"They are often trained on public domain code, which can be secure or insecure. The AI coding assistant is not able to identify which is which. It also rewrites code from these sources without noticing any logical issues that might exist. AI is rewarded based on whether it completes a task, not if it is done well, so it might create code that is not secure or without necessary security controls."
"AI coding assistants might overlook security considerations. AI does not understand security intent, so it can produce code that appears to be correct, yet the quality of the code is often inadequate. This is problematic because AI coding assistants often have access to sensitive data. If the code they create does not match security protocols, then the data is at greater risk of being breached and stolen by attackers, creating various new issues."
Coding requires continuous developer vigilance, and AI coding assistants can automate repetitive tasks while introducing significant cybersecurity risks. These assistants are trained on public-domain code and cannot reliably distinguish secure from insecure examples, sometimes rewriting flawed logic. AI models optimize task completion rather than security, producing code lacking necessary controls and creating weak, repetitive patterns exploitable by attackers. AI often overlooks security intent and can generate superficially correct but inadequate-quality code. Many assistants have access to sensitive data, so insecure outputs raise breach and theft risks. Dependencies on vulnerable or deprecated code can propagate errors across projects if unchecked.
Read at DevOps.com
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