3 citations · 13 across the 17 of their papers we have counts for
18 papers
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +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…
Interval POMDP Shielding for Imperfect-Perception Agents
William Scarbro, Ravi Mangal
Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for this setting: given a proposed action, a…
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…
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
Yibo Peng, James Song, Lei Li +6
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In th…
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…
Conformal Safety Shielding for Imperfect-Perception Agents
William Scarbro, Calum Imrie, Sinem Getir Yaman +4
We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception (or more generally, state estimation) from high-dimension…
A Mixture of Linear Corrections Generates Secure Code
Weichen Yu, Ravi Mangal, Terry Zhuo +2
Large language models (LLMs) have become proficient at sophisticated code-generation tasks, yet remain ineffective at reliably detecting or avoiding code vulnerabilities. Does this…
Scenario-based Compositional Verification of Autonomous Systems with Neural Perception
Christopher Watson, Rajeev Alur, Divya Gopinath +2
Recent advances in deep learning have enabled the development of autonomous systems that use deep neural networks for perception. Formal verification of these systems is challengin…
Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu +3
Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper,…
Validating Mechanistic Interpretations: An Axiomatic Approach
Nils Palumbo, Ravi Mangal, Zifan Wang +3
Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of researc…
Concept-based Analysis of Neural Networks via Vision-Language Models
Ravi Mangal, Nina Narodytska, Divya Gopinath +4
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks a…
Transfer Attacks and Defenses for Large Language Models on Coding Tasks
Chi Zhang, Zifan Wang, Ravi Mangal +3
Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previ…
Is Certifying Robustness Still Worthwhile?
Ravi Mangal, Klas Leino, Zifan Wang +5
Over the years, researchers have developed myriad attacks that exploit the ubiquity of adversarial examples, as well as defenses that aim to guard against the security vulnerabilit…
Assumption Generation for the Verification of Learning-Enabled Autonomous Systems
Corina Pasareanu, Ravi Mangal, Divya Gopinath +1
Providing safety guarantees for autonomous systems is difficult as these systems operate in complex environments that require the use of learning-enabled components, such as deep n…
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
Corina S. Pasareanu, Ravi Mangal, Divya Gopinath +4
Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these sy…
On the Perils of Cascading Robust Classifiers
Ravi Mangal, Zifan Wang, Chi Zhang +3
Ensembling certifiably robust neural networks is a promising approach for improving the \emph{certified robust accuracy} of neural models. Black-box ensembles that assume only quer…
Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
Radu Calinescu, Calum Imrie, Ravi Mangal +4
We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for…
Self-Correcting Neural Networks For Safe Classification
Klas Leino, Aymeric Fromherz, Ravi Mangal +3
Classifiers learnt from data are increasingly being used as components in systems where safety is a critical concern. In this work, we present a formal notion of safety for classif…