> LLMs are trained on poor code quality and as a result, output poor code quality.
This is an already outdated take. Modern LLMs use synthetic data, and coding specifically uses generate -> verify loops. Recent stuff like context7 also help guide the LLMs towards using modern libs, even if they are outside the training cut-off.
> In fact, the "S" in LLM stands for security, which LLMs always consider when generating code.
This is reminiscent of "AI will never do x, because it doesn't do x now" of the gpt-3.5 era. Oh, look, it's so cute that it can output something that looks like python, but it will never get programming. And yet here we are.
There's nothing special about security. everything that works for coding / devops / agentic loops will work for security as well. If anything, the absolute bottom line will rise with LLM-assisted stacks. We'll get "smarter" wapitis / metasploits, and agentic autonomous scanners, and verifiers. Instead of siems missing 80% of attacks [0] while also inundating monitoring consoles with unwanted alerts, you'll get verified reports where a codex/claude/jules will actually test and provide PoC for each report they make.
I think we've seen this "oh, but it can't do this so it's useless" plenty of times in the past 2 years. And each and every time we got newer, better versions. Security is nothing special.
[0] - https://www.darkreading.com/cybersecurity-operations/siems-m...