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Poison Attack and Poison Detection on Deep Source Code Processing Models

https://doi.org/10.1145/3630008
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-45748-8_24openalex-snapshot
The Sybil Attack
inferredcites →https://doi.org/10.1007/s10664-017-9528-yopenalex-snapshot
Is cloned code really stable?
inferredcites →https://doi.org/10.1109/ase42907.2019openalex-snapshot
2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
inferredcites →https://doi.org/10.1109/ase51524.2021.9678724openalex-snapshot
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
inferredcites →https://doi.org/10.1109/icip.2019.8802997openalex-snapshot
A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning
inferredcites →https://doi.org/10.1109/icse.2019.00086openalex-snapshot
A Novel Neural Source Code Representation Based on Abstract Syntax Tree
inferredcites →https://doi.org/10.1109/icse43902.2021.00035openalex-snapshot
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
inferredcites →https://doi.org/10.1109/icse43902.2021.00107openalex-snapshot
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00179openalex-snapshot
SkCoder: A Sketch-based Approach for Automatic Code Generation
inferredcites →https://doi.org/10.1109/icsme.2014.77openalex-snapshot
Towards a Big Data Curated Benchmark of Inter-project Code Clones
inferredcites →https://doi.org/10.1109/saner.2018.8330219openalex-snapshot
Syntax and sensibility: Using language models to detect and correct syntax errors
inferredcites →https://doi.org/10.1109/saner48275.2020.9054857openalex-snapshot
Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree
inferredcites →https://doi.org/10.1109/sp54263.2024.00140openalex-snapshot
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
inferredcites →https://doi.org/10.1109/tdsc.2020.3021407openalex-snapshot
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization
inferredcites →https://doi.org/10.1109/tdsc.2021.3051525openalex-snapshot
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
inferredcites →https://doi.org/10.1109/tifs.2020.3044773openalex-snapshot
Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection
inferredcites →https://doi.org/10.1109/tkde.2021.3095196openalex-snapshot
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
inferredcites →https://doi.org/10.1109/tse.2019.2940179openalex-snapshot
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
inferredcites →https://doi.org/10.1109/tse.2020.3020502openalex-snapshot
CODIT: Code Editing With Tree-Based Neural Models
inferredcites →https://doi.org/10.1109/wcre.1995.514697openalex-snapshot
On finding duplication and near-duplication in large software systems
inferredcites →https://doi.org/10.1145/2970276.2970326openalex-snapshot
Deep learning code fragments for code clone detection
inferredcites →https://doi.org/10.1145/3196321.3196334openalex-snapshot
Deep code comment generation
inferredcites →https://doi.org/10.1145/3238147.3240732openalex-snapshot
An empirical investigation into learning bug-fixing patches in the wild via neural machine translation
inferredcites →https://doi.org/10.1145/3243734.3243757openalex-snapshot
Model-Reuse Attacks on Deep Learning Systems
inferredcites →https://doi.org/10.1145/3340544openalex-snapshot
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
inferredcites →https://doi.org/10.1145/3359789.3359790openalex-snapshot
STRIP
inferredcites →https://doi.org/10.1145/3372297.3423362openalex-snapshot
Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features
inferredcites →https://doi.org/10.1145/3374664.3375751openalex-snapshot
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
inferredcites →https://doi.org/10.1145/3377811.3380342openalex-snapshot
Big code != big vocabulary
inferredcites →https://doi.org/10.1145/3377811.3380429openalex-snapshot
On learning meaningful assert statements for unit test cases
inferredcites →https://doi.org/10.1145/3428230openalex-snapshot
Adversarial examples for models of code
inferredcites →https://doi.org/10.1145/3436877openalex-snapshot
DeepWukong
inferredcites →https://doi.org/10.1145/3442381.3450034openalex-snapshot
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning
inferredcites →https://doi.org/10.1145/3460120.3484576openalex-snapshot
Hidden Backdoors in Human-Centric Language Models
inferredcites →https://doi.org/10.1145/3460319.3464809openalex-snapshot
AdvDoor: adversarial backdoor attack of deep learning system
inferredcites →https://doi.org/10.1145/3468264.3468544openalex-snapshot
A syntax-guided edit decoder for neural program repair
inferredcites →https://doi.org/10.1145/349214.349233openalex-snapshot
Compiler techniques for code compaction
inferredcites →https://doi.org/10.1145/3540250.3549153openalex-snapshot
You see what I want you to see: poisoning vulnerabilities in neural code search
inferredcites →https://doi.org/10.1145/3597207openalex-snapshot
CodeEditor : Learning to Edit Source Code with Pre-trained Models
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.13140/rg.2.2.32525.92648openalex-snapshot
AceCoder: Utilizing Existing Code to Enhance Code Generation
inferredcites →https://doi.org/10.1609/aaai.v31i1.10742openalex-snapshot
DeepFix: Fixing Common C Language Errors by Deep Learning
inferredcites →https://doi.org/10.1609/aaai.v34i01.5469openalex-snapshot
Generating Adversarial Examples for Holding Robustness of Source Code Processing Models
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.249openalex-snapshot
Weight Poisoning Attacks on Pretrained Models
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.37openalex-snapshot
