Sep 23, 2025·8 min
Security, Performance, and Reliability in AI-Generated Codebases
A practical guide to evaluating security, performance, and reliability in AI-generated codebases with clear checklists for review, testing, and monitoring.
A practical guide to evaluating security, performance, and reliability in AI-generated codebases with clear checklists for review, testing, and monitoring.
“AI-generated code” can mean very different things depending on your team and tooling. For some, it’s a few autocomplete lines inside an existing module. For others, it’s whole endpoints, data models, migrations, test stubs, or a large refactor produced from a prompt. Before you can judge quality, write down what counts as AI-generated in your repo: snippets, entire functions, new services, infrastructure code, or “AI-assisted” rewrites.
The key expectation: AI output is a draft, not a guarantee. It can be impressively readable and still miss edge cases, misuse a library, skip authentication checks, or introduce subtle performance bottlenecks. Treat it like code from a fast junior teammate: helpful acceleration, but it needs review, tests, and clear acceptance criteria.
If you’re using a “vibe-coding” workflow (for example, generating a full feature from a chat prompt in a platform like Koder.ai—frontend in React, backend in Go with PostgreSQL, or a Flutter mobile app), this mindset matters even more. The larger the generated surface area, the more important it is to define what “done” means beyond “it compiles.”
Security, performance, and reliability don’t reliably “appear” in generated code unless you ask for them and verify them. AI tends to optimize for plausibility and common patterns, not for your threat model, traffic shape, failure modes, or compliance obligations. Without explicit criteria, teams often merge code that works in a happy-path demo but fails under real load or adversarial input.
In practice, these overlap. For example, rate limiting improves security and reliability; caching can improve performance but can hurt security if it leaks data between users; strict timeouts improve reliability but can surface new error-handling paths that must be secured.
This section sets the baseline mindset: AI speeds up writing code, but “production-ready” is a quality bar you define and continuously verify.
AI-generated code often looks tidy and confident, but the most frequent problems aren’t stylistic—they’re gaps in judgment. Models can produce plausible implementations that compile and even pass basic tests, while quietly missing the context your system depends on.
Certain categories show up repeatedly during reviews:
catch blocks that hide real issues.Generated code can carry hidden assumptions: time zones always UTC, IDs always numeric, requests always well-formed, network calls always fast, retries always safe. It may also include partial implementations—a stubbed security check, a “TODO” path, or a fallback branch that returns default data instead of failing closed.
A common failure mode is borrowing a pattern that’s correct somewhere else, but wrong here: reusing a hashing helper without the right parameters, applying a generic sanitizer that doesn’t match your output context, or adopting a retry loop that unintentionally amplifies load (and cost).
Even when code is generated, humans remain accountable for its behavior in production. Treat AI output as a draft: you own the threat model, the edge cases, and the consequences.
AI-generated code often looks confident and complete—which makes it easy to skip the basic question: “What are we protecting, and from whom?” A simple threat model is a short, plain-language habit that keeps security decisions explicit before the code solidifies.
Start by naming the assets that would hurt if compromised:
Then list the actors: regular users, admins, support staff, external services, and attackers (credential stuffing, fraudsters, bots).
Finally, draw (or describe) trust boundaries: browser ↔ backend, backend ↔ database, backend ↔ third-party APIs, internal services ↔ public internet. If AI proposes “quick” shortcuts across these boundaries (e.g., direct database access from a public endpoint), flag it immediately.
Keep it short enough to actually use:
Capture the answers in the PR description, or create a brief ADR (Architecture Decision Record) when the choice is long-lived (e.g., token format, webhook verification approach). Future reviewers can then tell whether AI-generated changes still match the original intent—and what risks were knowingly accepted.
AI-generated code can look clean and consistent while still hiding security footguns—especially around defaults, error handling, and access control. During review, focus less on style and more on “what can an attacker do with this?”
Trust boundaries. Identify where data enters the system (HTTP requests, webhooks, queues, files). Ensure validation happens at the boundary, not “somewhere later.” For output, check encoding is context-appropriate (HTML, SQL, shell, logs).
Authentication vs. authorization. AI code often includes “isLoggedIn” checks but misses resource-level enforcement. Verify every sensitive action checks who can act on which object (e.g., userId in the URL must match permissions, not just exist).
Secrets and config. Confirm API keys, tokens, and connection strings are not in source, sample configs, logs, or tests. Also check that “debug mode” isn’t enabled by default.
Error handling and logging. Ensure failures don’t return raw exceptions, stack traces, SQL errors, or internal IDs. Logs should be useful but not leak credentials, access tokens, or personal data.
