The AI Code Review: What Cursor Gets Wrong (And How to Fix It)
48% of Cursor's auto-generated code contains critical issues. Here's what the benchmarks reveal and how to use AI coding tools without destroying your codebase.
March 8, 2025 10 min read
Cursor's auto mode generates code with a 48% failure rate requiring corrections, including critical vulnerabilities.
Let that sink in. Nearly half of the code needs fixing.
AI-generated pull requests contain 10.83 issues each versus 6.45 in human PRs. That's 1.7x more problems. And AI-authored code has 1.4x more critical issues and 1.7x more major issues than human-written code.
But here's what makes this interesting: Cursor is still faster. 62.95 seconds average versus 89.91 seconds for GitHub Copilot.
So you're getting code 30% faster that has 70% more issues.
The question isn't whether to use AI coding tools. It's how to use them without destroying your codebase.
The Quality vs Speed Tradeoff
Cursor solves tasks faster but with a lower success rate.
Copilot is slower but more accurate. Cursor is faster but more error-prone.
What this means in practice:
If you measure productivity by code written, Cursor wins. If you measure productivity by working code shipped, Copilot wins. If you measure productivity by total time including debugging, neither may win.
The dirty secret of AI coding tools: speed metrics don't capture the time spent fixing AI-generated bugs.
When you're planning your , factor in debugging time, not just generation time. AI tools compress the writing phase but expand the debugging phase.
AI-generated code introduced risky security flaws in 45% of tests. 62% of AI-generated code contains design flaws or known security vulnerabilities.
The specific vulnerabilities:
1.88x more likely to introduce improper password handling
1.91x more likely to make insecure object references
2.74x more likely to add XSS vulnerabilities
1.82x more likely to implement insecure deserialization
86% failure rate defending against Cross-Site Scripting in relevant code samples
Here's the kicker: while models improved at writing functional and syntactically correct code, security performance remained flat regardless of model size or training sophistication.
The implication: Bigger, newer models won't solve the security problem. The issue isn't model capability. It's that AI tools lack security context by design.
AI coding assistants don't inherently understand your application's risk model, internal standards, or threat landscape. They generate code that looks correct but behaves insecurely.
When you're building with AI, assume every line of AI-generated code has potential security issues. Review with that assumption.
The Architectural Drift Problem
One of the hardest risks to detect is architectural drift - subtle model-generated design changes that break security invariants without violating syntax.
What this looks like:
AI suggests moving authentication logic from middleware to route handlers. Syntactically correct. Architecturally wrong. Now half your routes are protected, half aren't.
AI generates a new API endpoint that bypasses rate limiting. Works perfectly in testing. Opens you to DDoS in production.
AI refactors database queries to be more efficient. Accidentally removes authorization checks. Data leaks across tenants.
The danger: These changes often evade static analysis tools and human reviewers because the code "looks correct."
Traditional code review processes aren't designed to catch architectural drift. You need to specifically look for:
Changes to authentication/authorization patterns
New database queries that might skip access controls
API endpoints that bypass existing middleware
Refactoring that removes security checks
This is a new category of risk that didn't exist before AI coding tools.
The Carnegie Mellon Finding
More than 800 popular GitHub projects experienced degrading code quality after adopting AI tools.
AI briefly accelerates code generation. But code quality trends continue moving in the wrong direction over time.
Why this happens:
AI generates code that works initially but has subtle issues. Those issues compound. Technical debt accumulates faster than with human-written code because the problems are harder to spot.
The code looks reasonable. It passes tests. It ships. Then months later, you discover the AI made assumptions that don't hold under production load, or implemented patterns that don't scale, or created coupling that makes refactoring expensive.
The pattern: AI tools are excellent at the tasks developers find boring (boilerplate) but fail at the tasks that actually require expertise (complex logic, architecture).
This suggests AI tools don't replace senior engineering judgment. They just shift where that judgment is needed.
Cursor vs Copilot: The Real Differences
Beyond speed and accuracy, the tools have different strengths and use cases.
Context awareness:
Cursor: Larger context window, full repo analysis, better for complex multi-file tasks
Copilot: Sees active buffer plus surrounding files, good at local reasoning
Platform approach:
Cursor: Standalone AI code editor that wants to be your entire dev environment
Complex projects requiring advanced AI capabilities
Full repo context matters for your work
You want granular control over AI behavior
You're experienced with VS Code
When to choose Copilot:
You want speed and simplicity
Tight GitHub integration matters
You work primarily on file-specific tasks
You use JetBrains or other non-VS Code IDEs
GitHub Copilot enhances whatever coding setup you already love. Cursor wants to become your entire development environment.
The platform lock-in risk is real. If you build workflows around Cursor-specific features, migrating becomes harder.
The Context Window Trap
Cursor's larger context window is marketed as an advantage. But the research shows issues.
The problems:
30% increase in CPU and memory usage from AI operations
25% of large codebase tasks experience latency spikes
Editor can lag or freeze with larger codebases
Recent updates introduced "conversation too long" errors that made the editor "completely unusable" for some users
The insight: More context doesn't necessarily mean better code. It may just mean slower, more resource-intensive generation with the same quality issues.
A developer working on a racing game hit Cursor's limitation after just 1 hour of coding when it refused to continue after 800 lines. The AI told them: "can't generate code, develop the logic yourself to ensure..."
The takeaway: Cursor's context window helps with some tasks but creates new problems. Larger context correlates with higher resource usage, not necessarily higher quality output.
