LLMs write Rust with seven categories of hidden bugs the compiler doesn't catch

A developer spent six months using Claude, GPT and Cursor as a full-fledged second programmer on production Rust code. Throughout that time, he carefully tracked their mistakes and compiled an impressive list: seven categories of bugs that LLMs make systematically.

The worst part is that these bugs pass the compiler, unit tests and even the clippy static analyzer. Yet they corrupt memory or wreck the application's architecture.

Seven types of hidden bugs

These are the categories LLMs get wrong again and again:

• Lifetime laundering: variables live longer than Rust's type system allows. This leads to use-after-free at runtime.
• Mutex across await: code holds a lock while awaiting an async operation. A guaranteed deadlock.
• Drop in transactions: the model forgets to call drop when a resource needs closing (closing a file, rolling back a transaction). The resource leaks.
• Unaligned read: dereferencing memory with the wrong alignment. The processor crashes on ARM and silently breaks on x86.
• Async cancellation: async operations get cancelled incorrectly, leaving dangling state behind.
• Orphan rule: trait implementations violate the orphan rule. The code compiles in one project and breaks when you add a dependency.
• Arrays on the stack: huge arrays get allocated on the stack instead of the heap. The result is a stack overflow and a segfault.

Why this is dangerous in production

The whole appeal of Rust is that the compiler catches bugs during development, not in production. But the compiler lets these seven bugs through, because they don't violate syntax or the formal typing rules.

As a result, the code builds without warnings, passes unit tests, runs through clippy and ships to production. There, everything breaks, but by then it's too late. This is worse than an explicit compiler error, because the developer believes everything is fine.

Why LLMs get this wrong

Rust is a niche language in LLM training data. Python, JavaScript and Java make up 70% of the code the models were trained on. Rust may account for 2-3%.

At the same time, Rust demands a deep understanding of ownership, lifetimes and the type system, which LLMs grasp only superficially. Models spot patterns and copy them. They know that "this is usually written like this," but not why it works in Java and breaks in Rust. So they reproduce the patterns incorrectly, creating the illusion of code that compiles.

Rust breaks LLMs because it's a language where you have to think, not

just copy patterns.

Practical takeaways

The developer doesn't advise abandoning LLMs entirely, but he recommends the following:

• Don't rely on LLMs for critical code: cryptography, memory handling, concurrency
• Require formal code review even when the compiler doesn't complain
• Use Miri, an interpreter that catches undefined behavior during development
• Learn Rust well enough to either write the code yourself or find the bugs in LLM-generated code

What this means

LLMs are a useful tool, but not a replacement for humans. They work well in dynamic languages (Python, JavaScript) because those have fewer rules you need to understand intuitively. In Rust, you either have to check every line or keep a human in the decision-making loop.

For frameworks like Next.js or Django, an LLM assistant is a powerful accelerator. For production Rust, it's more a draft for human review than a full-fledged colleague.