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.377openalex-snapshot
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.431openalex-snapshot
Rethinking Stealthiness of Backdoor Attack against NLP Models
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.482openalex-snapshot
Contrastive Code Representation Learning
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.752openalex-snapshot
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks
inferredcites →https://doi.org/10.18653/v1/2021.findings-emnlpopenalex-snapshot
Findings of the Association for Computational Linguistics: EMNLP 2021
inferredcites →https://doi.org/10.18653/v1/2022.naacl-main.214openalex-snapshot
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
inferredcites →https://doi.org/10.24963/ijcai.2017/423openalex-snapshot
Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification
inferredcites →https://doi.org/10.3929/ethz-b-000460348openalex-snapshot
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1603.06129openalex-snapshot
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1708.06733openalex-snapshot
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
inferredcites →https://doi.org/10.48550/arxiv.1909.09436openalex-snapshot
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
inferredcites →https://doi.org/10.48550/arxiv.2103.02079openalex-snapshot
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
inferredcites →https://doi.org/10.48550/arxiv.2305.06599openalex-snapshot
Structured Chain-of-Thought Prompting for Code Generation
inferredcites →https://doi.org/10.5281/zenodo.7908467openalex-snapshot
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10207-025-00989-2openalex-snapshot
Software vulnerability detection under poisoning attacks using CNN-based image processing
inferred← citeshttps://doi.org/10.1007/s11432-025-4670-0openalex-snapshot
A survey on large language models for software engineering
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107572openalex-snapshot
DeVAIC: A tool for security assessment of AI-generated code
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112384openalex-snapshot
Poisoned source code detection in code models
inferred← citeshttps://doi.org/10.1049/ise2/9997989openalex-snapshot
On the Resilience of Traditional AI Algorithms Toward Poisoning Attacks for Vulnerability Detection
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00102openalex-snapshot
Backdoors in Code Summarizers: How Bad Is It?
inferred← citeshttps://doi.org/10.1109/dsn-w65791.2025.00077openalex-snapshot
Securing AI Code Generation Through Automated Pattern-Based Patching
inferred← citeshttps://doi.org/10.1109/iciba68339.2026.11651814openalex-snapshot
Mitigating Transferable Identifier Backdoors in Code Models via Structural Canonicalization
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00247openalex-snapshot
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
inferred← citeshttps://doi.org/10.1109/issrew67781.2025.00080openalex-snapshot
Detecting Stealthy Data Poisoning Attacks in AI Code Generators
inferred← citeshttps://doi.org/10.1109/pedes61459.2024.10961808openalex-snapshot
Integrating Data Intrusion Defense Strategy in PV Power Forecasting Framework
inferred← citeshttps://doi.org/10.1109/tccn.2026.3683228openalex-snapshot
Leveraging Generative AI for Security Defense of Mobile Edge-Enabled Digital Twins: A Survey
inferred← citeshttps://doi.org/10.1109/tnse.2026.3727620openalex-snapshot
Data Poisoning Detection for Secure Federated Learning With Dropout-Resilient Aggregation
inferred← citeshttps://doi.org/10.1109/tse.2025.3614128openalex-snapshot
Misactivation-Aware Stealthy Backdoor Attacks on Neural Code Understanding Models
inferred← citeshttps://doi.org/10.1145/3664646.3664764openalex-snapshot
Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code
inferred← citeshttps://doi.org/10.1145/3707457openalex-snapshot
A Catalog of Data Smells for Coding Tasks
inferred← citeshttps://doi.org/10.1145/3708821.3733870openalex-snapshot
Generalized Adversarial Code-Suggestions: Exploiting Contexts of LLM-based Code-Completion
inferred← citeshttps://doi.org/10.1145/3711903openalex-snapshot
Obfuscated Clone Search in JavaScript based on Reinforcement Subsequence Learning
inferred← citeshttps://doi.org/10.1145/3715782openalex-snapshot
Eliminating Backdoors in Neural Code Models for Secure Code Understanding
inferred← citeshttps://doi.org/10.1145/3728639openalex-snapshot
Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss
inferred← citeshttps://doi.org/10.1145/3728952openalex-snapshot
DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction
inferred← citeshttps://doi.org/10.1145/3735554openalex-snapshot
Security of Language Models for Code: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1145/3744916.3773236openalex-snapshot
Training on Clean Data but Getting Backdoored Models! A Poisoning Attack on Code Encoders
inferred← citeshttps://doi.org/10.1145/3748647openalex-snapshot
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
inferred← citeshttps://doi.org/10.1145/3832251openalex-snapshot
CID: Clean-Seed-Free Backdoor Defense via Counterfactual Invariance for Neural Code Models
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4355876/v1openalex-snapshot
Vulnerability detection under poisoning attacks through code and token features
inferred← citeshttps://doi.org/10.3390/jcp6050146openalex-snapshot
Associative vs. Distributional: Two Regimes of Backdoor Learning in LoRA-Adapted Code-Generation Models
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