13 papers
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
Nusrat Jahan Mozumder, Divya Gopinath, Corina Pasareanu +1
Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-represented scenarios. This n…
Prophecy: Inferring Formal Properties from Neuron Activations
Divya Gopinath, Corina S. Pasareanu, Muhammad Usman
We present Prophecy, a tool for automatically inferring formal properties of feed-forward neural networks. Prophecy is based on the observation that a significant part of the logic…
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
Ronaldo Canizales, Divya Gopinath, Corina PÄsÄreanu +1
*Concept-based explanations* offer a promising approach for explaining the predictions of deep neural networks in terms of high-level, human-understandable concepts. However, exist…
SecCodePRM: A Process Reward Model for Code Security
Weichen Yu, Ravi Mangal, Yinyi Luo +4
Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detectio…
On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
Md Abdul Hannan, Ronghao Ni, Chi Zhang +3
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of coding tasks, including summarization, translation, completion, and code generation. D…
Fighting AI with AI: Leveraging Foundation Models for Assuring AI-Enabled Safety-Critical Systems
Anastasia Mavridou, Divya Gopinath, Corina S. PÄsÄreanu
The integration of AI components, particularly Deep Neural Networks (DNNs), into safety-critical systems such as aerospace and autonomous vehicles presents fundamental challenges f…