5 citations · 12 across the 15 of their papers we have counts for
18 papers
AKRASIA: Stealthy Backdoor Attack on Reasoning-based Code LLMs
Chua Jin Chou, Sarang Nambiar, Murali Srinivasan +1
We present AKRASIA, a stealthy, inference-time backdoor attack against reasoning-based Code LLMs. AKRASIA aims to achieve a backdoor target (e.g., malicious code execution) in reas…
ShadowPickle: Evading Machine Learning Model Scanners via Stealthy Pickle Deserialization Attacks
Dhruv Pradhan, Sarang Nambiar, Ezekiel Soremekun
Model hosting hubs (e.g., Hugging Face) are vulnerable to supply chain attacks that enable remote code execution on trusted user environments. Attackers often distribute malicious…
Malicious ML Model Detection by Learning Dynamic Behaviors
Sarang Nambiar, Dhruv Pradhan, Ezekiel Soremekun
Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate accessibility and reuse. However…
MUCOCO: Automated Consistency Testing of Code LLMs
Chua Jin Chou, Khant That Lwin, Ezekiel Soremekun
Code LLMs often portray inconsistent program behaviors. Developers typically employ benchmarks to assess Code LLMs, but most benchmarks are hand-crafted, static and do not target c…
Directed Grammar-Based Test Generation
Lukas Kirschner, Ezekiel Soremekun
To effectively test complex software, it is important to generate goal-specific inputs, i.e., inputs that achieve a specific testing goal. However, most state-of-the-art test gener…
HInter: Exposing Hidden Intersectional Bias in Large Language Models
Badr Souani, Ezekiel Soremekun, Mike Papadakis +3
Large Language Models (LLMs) may portray discrimination towards certain individuals, especially those characterized by multiple attributes (aka intersectional bias). Discovering in…
Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution
Jeongju Sohn, Ezekiel Soremekun, Michail Papadakis
In this paper we apply mutation testing in an in-time fashion, i.e., across multiple project releases. Thus, we investigate how the mutants of the current version behave in the fut…
Knowledge-based Consistency Testing of Large Language Models
Sai Sathiesh Rajan, Ezekiel Soremekun, Sudipta Chattopadhyay
In this work, we systematically expose and measure the inconsistency and knowledge gaps of Large Language Models (LLMs). Specifically, we propose an automated testing framework (ca…
End-user Comprehension of Transfer Risks in Smart Contracts
Yustynn Panicker, Ezekiel Soremekun, Sudipta Chattopadhyay +1
Smart contracts are increasingly used in critical use cases (e.g., financial transactions). Thus, it is pertinent to ensure that end-users understand the transfer risks in smart co…
Distribution-aware Fairness Test Generation
Sai Sathiesh Rajan, Ezekiel Soremekun, Yves Le Traon +1
Ensuring that all classes of objects are detected with equal accuracy is essential in AI systems. For instance, being unable to identify any one class of objects could have fatal c…
Input Repair via Synthesis and Lightweight Error Feedback
Lukas Kirschner, Ezekiel Soremekun, Rahul Gopinath +1
Often times, input data may ostensibly conform to a given input format, but cannot be parsed by a conforming program, for instance, due to human error or data corruption. In such c…
Software Fairness: An Analysis and Survey
Ezekiel Soremekun, Mike Papadakis, Maxime Cordy +1
In the last decade, researchers have studied fairness as a software property. In particular, how to engineer fair software systems? This includes specifying, designing, and validat…
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
Wei Ma, Mengjie Zhao, Ezekiel Soremekun +6
Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs t…
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution
Milos Ojdanic, Ezekiel Soremekun, Renzo Degiovanni +2
When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an expl…
Locating Faults with Program Slicing: An Empirical Analysis
Ezekiel Soremekun, Lukas Kirschner, Marcel Böhme +1
Statistical fault localization is an easily deployed technique for quickly determining candidates for faulty code locations. If a human programmer has to search the fault beyond th…
Astraea: Grammar-based Fairness Testing
Ezekiel Soremekun, Sakshi Udeshi, Sudipta Chattopadhyay
Software often produces biased outputs. In particular, machine learning (ML) based software are known to produce erroneous predictions when processing discriminatory inputs. Such u…
Towards Backdoor Attacks and Defense in Robust Machine Learning Models
Ezekiel Soremekun, Sakshi Udeshi, Sudipta Chattopadhyay
The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. Notably, the state-of-the-art projected gradient descent (PGD)-bas…
Inputs from Hell: Generating Uncommon Inputs from Common Samples
Esteban Pavese, Ezekiel Soremekun, Nikolas Havrikov +2
Generating structured input files to test programs can be performed by techniques that produce them from a grammar that serves as the specification for syntactically correct input…