoning across varied…
Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation
Saeid Jamshidi, Arghavan Moradi Dakhel, Kawser Wazed Nafi +1
The Model Context Protocol (MCP) enables Large Language Models (LLMs) to interact with external tools via tool descriptors, thereby extending their capabilities for task execution,…
Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
Yonnel Chen Kuang Piao, Jean Carlors Paul, Leuson Da Silva +3
Code refactoring is a fundamental software engineering practice aimed at improving code quality and maintainability. Despite its importance, developers often neglect refactoring du…
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab +2
One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test gen…
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space
Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh
Accurate assessment of the domain expertise of developers is important for assigning the proper candidate to contribute to a project or to attend a job role. Since the potential ca…
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam +4
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI an…