Ask for one negative test per risky path (unauthorized access, invalid input, expired token). If the code can’t be tested that way, it’s often a sign the security boundary isn’t clear.
AI-generated code often “solves” problems by adding packages. That can quietly expand your attack surface: more maintainers, more update churn, more transitive dependencies you didn’t explicitly choose.
Start by making dependency choice intentional.
A simple rule works well: no new dependency without a short justification in the PR description. If the AI suggests a library, ask whether standard library code or an existing approved package already covers the need.
Automated scans are only useful if findings lead to action. Add:
Then define handling rules: what severity blocks merges, what can be time-boxed with an issue, and who approves exceptions. Keep these rules documented and link them from your contribution guide (e.g., /docs/contributing).
Many incidents come from transitive dependencies pulled in indirectly. Review lockfile diffs in PRs, and regularly prune unused packages—AI code can import helpers “just in case” and never use them.
Write down how updates happen (scheduled bump PRs, automated tooling, or manual), and who approves dependency changes. Clear ownership prevents stale, vulnerable packages from lingering in production.
Performance isn’t “the app feels fast.” It’s a set of measurable targets that match how people actually use your product—and what you can afford to run. AI-generated code often passes tests and looks clean, yet still burns CPU, hits the database too often, or allocates memory unnecessarily.
Define “good” in numbers before you tune anything. Typical goals include:
These targets should be tied to a realistic workload (your “happy path” plus common spikes), not a single synthetic benchmark.
In AI-generated codebases, inefficiency often shows up in predictable places:
Generated code is frequently “correct by construction” but not “efficient by default.” Models tend to choose readable, generic approaches (extra abstraction layers, repeated conversions, unbounded pagination) unless you specify constraints.
Avoid guessing. Start with profiling and measurement in an environment that resembles production:
If you can’t show a before/after improvement against your goals, it’s not optimization—it’s churn.
AI-generated code often “works” but quietly burns time and money: extra database round trips, accidental N+1 queries, unbounded loops over large datasets, or retries that never stop. Guardrails make performance a default rather than a heroic afterthought.
Caching can hide slow paths, but it can also serve stale data forever. Use caching only when there is a clear invalidation strategy (time-based TTL, event-based invalidation, or versioned keys). If you can’t explain how a cached value gets refreshed, don’t cache it.
Confirm timeouts, retries, and backoff are set intentionally (not infinite waits). Every external call—HTTP, database, queue, or third-party API—should have:
This prevents “slow failures” that tie up resources under load.
Avoid blocking calls in async code paths; check thread usage. Common offenders include synchronous file reads, CPU-heavy work on the event loop, or using blocking libraries inside async handlers. If you need heavy computation, offload it (worker pool, background job, or separate service).
Ensure batch operations and pagination for large datasets. Any endpoint returning a collection should support limits and cursors, and background jobs should process in chunks. If a query can grow with user data, assume it will.
Add performance tests to catch regressions in CI. Keep them small but meaningful: a few hot endpoints, a representative dataset, and thresholds (latency percentiles, memory, and query counts). Treat failures like test failures—investigate and fix, not “rerun until green.”
Reliability isn’t just “no crashes.” For AI-generated code, it means the system produces correct results under messy inputs, intermittent outages, and real user behavior—and when it can’t, it fails in a controlled way.
Before reviewing implementation details, agree on what “correct” looks like for each critical path:
These outcomes give reviewers a standard to judge AI-written logic that may look plausible but hides edge cases.
AI-generated handlers often “just do the thing” and return 200. For payments, job processing, and webhook ingestion, that’s risky because retries are normal.
Check that the code supports idempotency:
If the flow touches a database, queue, and cache, verify that consistency rules are spelled out in code—not assumed.
Look for:
Distributed systems fail in pieces. Confirm the code handles scenarios like “DB write succeeded, event publish failed” or “HTTP call timed out after the remote side succeeded.”
Prefer timeouts, bounded retries, and compensating actions over infinite retries or silent ignores. Add a note to validate these cases in tests (covered later in /blog/testing-strategy-that-catches-ai-mistakes).
AI-generated code often looks “complete” while hiding gaps: missing edge cases, optimistic assumptions about inputs, and error paths that were never exercised. A good testing strategy is less about testing everything and more about testing what can break in surprising ways.
Start with unit tests for logic, then add integration tests where real systems can behave differently than mocks.
Integration tests are where AI-written glue code most often fails: wrong SQL assumptions, incorrect retry behavior, or mis-modeled API responses.
AI code frequently under-specifies failure handling. Add negative tests that prove the system responds safely and predictably.
Make these tests assert on outcomes that matter: correct HTTP status, no data leakage in error messages, idempotent retries, and graceful fallbacks.
