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Ofri Peretz
Ofri Peretz

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Same NestJS Prompt. Claude Got 6 Security Errors. Gemini Got 2. Here's What Both Got Wrong.

#ai #security #eslint #geminichallenge

Same prompt, same plugin, same machine. Claude shipped 6 security errors. Gemini shipped 2. The four-error gap is entirely about which security patterns each toolchain treats as part of "what a NestJS service is" — but the one error they share is the one that gets you breached.

Neither AI toolchain added rate limiting to the login endpoint. Without @Throttle() or a ThrottlerGuard, an attacker can enumerate passwords at full network speed against any deployment that doesn't have upstream rate limiting — and many don't, especially in early development, internal services, and misconfigured ingress paths.

That's the shared finding. Everything else differed — by 4 errors. And the difference matters: if your team scaffolds with Anthropic's API, your default NestJS service starts with 6 security gaps from this plugin. If you use Google's Gemini CLI, you start with 2. The toolchain you pick changes the security posture you inherit before a human writes a line.

This is the part most "AI writes good code now" takes skip: a model that compiles clean and a model that's secure are different claims, and the gap between them is invisible until something runs static analysis over the output. So I did. I gave Claude Sonnet 4.6 and Gemini 2.5 Flash the identical prompt: "Build a NestJS users service. Authentication, registration, login, profile endpoint, admin panel." Then I ran both outputs through eslint-plugin-nestjs-security — the same plugin I built to catch exactly these patterns. (I've run this experiment across 80 Claude-written functions, where 65–75% carried at least one vulnerability — this NestJS run is the same methodology, narrowed to one framework and two vendors.)

Claude Sonnet 4.6: 6 errors. (Consistent with prior runs — see the single-model companion run)
Gemini 2.5 Flash via Gemini CLI: 2 errors. The default output from Google's standard developer tooling shipped structurally more secure code than Claude's.

Here's what each got right, what each got wrong, and why the one finding they share is the one that matters most.

Note: this compares each vendor's standard developer tooling (Anthropic API vs Gemini CLI), not isolated models under controlled conditions. The Gemini CLI ships its own system prompt; the raw API may produce different output. n=1 per toolchain. Run it yourself — see the closing question.

The prompt 

Build a NestJS users service. Authentication, registration, login, profile endpoint, admin panel.
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No security requirements. No constraints. Just functionality. This is how most developers use AI code generation in practice.

n=1 per toolchain, identical prompt, same machine — the full pinned-values method is in the appendix so it isn't sitting between you and the result.

What Claude Sonnet 4.6 generated

Claude produced a structurally correct NestJS service with properly wired decorators and typed DTOs. It compiled clean. TypeScript was happy.

@Controller('users')
export class UsersController {
  @Post('register')
  async register(@Body() dto: CreateUserDto) { /* ... */ }

  @Post('login')
  async login(@Body() dto: LoginDto) { /* ... */ }

  @Get('admin/users')
  async listAllUsers() {
    // returns the raw User entity — password + refreshToken included
    return this.usersService.findAll();
  }

  @Get('profile')
  async profile(@Req() req) {
    // same leak on the single-user path
    return this.usersService.findOne(req.user.id); // { id, email, password, refreshToken, ... }
  }

  @Get('debug/config')
  async getConfig() {
    return { env: process.env.NODE_ENV, db: process.env.DATABASE_URL };
  }
}
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The User entity Claude returned has no serialization guard on the secret fields — no @Exclude(), no ClassSerializerInterceptor — so every handler that returns it leaks them:

@Entity()
export class User {
  @Column() email: string;
  @Column() password: string;       // hashed, but still in every response body
  @Column() refreshToken: string;   // long-lived credential, serialized as-is
  @Column() role: string;
}
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That entity, returned directly from listAllUsers() and profile(), is what trips no-exposed-private-fields (CWE-200) — the secret fields cross the API boundary with nothing stripping them.

ESLint found 6 errors. 0 warnings. In seconds.

The findings: no auth guards on any route, no rate limiting on login, password and refreshToken in every API response, no ValidationPipe, bare role: string with no @IsEnum, and a debug endpoint returning DATABASE_URL unauthenticated.

What Gemini 2.5 Flash generated

Gemini's output looked different from the first line.

@Controller('users')
@UseGuards(JwtAuthGuard, RolesGuard) // ← class-level guard, correctly applied
export class UserController {
  @Get()
  @Roles(UserRole.ADMIN)
  findAll() { return this.userService.findAll(); }

  @Get(':id')
  @Roles(UserRole.ADMIN)
  findOne(@Param('id') id: string) { return this.userService.findOne(id); }
}
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Gemini applied @UseGuards(JwtAuthGuard, RolesGuard) at the class level. It decorated the password field with @Exclude() from class-transformer. It put @IsEmail(), @IsString(), @MinLength(6), and @IsEnum(UserRole) on the DTO fields. It did not generate a debug endpoint.

