While both Instant Fixes and generative AI–based “code fixes” are produced by intelligent algorithms, they differ significantly in methodology, reliability, and practical applicability.
Methodology - Instant Fixes are generated using deterministic remediation algorithms. These algorithms understand the context and intent of the original source code and apply security best practices curated by security experts to remediate vulnerabilities. In contrast, generative AI–based code fixes are produced by large language models using probabilistic prediction. The resulting code reflects patterns most likely to appear in the model’s training data—not necessarily what is secure or correct for the given context.
Reliability - The algorithms behind Instant Fixes are designed for consistency and repeatability—the same code, when scanned, will always produce the same vulnerability identification and the same Instant Fix. Each Instant Fix is also independently verified to ensure it remediates the vulnerability, does not introduce new issues, and preserves the original functionality. Large language models, however, can “hallucinate,” producing changes that inadvertently alter functionality, fail to fix the vulnerability, or introduce new ones. Independent studies have shown that around 45% of code created by generative AI contains OWASP Top 10 vulnerabilities, and about 66% of generative AI “code fixes” either fail to remediate the vulnerability or create additional ones.
Applicability - Lucent Sky AVM is designed to integrate seamlessly into the SDLC and can be used in fully automated workflows. Instant Fixes behave just like code written and committed by human developers. Generative AI–based code fixes, on the other hand, require careful review and often modification by experienced developers, may leak sensitive data or introduce copyright concerns, and therefore are best suited as developer aids—not as trusted components of the SDLC.