Everyone's talking about the new batch of coding assistants like they're the final word in developer productivity. I've been running them through the wringer on procurement and vendor security review tasks, and the hype doesn't match the output, especially on security.
My team was evaluating a contract clause for a third-party API integration. I prompted: "Generate a secure Python function to verify a JWT token from an external identity provider. Assume the public key is fetched from a JWKS endpoint. Include validation for issuer, audience, and expiration. Show error handling."
The output looked polished at first glance. It used a known library, had a function structure, and listed the validations. But the devil's in the details. The generated code missed critical checks entirely:
* It did not validate the token's algorithm header before verifying, opening up to algorithm confusion attacks.
* The audience check was a simple string equality, not handling the case where `aud` can be a list of strings.
* There was no rate limiting or caching on the JWKS fetch, which is a recipe for performance issues or a denial-of-service vector during high traffic.
* Error messages were generic, potentially leaking implementation details if not caught higher up.
The correct implementation isn't just about calling a library function. It's about understanding the OAuth 2.0 spec nuances, anticipating how the provider might deviate, and writing defensive code that fails securely. The assistant gave me the happy path with a security-themed paint job.
This isn't an isolated case. Ask it to draft a data processing agreement clause for cross-border data transfer, and it will give you a generic GDPR recital but miss the necessity of specifying the supplementary measures and the exact third-party subprocessor dependencies. It's pattern matching, not understanding.
When you're in procurement, you learn that a vendor's marketing material always highlights features, not the gotchas in the SLA. These code generation tools feel the same. They sell the feature of "secure code generation," but the real cost is in the missing validation, the assumptions, and the hidden vulnerabilities you have to find and fix yourself. That's a lot of technical debt hidden in a shiny demo.
Show me the data
Yeah, that JWKS fetch point is a killer. I've seen similar oversights in API integration guides from vendors, where they hand you a code snippet that hits their endpoint on every single request. It's fine for a demo, but you're right, it falls apart under real load. The audience check issue is also classic - I bet the model was trained on simplified examples where `aud` is always a string.
Have you tried feeding the initial generated code back in and asking specifically about those vulnerabilities? Sometimes they can correct themselves if you prompt for the edge cases directly. Curious if that approach ever yields something actually production-ready.
Still looking for the perfect one
That last point about performance is so real. I had a client rollout stall because their "demo-ready" integration had that exact uncached JWKS fetch. Under load, every single auth request added a full network hop, latency spiked, and the whole login flow crumbled. It looked fine in testing with ten users.
You can't just ask these tools to "add caching." They'll give you a naive in-memory dict without TTL or invalidation logic. Getting a production-ready, resilient cache that respects the JWKS spec's Cache-Control headers requires a human who's been burned before. The marketing makes it seem like you're getting a senior dev's brain, but you're really getting a first draft by someone who read the manual once.
Implementation is 80% process, 20% tool.
Yeah, that's the trap, isn't it? The marketing makes you feel like you're getting a complete solution, but it's all smooth edges with the hard parts sanded off. The caching example hits home - I've seen project timelines blown because a "proof of concept" snippet from a demo lacked any real-world resilience. Everyone assumes someone else will fill in the gaps, but those gaps are where projects live or die.
Have you found a better way to manage expectations with stakeholders who see these polished demos? They're always shocked when the "easy button" still needs weeks of hardening.
Exactly. The smooth, confident presentation of that output is the real problem. It gives a false sense of security. A junior engineer might just copy-paste it, and a rushed reviewer might tick the box because it "looks complete."
Your example of missing algorithm validation is perfect. It's not just an oversight - it's the tool parroting common library usage without understanding *why* those checks exist. The marketing makes it feel like you have an expert pair programmer, but you're actually doing a high-stakes game of spot-the-bug with a very confident intern.
You're nailing the exact problem. That "polished first glance" output is how vendors slip garbage past overworked procurement teams.
The contract trick is, they'll point to this generated code as "reference implementation" in an appendix. It looks thorough, so legal signs off. Then you're on the hook for the cost of the senior dev you actually need to make it work, while they've met their contractual obligation to "provide technical specifications."
The real joke is that missing algorithm validation. A procurement bot reviewing a vendor's security claims would probably miss it too, because it's trained on the same flawed corpus. You're left holding the bag when the breach happens.
Trust but verify.
Polished outputs are dangerous exactly because of that missing algorithm check. The code looks right, so it gets rubber-stamped. It's a contract lawyer's dream and an engineer's future headache.
You see this with API vendors too. They give you a clean snippet that works in a vacuum, then charge you for the "enterprise support package" when your auth layer melts down. The tool isn't writing secure code, it's writing plausible, saleable code.
And good luck getting procurement to budget for the senior review you'll need to fix it all. The demo already "proved" it works.
your mileage will vary
That point about enterprise support packages is spot on. I ran a test last month using a standard vendor SDK generation prompt. The output included a beautifully formatted retry loop with exponential backoff. It looked professional until you realized the retry condition was a generic `Exception` catch-all. Under network blips, it would silently retry on `InvalidCredentialsException`, potentially locking accounts.
The vendor's official "fix" for this pattern? It's in their premium support tier documentation. The tool generates the saleable demo, and the contract funds the cleanup.
Numbers don't lie
You're right that naive caching suggestions are a huge giveaway. I've documented this exact failure mode in migration guides for moving off vendor SDKs. The tools will often propose a simple `lru_cache` decorator on the JWKS fetch function, which creates two new problems.
First, it caches per-process, so it fails utterly in a multi-container environment. Second, and more critically, it completely ignores RFC 7517 section 4.5, which states cache behavior should be derived from HTTP Cache-Control headers, with a default fallback of ten minutes. A proper implementation needs to parse those headers, calculate a TTL, and handle re-fetch on cache miss without a stampede.
That's the gap between a snippet that compiles and a system that works. You need the architectural context the models lack.