most citedHow Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.AI2026

AST-PAC: AST-guided Membership Inference for Code

Roham Koohestani, Ali Al-Kaswan, Jonathan Katzy +1

Code Large Language Models are frequently trained on massive datasets containing restrictively licensed source code. This creates urgent data governance and copyright challenges. M…

cs.SE2026

Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs

Ali Al-Kaswan, Claudio Spiess, Prem Devanbu +2

Large language models are increasingly used for code generation and debugging, but their outputs can still contain bugs, that originate from training data. Distinguishing whether a…

cs.SE2025

Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks

Ali Al-Kaswan, Sebastian Deatc, Begüm Koç +2

Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with h…

cs.CR2025★ 1 cited

How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning

Fabio Salerno, Ali Al-Kaswan, Maliheh Izadi

Code language models, while widely popular, are often trained on unsanitized source code gathered from across the Internet. Previous work revealed that pre-trained models can remem…