67 lines
3.3 KiB
Markdown
67 lines
3.3 KiB
Markdown
Phase: 8
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Sequence: 010
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Slug: voiceprint-resource-exhaustion
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Verdict: VALID
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Rationale: VoicePrint audio endpoints accept unbounded base64 payloads with no maximum length; 100/min rate limit allows rapid large uploads that can exhaust server memory and disk
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Severity-Original: medium
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Severity: medium
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PoC-Status: pending
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Pre-FP-Flag: none
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Debate: piolium/attack-surface/balanced-chamber-summary.md
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## Summary
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The `voiceprintRouter.analyzeAudio` and `voiceprintRouter.createEnrollment` procedures accept `audioBase64` with only a `minLength(1)` validation. There is no maximum length, no content-type validation, and no size check before decoding. An authenticated attacker can send extremely large base64-encoded payloads that, when decoded, consume significant server memory during base64 decoding, ML preprocessing, and ML inference. The procedures use `protectedProcedure` (100/min default rate limit), providing weak protection against sustained attacks.
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## Location
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- `web/src/server/api/schemas/voiceprint.ts` lines 8–10 (schemas)
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- `web/src/server/services/voiceprint.service.ts` lines 135–140 (service)
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- `web/src/server/api/utils.ts` lines 23–28 (protectedProcedure)
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## Attacker Control
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An authenticated user can send extremely large base64-encoded audio payloads. A 100MB base64 payload (representing ~75MB of audio data) consumes ~300MB+ memory per request (base64 string + decoded buffer + ML features + model inference + disk write).
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## Trust Boundary Crossed
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Resource boundary. Unbounded input exceeds expected resource allocation, affecting all users on the same server.
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## Impact
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- **Memory exhaustion**: Single request can consume 300MB+; 100 rapid requests can exhaust server memory (OOM kill)
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- **Disk exhaustion**: Each request writes a ~75MB audio file to disk; rapid uploads fill disk
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- **ML model resource exhaustion**: ML preprocessing and inference are CPU-intensive; large inputs increase processing time
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- **Service disruption**: Memory exhaustion affects all users on the same server
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## Evidence
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```typescript
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// Schema — no maximum length
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export const AnalyzeAudioSchema = object({
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audioBase64: string([minLength(1)]), // No maxLength
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});
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// Service — no size check before decoding
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export async function analyzeAudio(userId: string, audioBase64: string) {
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const audioBuffer = Buffer.from(audioBase64, "base64"); // No size check
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// ...
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const features = await preprocessAudio(audioBuffer); // ML preprocessing
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const detection = await detectSynthetic(features); // ML inference
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}
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// Rate limit — 100/min for authenticated users
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const rateLimitTiers = {
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authenticated: { limit: 100, windowMs: 60_000 },
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};
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```
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## Reproduction Steps
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1. Authenticated user sends `voiceprintRouter.analyzeAudio` with 100MB base64 payload
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2. Server decodes base64 → 75MB buffer
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3. ML preprocessing and inference consume additional memory
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4. Audio file written to disk (~75MB)
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5. Repeat 100 times in 1 minute → ~30GB+ memory usage → OOM kill or service disruption
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## Defense Search Results
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- valibot `minLength(1)` only sets minimum, no maximum
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- `protectedProcedure` auth check requires authentication
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- Rate limit (authenticated tier) allows 100/min — insufficient for large payloads
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- No content-type validation (no MIME type check)
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- No payload size limit on the HTTP request body
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- No streaming upload support (entire payload loaded into memory)
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