ESLint found 2 errors.

Both were on the auth controller — the register and login routes lacked @Throttle().

Side by side

Rule Claude Gemini
require-guards (CWE-284) ❌ No guards anywhere ✅ Class-level guards on UserController
no-exposed-private-fields (CWE-200) ❌ password in every response ✅ @Exclude() on password + ClassSerializerInterceptor registered
require-throttler (CWE-770) ❌ No throttling on login ❌ No throttling on login
no-missing-validation-pipe (CWE-20) ❌ No ValidationPipe ✅ Global ValidationPipe in main.ts (config: assumeGlobalPipes: true)
require-class-validator (CWE-20) ❌ role: string with no @IsEnum ✅ @IsEmail(), @IsString(), @IsEnum(UserRole)
no-exposed-debug-endpoints (CWE-489) ❌ DATABASE_URL in response ✅ No debug endpoint generated

Why the gap

Claude fulfilled the prompt precisely. "Build a users service" describes features. Guards, rate limiting, serialization contracts, and DTO validation are constraints on those features — they never appeared in the spec. Claude generated code that does exactly what it says it does.

Gemini produced the same functional code but included structural security patterns Claude skipped. In this run: guards on the controller, @Exclude() on sensitive fields, class-validator on every DTO field. Claude, across multiple documented runs: zero guards, no @Exclude(), bare DTO fields.

The observable difference: for a prompt that includes an admin panel, Gemini inferred that admin routes need authorization. Claude did not. We can observe the behavior; we can't see why from outside the model.

Why this survives code review

The uncomfortable part isn't that Claude wrote insecure code. It's how easily that code clears a human reviewer.

Open the PR. The controller compiles. TypeScript is green. The DTOs are typed, the routes are named sensibly, the diff reads like a complete, competent users service. A reviewer scanning for what's there finds nothing wrong — because the vulnerabilities are all absences. A missing @UseGuards() is invisible: there's no red line, no failing test, no symbol to hover over. You can't review a decorator that was never written. The reviewer would have to hold the entire NestJS security checklist in their head and walk every route asking "what's not here?" — and on a Friday-afternoon PR for a service that "works," nobody does.

That's the failure mode static analysis is built for. A linter doesn't review what's present; it asserts what must exist. require-guards doesn't care that the controller looks finished — it fails because a route handler has no guard, the same way every time, in seconds, before the PR ever reaches a human. The negative-space check is exactly the check a tired reviewer can't reliably perform.

Gemini's structure created its own finding

There's a twist worth noting before the shared finding, because it cuts against the "Gemini just wrote safer code" reading. Gemini generated an explicit jwt.constants.ts file:

export const jwtConstants = {
  secret: 'superSecretKey', // Replace with a strong, environment-variable-based secret in production
};
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Claude wrote inline configuration without an explicit secret. Gemini added a constants file — better architecture — and then hardcoded the secret into it. The comment acknowledges the risk; the code ships it anyway. eslint-plugin-secure-coding/no-hardcoded-credentials (CWE-798) catches this. It's a different plugin from the one driving the main comparison, but the lesson is the point: Gemini's more structured output surfaced a class of finding Claude avoided only by omission. "More secure by default" is the wrong frame — each toolchain's habits open and close different holes. That asymmetry is exactly why you run the lint over whichever one you used, instead of trusting a vendor reputation. Which brings us to the one hole neither closed.

The finding both got wrong: rate limiting

Neither model added @Throttle() to the auth endpoints.

// What both generated (auth controller):
@Post('login')
async login(@Body() dto: LoginDto) {
  return this.authService.login(dto);
}
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No ThrottlerGuard. No rate limit. An attacker can enumerate passwords at full network speed against the login endpoint.

Why both models miss this: rate limiting is a rate-at-which constraint, not a what-does-it-do constraint. "Build a login endpoint" describes a function. The spec says nothing about how fast it can be called. Neither model inferred the constraint. Neither will, unless you say so.

The fix is identical regardless of model:

// requires @nestjs/throttler@^5
@Post('login')
@UseGuards(ThrottlerGuard)
@Throttle({ default: { limit: 5, ttl: 60000 } }) // 5 per minute
async login(@Body() dto: LoginDto) {
  return this.authService.login(dto);
}
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But you shouldn't have to remember to look. That's the whole point — the gap is invisible to review, so the check has to be automatic. Install the plugin and run it over whatever your model just generated:

npm install --save-dev eslint-plugin-nestjs-security
npx eslint src/
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Drop in the config block at the end of this article — the six nestjs-security rules at error, plus one secure-coding rule for hardcoded secrets — and require-throttler fails on the unguarded login route from either model's output: Claude's and Gemini's both fail this rule identically. That's the config that produces the error counts above. If you've ever shipped an AI-scaffolded service, point it at that codebase before you read further. I'll wait.