Boilerplate vs Complex Logic
For boilerplate and well-defined patterns, Cursor's AI is often praised. But when dealing with complex, nuanced, or abstract problems, its limitations start to show, failing to grasp the deeper logic of an application.
What AI tools actually excel at:
CRUD operations
REST API endpoints following standard patterns
Database schema generation
Form validation
Test boilerplate
Configuration files
What AI tools struggle with:
Complex business logic
Performance optimization
Security-critical code
Novel algorithms
System architecture
Tradeoff decisions
The pattern is clear: AI handles the boring, repetitive work well. It fails at the interesting, complex problems that require expertise.
When prioritizing features for your MVP, use AI for scaffolding and boilerplate. Write complex business logic yourself or have senior engineers review it line by line.
Performance Issues at Scale
AI operations increase CPU and memory usage by up to 30%. Latency spikes occur in around 25% of large codebase tasks.
Additional performance problems:
40% of developers encounter connectivity interruptions
Offline usage reduces AI feature availability by up to 80%
20%+ of development teams experience interruptions due to API rate limits
Reliance on third-party models can increase costs by up to 30%
These issues compound when you're building at scale. What works smoothly on a 1,000-line codebase becomes painful on a 100,000-line codebase.
The cost consideration:
Cursor Pro is $20/month. Cursor Ultra is $200/month. If you have a team of 10 developers, that's $200-$2,000/month in tool costs.
Then add 30% increase in API costs from model usage. Then add productivity loss from connectivity issues and rate limits.
The total cost of AI coding tools is higher than the subscription price.
The Conversation Loop Problem
A recent Cursor update introduced a "Your conversation is too long" error that would interrupt workflow, even for users providing very short, 1-2 line instructions.
What this reveals:
AI coding tools are stateful. They maintain conversation context. As context grows, performance degrades and errors emerge.
Best practices to avoid this:
Start new conversations for new features
Don't let context fill with irrelevant code
Be explicit about what context matters
Clear conversation history regularly
Focus on one task per conversation
Managing conversation state is a new skill developers need to learn. It's not just about writing good prompts. It's about managing context over time.
How to Actually Use AI Coding Tools
Based on the research, here's how to use AI tools without destroying your codebase.
1. Treat AI as a Junior Developer
Review all AI-generated code assuming it has security flaws. Never merge AI code without human review. Focus AI on boilerplate, handle complex logic yourself.
Scan all AI-generated code for common vulnerabilities
3. Architectural Guardrails
Establish coding standards AI must follow
Use linters and static analysis (but don't rely solely on them)
Manual review for any architectural changes
Version control everything to catch architectural drift
Track which code was AI-generated for future review
4. Context Management
Keep conversations focused and short
Start new conversations for new features
Don't let context window fill with irrelevant code
Be explicit about what context matters
Clear history when switching tasks
5. Testing Requirements
Increase test coverage when using AI-generated code
Security scanning on all AI-generated code
Manual verification of business logic
Track metrics on AI-generated code quality
Compare AI vs human code defect rates
6. Tool Selection Strategy
Use Copilot for integration with existing workflows. Use Cursor for greenfield projects where full control is acceptable. Evaluate based on your team's existing IDE preferences.
Consider cost: Cursor is 2x the price but not 2x the performance.
When building your MVP, start with the tool that integrates with your existing setup rather than forcing a platform switch.
The True Productivity Question
If you generate code 30% faster but it has 70% more issues, are you actually more productive?
The math:
AI generates 100 lines in 60 seconds (1.67 lines/second)
Human writes 100 lines in 90 seconds (1.11 lines/second)
By this math, human coding is faster when you include debugging time.
The calculation changes based on:
How fast you catch AI issues
How complex the issues are to fix
Whether issues make it to production
Cost of production bugs vs development bugs
But the point stands: raw generation speed is the wrong metric.
The Language-Specific Risk
Java had the highest failure rate: 70%+ of LLM-generated code introduced security flaws.
Why some languages are riskier:
Statically typed languages like Java have complex security patterns around memory management, threading, and object lifecycle. AI models trained on general code don't understand Java's specific security idioms.
Dynamic languages like JavaScript are more forgiving, but AI generates different categories of issues: prototype pollution, XSS, insecure dependencies.
The takeaway: Language matters. AI coding tools have different failure modes in different languages. Adjust your review processes accordingly.
The Bottom Line
AI coding tools generate code 30% faster with 70% more issues. Security vulnerabilities remain flat regardless of model improvements. Code quality degrades over time in projects using AI tools.
But that doesn't mean avoid AI tools. It means use them strategically.
The winning approach:
Use AI for boilerplate and repetitive tasks
Write complex business logic and security-critical code yourself
Review all AI code assuming it has issues
Implement automated security scanning
Track AI-generated code quality over time
Start conversations fresh for new features
Never merge AI code without human review
48% of Cursor's auto-generated code contains issues. That's the baseline. Your job is to catch those issues before they reach production.
AI coding tools are powerful accelerators when used correctly and dangerous liabilities when trusted blindly.
The companies that succeed with AI coding tools treat them as junior developers that need constant supervision, not senior engineers that can be trusted autonomously.
Ready to build with AI tools while maintaining code quality and security? Let's talk about AI development that balances speed with safety.
A practical comparison of Cursor and Codeium (Windsurf) AI coding assistants for startup teams, with recommendations based on budget and IDE preferences.
