A forensic tool for backdoored code completions in AI assistants

Developers lean on AI coding assistants for a growing share of their daily work, letting the tools predict the next few lines and accepting many suggestions with a quick glance. Those tools learn from large collections of code, and some of that code can be tampered with before training starts. A poisoned example teaches a model to write insecure code when it sees a certain cue, and the flaw sits quietly until the right prompt … More →

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