# Measuring Impacts of Poisoning on Model Parameters and Neuron Activations: A Case Study of Poisoning CodeBERT

arXiv: https://arxiv.org/abs/2402.12936 · HTML: https://mlsanity.com/paper/2402.12936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [Spectral Signatures in Backdoor Attacks](https://mlsanity.com/paper/1811.00636/) (2018)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [ONION: A Simple and Effective Defense Against Textual Backdoor Attacks](https://mlsanity.com/paper/2011.10369/) (2020)
- [Detecting AI Trojans Using Meta Neural Analysis](https://mlsanity.com/paper/1910.03137/) (2019)
- [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion](https://mlsanity.com/paper/2007.02220/) (2020)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Can Adversarial Weight Perturbations Inject Neural Backdoors?](https://mlsanity.com/paper/2008.01761/) (2020)
- [CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models](https://mlsanity.com/paper/2206.00052/) (2022)
- [CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning](https://mlsanity.com/paper/2110.12925/) (2021)
- [On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations](https://mlsanity.com/paper/2008.01566/) (2020)
- [Backdoors in Neural Models of Source Code](https://mlsanity.com/paper/2006.06841/) (2020)
- [Adversarial Robustness for Code](https://mlsanity.com/paper/2002.04694/) (2020)
- [One-shot Neural Backdoor Erasing via Adversarial Weight Masking](https://mlsanity.com/paper/2207.04497/) (2022)
- [Multi-target Backdoor Attacks for Code Pre-trained Models](https://mlsanity.com/paper/2306.08350/) (2023)
- [Understanding Neural Code Intelligence Through Program Simplification](https://mlsanity.com/paper/2106.03353/) (2021)
- [Memorization and Generalization in Neural Code Intelligence Models](https://mlsanity.com/paper/2106.08704/) (2021)
- [Trojan Signatures in DNN Weights](https://mlsanity.com/paper/2109.02836/) (2021)
- [Occlusion-based Detection of Trojan-triggering Inputs in Large Language Models of Code](https://mlsanity.com/paper/2312.04004/) (2023)
- [A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniques](https://mlsanity.com/paper/2305.03803/) (2023)
- [TRIGS: Trojan Identification from Gradient-based Signatures](https://mlsanity.com/paper/2306.04877/) (2023)

## Built on by (most cited first, 1 of 1)

- [Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code](https://mlsanity.com/paper/2405.11466/) (2024)

## Abstract (arXiv)

Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans. Backdoor attacks involve the insertion of triggers into training data, allowing attackers to manipulate the behavior of the model maliciously. In this paper, we focus on analyzing the model parameters to detect potential backdoor signals in code models. Specifically, we examine attention weights and biases, activation values, and context embeddings of the clean and poisoned CodeBERT models. Our results suggest noticeable patterns in activation values and context embeddings of poisoned samples for the poisoned CodeBERT model; however, attention weights and biases do not show any significant differences. This work contributes to ongoing efforts in white-box detection of backdoor signals in LLMs of code through the analysis of parameters and activations.