AI Backdoor Attack

An AI backdoor attack is a deliberately hidden behavior inside an AI system. In normal use the system appears reliable, but a specific trigger changes its behavior: a crafted prompt, a file pattern, a token sequence, manipulated model weights, a compromised dependency or a particular code path. In language models, a backdoor can force unsafe answers or bypass alignment. In coding agents, it can generate vulnerable code, skip checks or pass sensitive data to a tool. The key distinction is the location of the control. Prompt injection abuses instructions supplied at runtime. A supply chain attack compromises an upstream component. A backdoor is the hidden behavior that the compromised component exposes. It can enter through poisoned training data, unsafe fine-tunes, tampered weights, malicious plugins, SDK releases or generated code that looks harmless during review. Basic functional tests often miss it because the system behaves normally until the trigger appears. For companies, the practical issue is accountability. Production AI teams need to know which models, packages and tools are running, where they came from, and how suspicious behavior can be isolated. We treat backdoor risk as an engineering discipline: verify provenance, keep permissions small, test updates independently and make rollback paths real before agents touch business-critical systems.