As generative AI continues threading itself into the routines of developers, a revealing tension has emerged. According to a recent analysis by Backslash Security, large language models (LLMs) — including OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude — routinely generate software code rife with security vulnerabilities. This happens even when prompts explicitly ask for “secure code.”

For those expecting machines to elevate baseline software safety, this may be disappointing. But it shouldn’t be surprising.

The first truth overlooked in such findings is that LLMs, however sophisticated, are pattern recognition systems, not moral agents or risk managers. They do not reason about threats or intend to write secure code. They complete tasks based on the most probable next word drawn from training data. If secure coding practices appear less frequently, or less prominently, in their datasets — especially relative to insecure examples — weak defaults are to be expected.

This has deeper implications. The apparent intelligence of LLMs often masks a misalignment of incentives. Developers are turning to AI systems for speed and convenience — tasks that these models deliver with startling fluency. But security is rarely urgent until it fails. The quiet success of safe code is invisible; the drama of an exploit is not. Security-by-default requires a different design orientation altogether. And neither the models nor their usage patterns are currently set up for it.

The second assumption here — more subtle — is that the problem lies in poor prompts. The term of art is “naïve prompting”: casually asking an LLM to “write code to upload a file,” for instance, without specifying how securely. The solution, some suggest, is smarter prompting or tighter guidelines. But this places a curiously utopian burden on end-users. Why should the safety of generated systems hinge on whether someone remembered the right phrasing in a chat box?

It’s worth stating this plainly: expecting individual developers to be both prompt engineers and information security specialists is structurally unsound. It’s like asking each Wikipedia reader to fact-check the article before quoting it. The tool, not the user, must carry a bias toward safety.

Which brings us to the third point — and the real opportunity. The findings suggest that some models, like Anthropic’s Claude 3.7, performed better when given even vague security-aware prompts. This hints at an underlying capability: the models can generate competent, vulnerability-free code, but only if sufficiently cued. The responsibility, then, shifts to those curating how these systems are deployed — model builders, IDE plugin developers, corporate engineering leads.

Beyond prompt guards or fine-tuning, we may need an infrastructural mindset. Embedding OWASP rules directly into model deployment pipelines. Marking outputs with automated risk scans before integration. Treating LLM outputs as inherently provisional — like early drafts, not divine revelation.

Meanwhile, India sits at a peculiar intersection. With our hybrid software economy of export coding and rapid SaaS startups, the temptation to lean on GenAI will only grow. But so will the costs of cutting corners invisibly. We may find ourselves rewriting code not once, but thrice: first by hand, then by LLM, and finally by incident response after a breach.

The hope is not to resist the shift. It is to lift the floor. A future where generative tools prioritize security not upon request, but by expectation, is still possible. But it demands something rare in AI discourse these days: alignment not with ambition, but with care.