When a component parses inputs, builds queries, or transforms user data, traditional examples miss weird combinations.
Property-based tests are especially effective for catching boundary bugs (length limits, encoding issues, unexpected nulls) that AI implementations may overlook.
Coverage numbers are useful as a minimum bar, not a finish line.
Prioritize tests around authentication/authorization decisions, data validation, money/credits, deletion flows, and retry/timeout logic. If you’re unsure what’s “high risk,” trace the request path from the public endpoint to the database write and test the branches along the way.
AI-generated code can look “done” while still being hard to operate. The quickest way teams get burned in production is not a missing feature—it’s missing visibility. Observability is what turns a surprising incident into a routine fix.
Make structured logging non-optional. Plain text logs are fine for local dev, but they don’t scale once multiple services and deployments are involved.
Require:
The goal is that a single request ID can answer: “What happened, where, and why?” without guessing.
Logs explain why; metrics tell you when things start degrading.
Add metrics for:
AI-generated code often introduces hidden inefficiencies (extra queries, unbounded loops, chatty network calls). Saturation and queue depth catch these early.
An alert should point to a decision, not just a graph. Avoid noisy thresholds (“CPU > 70%”) unless they’re tied to user impact.
Good alert design:
Test alerts on purpose (in staging or during a planned exercise). If you can’t verify an alert fires and is actionable, it’s not an alert—it’s a hope.
Write lightweight runbooks for your critical paths:
Keep runbooks close to the code and process—e.g., in the repo or internal docs linked from /blog/ and your CI/CD pipeline—so they get updated when the system changes.
AI-generated code can increase throughput, but it also increases variance: small changes can introduce security issues, slow paths, or subtle correctness bugs. A disciplined CI/CD pipeline turns that variance into something you can manage.
This is also where end-to-end generation workflows need extra discipline: if a tool can generate and deploy quickly (as Koder.ai can with built-in deployment/hosting, custom domains, and snapshots/rollback), your CI/CD gates and rollback procedures should be equally fast and standardized—so speed doesn’t come at the cost of safety.
Treat the pipeline as the minimum bar for merge and release—no exceptions for “quick fixes.” Typical gates include:
If a check is important, make it blocking. If it’s noisy, tune it—don’t ignore it.
Prefer controlled rollouts over “all-at-once” deploys:
Define automatic rollback triggers (error rate, latency, saturation) so the rollout stops before users feel it.
A rollback plan is only real if it’s fast. Keep database migrations reversible where possible, and avoid one-way schema changes unless you also have a tested forward-fix plan. Run periodic “rollback drills” in a safe environment.
Require PR templates that capture intent, risk, and testing notes. Maintain a lightweight changelog for releases, and use clear approval rules (e.g., at least one reviewer for routine changes, two for security-sensitive areas). For a deeper review workflow, see /blog/code-review-checklist.
“Production-ready” for AI-generated code shouldn’t mean “it runs on my machine.” It means the code can be safely operated, changed, and trusted by a team—under real traffic, real failures, and real deadlines.
Before any AI-generated feature ships, these four items must be true:
AI can write code, but it can’t own it. Assign a clear owner for each generated component:
If ownership is unclear, it’s not production-ready.
Keep it short enough to actually use in reviews:
This definition keeps “production-ready” concrete—less debate, fewer surprises.
AI-generated code is any change whose structure or logic was substantially produced by a model from a prompt—whether that’s a few lines of autocomplete, a whole function, or an entire service scaffold.
A practical rule: if you wouldn’t have written it that way without the tool, treat it as AI-generated and apply the same review/test bar.
Treat AI output as a draft that can be readable and still be wrong.
Use it like code from a fast junior teammate:
Because security, performance, and reliability rarely appear “by accident” in generated code.
If you don’t specify targets (threat model, latency budgets, failure behavior), the model will optimize for plausible patterns—not for your traffic, compliance needs, or failure modes.
Watch for recurring gaps:
Also scan for partial implementations like TODO branches or fail-open defaults.
Start small and keep it actionable:
Then ask: “What is the worst thing a malicious user could do with this feature?”
Focus on a few high-signal checks:
Ask for at least one negative test on the riskiest path (unauthorized, invalid input, expired token).
Because the model may “solve” tasks by adding packages, which expands attack surface and maintenance burden.
Guardrails:
Review lockfile diffs to catch risky transitive additions.
Define “good” with measurable targets tied to real workload:
Then profile before optimizing—avoid changes you can’t validate with before/after measurements.
Use guardrails that prevent common regressions:
Reliability means correct behavior under retries, timeouts, partial outages, and messy inputs.
Key checks:
Prefer bounded retries and clear failure modes over infinite retry loops.