Hallucinated & typosquatted dependencies
Models invent packages that don't exist roughly one in five times - and the same fake names recur, so attackers pre-register them (slopsquatting) and wait for installs. A single hallucinated package has spread to hundreds of repos.
How PeakStack handles it
Verify every dependency in your manifests against the live npm, PyPI, and crates.io registries on each commit, flagging packages that don't exist and names a short edit-distance from popular ones.
Insecure code & broken access control
Around 45% of AI-generated code contains a known security weakness, and most are caught by only one of five major scanners - so any single tool misses the majority. The classic failures are missing authorization and permissive database rules.
How PeakStack handles it
Review every change for security and logic flaws and return each finding with its severity, the exact file and line, why it matters, and a concrete fix.
Scalability bottlenecks under load
Code that's fine for one user can collapse for a thousand: N+1 queries, full-table scans, unbounded result sets, and blocking I/O that never showed up in the demo.
How PeakStack handles it
Run deterministic static analysis for these exact patterns - same input, same finding - so scaling bottlenecks surface before traffic does, not during an outage.
Runaway cloud cost & broken unit economics
Recursive serverless triggers have produced $30,000 spikes, and flat-rate pricing turns power users into losses when inference cost scales with usage - scaling AI startups average just 25% gross margin versus 80%+ for classic SaaS.
How PeakStack handles it
Estimate per-request and per-user cost for each capability from the infrastructure and APIs detected, so an expensive pattern is visible before the bill arrives.
The outcome
From launch anxiety to a clear ship decision
The risks above are invisible by default. PeakStack makes them visible - turning “I think it works” into a number you can act on, with the exact fixes ranked by impact.
Before PeakStack
- App works on your machine
- Unknown security issues
- Unknown cloud costs
- Unknown scaling risks
- Launch by crossed fingers
After PeakStack
Example reportWhat makes it different
It scores business capabilities, not files
A flat list of file warnings tells you nothing about what to fix first. PeakStack groups your code into the capabilities it actually implements - Checkout, Auth, Billing - and scores risk where it lives, so you instantly see which part of the product is the weak link.
- Checkout Highest risk54
- Authentication81
- Billing92
- Notifications76
The wow moment
It reconstructs your architecture from source code
PeakStack reads your repository and draws the system you actually built - every capability and the infrastructure it touches - so you can see coupling and blast radius at a glance. No diagram to maintain; it's derived from the code on every analysis.
Example - reconstructed automatically from a connected repository.
Ready to ship what you build?
PeakStack reviews AI-built code for security, scalability, and cost on every commit - with the exact file, line, and fix.
Explore PeakStack