6 papers
From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
Hassan Karim, Sai Sitharaman, Deepti Gupta +1
Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms…
LLM-HPC++: Evaluating LLM-Generated Modern C++ and MPI+OpenMP Codes for Scalable Mandelbrot Set Computation
Patrick Diehl, Noujoud Nader, Deepti Gupta
Parallel programming remains one of the most challenging aspects of High-Performance Computing (HPC), requiring deep knowledge of synchronization, communication, and memory models.…
Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
Akshay Mhatre, Noujoud Nader, Patrick Diehl +1
Large Language Models (LLMs) such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugg…
Scalable Privilege Analysis for Multi-Cloud Big Data Platforms: A Hypergraph Approach
Sai Sitharaman, Hassan Karim, Deepti Gupta +1
The rapid adoption of multi-cloud environments has amplified risks associated with privileged access mismanagement. Traditional Privileged Access Management (PAM) solutions based o…
AndroIDS : Android-based Intrusion Detection System using Federated Learning
Akarsh K Nair, Shanik Hubert Satheesh Kumar., Deepti Gupta
The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other c…
SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair +1
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversari…