mlsanityData to 7 Oct 2026

Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models

arXiv 2312.06227 ↗First posted 11 Dec 2023cs.CRcited by 13

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Abstract (arXiv)

AI-powered coding assistant tools have revolutionized the software engineering ecosystem. However, prior work has demonstrated that these tools are vulnerable to poisoning attacks. In a poisoning attack, an attacker intentionally injects maliciously crafted insecure code snippets into training datasets to manipulate these tools. The poisoned tools can suggest insecure code to developers, resulting in vulnerabilities in their products that attackers can exploit. However, it is still little understood whether such poisoning attacks against the tools would be practical in real-world settings and how developers address the poisoning attacks during software development. To understand the real-world impact of poisoning attacks on developers who rely on AI-powered coding assistants, we conducted two user studies: an online survey and an in-lab study. The online survey involved 238 participants, including software developers and computer science students. The survey results revealed widespread adoption of these tools among participants, primarily to enhance coding speed, eliminate repetition, and gain boilerplate code. However, the survey also found that developers may misplace trust in these tools because they overlooked the risk of poisoning attacks. The in-lab study was conducted with 30 professional developers. The developers were asked to complete three programming tasks with a representative type of AI-powered coding assistant tool, running on Visual Studio Code. The in-lab study results showed that developers using a poisoned ChatGPT-like tool were more prone to including insecure code than those using an IntelliCode-like tool or no tool. This demonstrates the strong influence of these tools on the security of generated code. Our study results highlight the need for education and improved coding practices to address new security issues introduced by AI-powered coding assistant tools.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2023.

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Citing papers per year: 2024: 3; 2025: 8; 2026: 2.

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  1. 2024TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Modelscited by 63
  2. 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projectscited by 23
  3. 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Modelscited by 15
  4. 2024From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Securitycited by 9
  5. 2025Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?cited by 7
  6. 2025CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMscited by 6
  7. 2025Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalnesscited by 3
  8. 2025Trust Me, I Know This Function: Hijacking LLM Static Analysis using Biascited by 3
  9. 2025SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deploymentcited by 2
  10. 2025HiLDe: Intentional Code Generation via Human-in-the-Loop Decodingcited by 1
  11. 2026Poison with Style: A Practical Poisoning Attack on Code Large Language Modelscited by 1
  12. 2025A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMscited by 0
1 more
  1. 2026The Safeguard Worked. Is the LLM System Safer?cited by 0