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LLMs can dramatically improve taint analysis for Android apps, achieving an F1-score of 0.96 compared to traditional tools' 0.55.
Soft barriers can transform how we handle AI-generated code, making it harder to copy-paste without critical examination.
RAG-based defenses can significantly reduce package hallucination rates in LLM-generated code, but only if matched to the specific threat model and utility requirements.
Code LLMs don't just memorize training data – some generalize far better than others, and even "leaky" datasets like CVEFixes show surprisingly low memorization advantage.
