Cache Poisoning in Nestjs with Dynamodb

Cache poisoning occurs when an attacker causes cached responses to store malicious or incorrect data, leading to subsequent users receiving compromised content. In a NestJS application that uses Amazon DynamoDB as a data source, this risk arises when application-layer caching is implemented without strict validation, normalization, and isolation of cached entries. Because DynamoDB stores structured items by key, predictable key construction combined with insufficient input validation can allow an attacker to influence what is cached and how it is retrieved.

Consider a typical NestJS service that caches user profile data using an in-memory or distributed cache. If the cache key is derived directly from user-controlled input such as a request parameter without canonicalization, an attacker can deliberately vary the key to poison distinct cache slots. For example, a profile endpoint /profiles/:userId might compute the cache key as profile:${userId}. If the application does not enforce that userId is a canonical identifier (e.g., a UUID versus an email or an injected string), the same profile can be stored under multiple keys. This leads to cache fragmentation and the possibility of stale or malicious entries persisting across user boundaries.

DynamoDB-specific behaviors can amplify the impact. Because DynamoDB does not enforce schema constraints at the database level, the cached representation may inadvertently include sensitive attributes or version-specific fields that the application later trusts. If the NestJS service caches raw DynamoDB items without stripping or transforming sensitive fields, poisoned cache entries can expose private data to other users. Additionally, conditional writes and optimistic locking via ConditionExpression can be bypassed if cache invalidation logic is tied solely to application-layer timestamps rather than DynamoDB's Version attribute or stream-based change detection.

Another vector involves query responses. If a NestJS controller passes filters directly to a DynamoDB query and caches the resulting items by the raw query parameters, an attacker can manipulate those parameters to cause harmful data to be cached under common query patterns. For example, an endpoint that supports filtering by status may accept ?status=pending. If the cache key includes the unvalidated status value, an attacker could induce the cache to store entries with maliciously crafted status values, which later appear to legitimate users. Because DynamoDB responses are often deserialized directly into JavaScript objects in NestJS, deserialization logic must be strict; otherwise, nested or unexpected attributes can alter object identity and reference behavior in the cache layer.

LLM/AI Security checks in middleBrick highlight risks where untrusted data enters model prompts or outputs, and cache poisoning parallels this when untrusted data shapes cached content that may later be surfaced to AI components or end users. By treating cache entries as untrusted input, you reduce the blast radius of poisoned data. Proper input validation, schema normalization, and strict cache key design ensure that DynamoDB-backed caches in NestJS remain predictable and confined to intended tenants and data contexts.

Remediation focuses on canonicalizing inputs, isolating cache keys, validating and transforming DynamoDB responses, and ensuring cache entries cannot interfere with one another. Below are concrete patterns and code examples for a NestJS service that safely interacts with DynamoDB using the AWS SDK v3 and integrates caching at the service layer.

1. Canonicalize user identifiers and cache keys

Ensure that identifiers used for cache keys are normalized, immutable, and type-checked. Prefer UUIDs over user-supplied strings. If you must accept a user identifier, map it to a canonical ID stored in DynamoDB before using it in cache logic.

import { Injectable } from '@nestjs/common';
import { DynamoDBClient, GetItemCommand } from '@aws-sdk/client-dynamodb';
import { marshall, unmarshall } from '@aws-sdk/util-dynamodb';
import { v4 as uuidv4 } from 'uuid';

@Injectable()
export class ProfileService {
  constructor(
    private readonly ddb: DynamoDBClient,
    private readonly cache: CacheService, // abstracted cache adapter
  ) {}

  async getProfile(candidateId: string): Promise {
    // Canonicalize: verify candidateId is a UUID; if not, look up mapping
    const id = this.isValidUuid(candidateId) ? candidateId : await this.lookupCanonicalId(candidateId);
    const cacheKey = `profile:${id}`;

    const cached = this.cache.get(cacheKey);
    if (cached) return cached;

    const cmd = new GetItemCommand({
      TableName: process.env.PROFILE_TABLE,
      Key: marshall({ id }),
    });
    const { Item } = await this.ddb.send(cmd);
    if (!Item) return null;

    const profile = unmarshall(Item);
    // Validate and transform: keep only expected fields
    const safeProfile = {
      id: profile.id,
      name: String(profile.name ?? ''),
      email: String(profile.email ?? ''),
      // explicitly drop sensitive fields
    };
    this.cache.set(cacheKey, safeProfile, 300); // 5 min TTL
    return safeProfile;
  }

  private isValidUuid(u: string): boolean {
    return /^[0-9a-f]{8}-[0-9a-f]{4}-[14][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$/i.test(u);
  }

  private async lookupCanonicalId(input: string): Promise {
    // Example mapping table query; implement with GetItem or Query
    // Return canonical UUID or throw if not found
    return 'urn:uuid:00000000-0000-0000-0000-000000000000';
  }
}

2. Isolate cache keys by tenant and query context

In multi-tenant scenarios, include a tenant or workspace identifier in the cache key. Avoid caching query responses that contain filters unless those filter values are also canonicalized and limited to an allowlist.

async getFilteredItems(tenantId: string, status: string): Promise {
  const canonicalStatus = this.canonicalStatus(status); // allowlist validation
  const cacheKey = `tenant:${tenantId}:items:status:${canonicalStatus}`;
  // ... use tenant-prefixed DynamoDB query with FilterExpression
}

3. Validate and sanitize DynamoDB responses before caching

Do not cache raw DynamoDB items. Use unmarshall and then enforce a strict schema. Drop or redact unexpected attributes to prevent nested objects from affecting cache identity or being used in deserialization attacks.

4. Use version attributes for cache invalidation

When using conditional writes, include a version attribute (e.g., numeric or timestamp) in the cache entry metadata. Invalidate or update the cache only when the version changes, preventing stale poisoned entries from being served.

5. MiddleBrick integrations for ongoing verification

Use the middleBrick CLI to scan your endpoints regularly: middlebrick scan <url>. In CI/CD, add the GitHub Action to fail builds if security scores degrade. The MCP Server can be used while developing to scan APIs directly from your IDE, helping catch cache poisoning risks early in NestJS services that rely on DynamoDB.

By combining input canonicalization, strict schema validation, and tenant-aware cache keys, you reduce the likelihood that poisoned entries spread across users or persist beyond their intended scope in DynamoDB-backed NestJS applications.