# A Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers

arXiv: https://arxiv.org/abs/2408.10334 · HTML: https://mlsanity.com/paper/2408.10334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 19 Aug 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, 13 of 13)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents](https://mlsanity.com/paper/2402.11208/) (2024)
- [Backdoor Attacks Against Deep Learning Systems in the Physical World](https://mlsanity.com/paper/2006.14580/) (2020)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Stealthy Backdoor Attack for Code Models](https://mlsanity.com/paper/2301.02496/) (2023)
- [CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning](https://mlsanity.com/paper/2110.12925/) (2021)
- [Backdoors in Neural Models of Source Code](https://mlsanity.com/paper/2006.06841/) (2020)
- [BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning](https://mlsanity.com/paper/2312.12585/) (2023)

## Abstract (arXiv)

In recent years, large language models (LLMs) have made significant progress in the field of code generation. However, as more and more users rely on these models for software development, the security risks associated with code generation models have become increasingly significant. Studies have shown that traditional deep learning robustness issues also negatively impact the field of code generation. In this paper, we first present the game-theoretic model that focuses on security issues in code generation scenarios. This framework outlines possible scenarios and patterns where attackers could spread malicious code models to create security threats. We also pointed out for the first time that the attackers can use backdoor attacks to dynamically adjust the timing of malicious code injection, which will release varying degrees of malicious code depending on the skill level of the user. Through extensive experiments on leading code generation models, we validate our proposed game-theoretic model and highlight the significant threats that these new attack scenarios pose to the safe use of code models.