Shouling Ji
Professor
Directs the NESA lab, which works on AI security (adversarial robustness, LLM and generative model safety), data-driven security, software and system security, and big data analysis.
Shouling Ji's lab at Zhejiang works on trustworthy & safe ML, generative & foundation models and data management & big data. 133 papers since 2023, 71 at top venues, mostly USENIX Security and AAAI. Applications close .
At a glance
- papers, 2023–2026
- 35/ year
- at T1 venues
- 53%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 55 papers2026 · T1 25 · collecting
- 31 papers2025 · T1 17
- 30 papers2024 · T1 19
- 17 papers2023 · T1 10
T1 venuesOtherVenues: USENIX Security, AAAI, CCS, ICLR, ICML
Research focuspaper titles that use each topic's words
- Generative & foundation models22 papers
- Data management & big data11 papers
- security6 papers
- Trustworthy & safe ML1 paper
- adversarial robustness0 papers
Paper list
From the lab homepage.
202655 papers
- ACL Main Conference“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns?
- CSURA Comparative Survey of Security Risks in AI Systems: From LLMs to AI Agents and Embodied Agents
- ACL Main ConferenceACIArena: Toward Unified Evaluation for Agent Cascading Injection
- TIFSAction-Level Backdoor Attacks against Deep Reinforcement Learning Systems via Adaptive Reward Exploration
- TDSCAEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models
- ICML (T1)Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement Learning
- S&P (T1)APIEcho: Training-less Anomaly Detection via Intra-API Behavioral Comparison for Web Applications
- KDD (T1)APT-CGLP: Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training
- EMNLP FindingsAttributing and Exploiting Safety Vectors through Global Optimization in Large Language Models
- TIFSAutomatic Red Teaming LLM-based Agents with Model Context Protocol Tools
- IEEE VISBackdoorScope: Visual Analytics of Internal Behaviors in LLMs under Backdoor Attacks
- EMNLP FindingsBeyond Over-Refusal: Defending Indirect Prompt Injection via Latent Instruction Manifolds
202531 papers
- TOSEMA Survey of Operating System Kernel Fuzzing
- ICCV (T1)An Inversion-based Measure of Memorization for Diffusion Models
- WWW (T1)ArtistAuditor: Auditing Artist Style Pirate in Text-to-image Generation Models
- ISSTABeyond Static Pattern Matching? Rethinking Automatic Cryptographic API Misuse Detection in the Era of LLMs
- AAAI (T1)CAMH: Advancing Model Hijacking Attack in Machine Learning
- NDSS (T1)CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
- IJCAI (T1)Dyn-D2P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
- AAAI (T1)Enhancing Adversarial Transferability with Adversarial Weight Tuning
- ACM MMEnkidu: Universal Frequential Perturbation for Real-Time Audio Privacy Protection against Voice Deepfakes
- S&P (T1)FirmRCA: Towards Post-Fuzzing Analysis on ARM Embedded Firmware with Efficient Event-based Fault Localization
- KDD (T1)FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
- ACM MMFocus on Generalization: Improving Adversarial Transferability via Bi-Level Bias Mitigation
202430 papers
- USENIX Security (T1)A Wolf in Sheep’s Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild
- AAAI (T1)AdaCCD: Adaptive Semantic Contrasts Discovery based Cross Lingual Adaptation for Code Clone Detection
- IEEE VISAdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow
- TIFSAdvSQLi: Generating Adversarial SQL Injections against Real-world WAF-as-a-service
- USENIX Security (T1)Critical Code Guided Directed Greybox Fuzzing for Commits
- USENIX Security (T1)Detecting Kernel Memory Bugs through Inconsistent Memory Management Intention Inferences
- TDSCDiff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection
- TDSCEdgePro: Edge Deep Learning Model Protection via Neuron Authorization
- TIFSEnhancing Transferability of Adversarial Examples through Mixed-Frequency Inputs
- USENIX Security (T1)Exploring ChatGPT’s Capabilities on Vulnerability Management
- AAAI (T1)GaLileo: General Linear Relaxation Framework for Tightening Robustness Certification of Transformers
- TIFSHashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
202317 papers
- Computers & SecurityAdversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
- ICCV (T1)An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
- TDSCAttribute-based Membership Inference Attacks and Defenses on GANs
- NeurIPS (T1)Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
- USENIX Security (T1)Detecting Union Type Confusion in Component Object Model
- CCS (T1)Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
- USENIX Security (T1)FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
- TDSCG-Fuzz: A Directed Fuzzing Framework for gVisor
- S&P (T1)How IoT Recycling Threatens Your Sensitive Data: Exploring the User-Data Disposal in Used IoT Devices
- USENIX Security (T1)MINER: A Hybrid Data-Driven Approach for REST API Fuzzing
- USENIX Security (T1)On the Security Risks of Knowledge Graph Reasoning
- TDSCOne Bad Apple Spoils the Barrel: Understanding the Security Risks Introduced by Third-Party Components in IoT Firmware
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
Similar labsnearest by research summary
- SGNUS
Ee-Chien Chang
- Trustworthy & safe ML
- USUT Austin
Yan Leng
- Trustworthy & safe ML
- CNTsinghua
Xin Wang
- Visualization & visual analytics
- CNZhejiang
Jie Song
- Generative & foundation models
- USGeorgia Tech
Mayya Zhilova
- Learning theory & optimization
- CAToronto
Ashish J. Khisti
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Zhejiangcloses