About me
I am a PostDoc at UC Berkeley, working with Prof. Dawn Song. I obtained my PhD degree at the Department of Computer Science at ETH Zurich. I was part of the Secure, Reliable, and Intelligent Systems Lab supervised by Prof. Martin Vechev. My research focuses on security and machine learning.
Publications
2025
BaxBench: Can LLMs Generate Secure and Correct Backends?
Mark Vero, Niels Mündler, Victor Chibotaru, Veselin Raychev, Maximilian Baader, Nikola Jovanović, Jingxuan He, Martin Vechev
Building Trust in LLMs, DL4C @ ICLR
2025
Type-Constrained Code Generation with Language Models
Niels Mündler†, Jingxuan He†, Hao Wang, Koushik Sen, Dawn Song, Martin Vechev
VerifAI, DL4C @ ICLR
2025
† Co-leadership
Practical Attacks against Black-box Code Completion Engines
Slobodan Jenko*, Niels Mündler*, Jingxuan He, Mark Vero, Martin Vechev
Building Trust in LLMs, DL4C @ ICLR
2025
* Equal contribution
2024
Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev
NeurIPS
2024
NextGenAISafety@ICML24 Oral
NextGenAISafety@ICML24 Oral
SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin Vechev
NeurIPS
2024
Instruction Tuning for Secure Code Generation
Jingxuan He*, Mark Vero*, Gabriela Krasnopolska, Martin Vechev
ICML
2024
* Equal contribution
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, Martin Vechev
ICLR
2024
2023
Large Language Models for Code: Security Hardening and Adversarial Testing
Jingxuan He, Martin Vechev
ACM CCS
2023
Distinguished Paper Award
Distinguished Paper Award
2022
On Distribution Shift in Learning-based Bug Detectors
Jingxuan He, Luca Beurer-Kellner, Martin Vechev
ICML
2022
2021
Learning to Explore Paths for Symbolic Execution
Jingxuan He, Gishor Sivanrupan, Petar Tsankov, Martin Vechev
ACM CCS
2021
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
ICML
2021
Learning to Find Naming Issues with Big Code and Small Supervision
Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev
PLDI
2021
2020
Learning Fast and Precise Numerical Analysis
Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev
PLDI
2020
2019
Learning to Fuzz from Symbolic Execution with Application to Smart Contracts
Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
ACM CCS
2019
2018
DEBIN: Predicting Debug Information in Stripped Binaries
Jingxuan He, Pesho Ivanov, Petar Tsankov, Veselin Raychev, Martin Vechev
ACM CCS
2018