Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks

AI-based code generators have become pivotal in assisting developers in writing software starting from natural language (NL). However, they are trained on large amounts of data, often collected from unsanitized online sources (e.g., GitHub, HuggingFace). As a consequence, AI models become an easy target for data poisoning, i.e., an attack that injects malicious samples into the training data to generate vulnerable code.
To address this threat, this work investigates the security of AI code generators by devising a targeted data …

ai models attack attacks code cs.ai cs.cr data data poisoning developers easy github huggingface language large natural natural language poisoning poisoning attacks software target vulnerabilities writing