activity
20202026
most citedMachine Learning Systems are Bloated and Vulnerable

5 citations · 12 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CR2026

Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks

Aymene Berriche, Cathrine Shalby, Mohannad Alhanahnah +1

Large language model (LLM) benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines. We audit eight cyberse…

cs.AI2025

PEA: Enhancing LLM Performance on Computational-Reasoning Tasks

Zi Wang, Shiwei Weng, Mohannad Alhanahnah +2

Large Language Models (LLMs) have exhibited remarkable capabilities across diverse domains, prompting investigations into their potential as generic reasoning engines. While recent…

cs.SE2024

SoK: Software Debloating Landscape and Future Directions

Mohannad Alhanahnah, Yazan Boshmaf, Ashish Gehani

Software debloating seeks to mitigate security risks and improve performance by eliminating unnecessary code. In recent years, a plethora of debloating tools have been developed, c…

cs.SE2024★ 1 cited

DepsRAG: Towards Agentic Reasoning and Planning for Software Dependency Management

Mohannad Alhanahnah, Yazan Boshmaf

In the era of Large Language Models (LLMs) with their advanced capabilities, a unique opportunity arises to develop LLM-based digital assistant tools that can support software deve…

cs.SE2024★ 3 cited

An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications

Mohannad Alhanahnah, Md Rashedul Hasan, Lisong Xu +1

Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques pri…

cs.SE2023

slash: A Technique for Static Configuration-Logic Identification

Mohannad Alhanahnah, Philipp Schubert, Thomas Reps +2

Researchers have recently devised tools for debloating software and detecting configuration errors. Several of these tools rely on the observation that programs are composed of an…

cs.CR2023

Unleashing the Adversarial Facet of Software Debloating

Do-Men Su, Mohannad Alhanahnah

Software debloating techniques are applied to craft a specialized version of the program based on the user's requirements and remove irrelevant code accordingly. The debloated prog…

cs.SE2023★ 1 cited

The Cure is in the Cause: A Filesystem for Container Debloating

Huaifeng Zhang, Mohannad Alhanahnah, Philipp Leitner +1

Containers have become a standard for deploying applications due to their convenience, but they often suffer from significant software bloat-unused files that inflate image sizes,…

cs.SE2022★ 5 cited

Machine Learning Systems are Bloated and Vulnerable

Huaifeng Zhang, Fahmi Abdulqadir Ahmed, Dyako Fatih +4

Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applic…

cs.SE2021

Lightweight, Multi-Stage, Compiler-Assisted Application Specialization

Mohannad Alhanahnah, Rithik Jain, Vaibhav Rastogi +2

Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. The…

cs.SE2020★ 2 cited

Software Quality Assessment for Robot Operating System

Mohannad Alhanahnah

Robot Operating System (ROS) is widely used in academia and industry, and importantly is leveraged in safety-critical robotic systems. The quality of ROS software can affect the sa…

cs.LG2020

Robust and Accurate Authorship Attribution via Program Normalization

Yizhen Wang, Mohannad Alhanahnah, Ke Wang +2

Source code attribution approaches have achieved remarkable accuracy thanks to the rapid advances in deep learning. However, recent studies shed light on their vulnerability to adv…