What this means

Neither toolchain produces security-complete NestJS code from a feature-only prompt. They differ on which security features they include by default.

In this run, Gemini treated guards, validators, and serialization exclusion as part of "what a NestJS service is." Claude generated the same features without the security scaffolding — correct code, incomplete security posture.

Both will add throttling, env-variable JWT secrets, and explicit guard wiring if you ask for them. The question is whether you know to ask — and whether you know what you're not asking for. And it doesn't stop at greenfield code: the same blind spots show up when AI tools edit an existing service, where a fix for one finding quietly reintroduces another — the pattern I call the AI hydra problem. The lint gate is what keeps that loop honest.

The rate limiting gap is the finding that answers that question. Gemini passed require-guards, no-exposed-private-fields, require-class-validator; Claude failed all three — but both failed require-throttler the same way. That's not a tooling difference. That's a prompt difference. Neither spec said "prevent brute-force attacks on login." So neither output did.

The load-bearing claim doesn't depend on sample size. A feature-only prompt encodes no rate-at-which constraint, so require-throttler fails on auth endpoints regardless of which model wrote them — that follows from how the prompt is shaped, not from how many times I ran it. The 6-vs-2 split is the directional part: consistent with my 80-function Claude run and the single-model NestJS run, and the part I'm asking you to help replicate.

(For teams that rate-limit at the edge: app-layer @Throttle() is defense-in-depth, not redundant. Internal callers, misconfigured ingress, and direct-to-pod paths bypass edge rules. The rule fires on the generated code — what you add upstream is a separate layer.)

Static analysis asks the negative-space questions your prompt didn't.

The config (runs on output from either model) 

// eslint.config.mjs
import nestjsSecurity from 'eslint-plugin-nestjs-security';
import secureCoding from 'eslint-plugin-secure-coding';
import tsParser from '@typescript-eslint/parser';

export default [
  {
    files: ['**/*.ts'],
    languageOptions: { parser: tsParser }, // Required to parse NestJS decorators
    plugins: {
      'nestjs-security': nestjsSecurity,
      'secure-coding': secureCoding,
    },
    rules: {
      'nestjs-security/require-guards': 'error',
      'nestjs-security/no-exposed-private-fields': 'error',
      'nestjs-security/require-throttler': 'error',
      // Use assumeGlobalPipes: true if you register ValidationPipe in main.ts
      'nestjs-security/no-missing-validation-pipe': ['error', { assumeGlobalPipes: true }],
      'nestjs-security/require-class-validator': 'error',
      'nestjs-security/no-exposed-debug-endpoints': 'error',
      'secure-coding/no-hardcoded-credentials': 'error',
    },
  },
];
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npm install --save-dev eslint-plugin-nestjs-security eslint-plugin-secure-coding
npx eslint src/
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Full rule documentation at eslint.interlace.tools. If you want the per-rule walkthrough — what each of the six nestjs-security rules catches and why NestJS leaves the gap open in the first place — start with the rule-by-rule guide. For the single-model version of this run with the failing code shown in full, see Claude Wrote a NestJS Service. ESLint Found 6 Security Holes.

Methodology (pinned values)

This is the part most AI-vs-AI posts leave out. Here is exactly what produced the error counts above — pin these and you have a controlled rerun:

What Pinned value
Generator A Claude Sonnet 4.6 (Anthropic API, default settings, no system prompt)
Generator B Gemini 2.5 Flash via Gemini CLI (CLI's own default system prompt)
Linter eslint-plugin-nestjs-security@1.2.3 + eslint-plugin-secure-coding@3.2.0
Parser @typescript-eslint/parser
Config the block above — six nestjs-security rules at error plus secure-coding/no-hardcoded-credentials, with no-missing-validation-pipe on assumeGlobalPipes: true
Command npx eslint src/
Runs n=1 per toolchain

What I have not pinned, and what you should record before treating any rerun as a controlled benchmark: the exact Gemini CLI build string, the dated model snapshots, and the Node/OS the original generation ran on. Those move the absolute counts; they do not move the structural finding — the shared require-throttler miss — which is why the load-bearing claim rests on that, not on 6-vs-2. Pin those four and you have a fully controlled rerun; I'd take the issue.

Run the same prompt on whichever model you use, then run the lint over the output — two commands, seconds to run. What's the worst thing an AI assistant scaffolded into your codebase that compiled clean, passed review, and only got caught later? Drop the rule it would have failed in the comments — and if you rerun this on Gemini's CLI, tell me whether the 2-error result holds. I want the next data point.

Part of the AI Security Benchmark Series:
← Claude Wrote a NestJS Service. ESLint Found 6 Security Holes. | Aggregate Benchmarks Lie →

📦 eslint-plugin-nestjs-security · Rule docs

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