Bleichenbacher Attack in Nestjs with Dynamodb

A Bleichenbacher attack is a chosen-ciphertext attack that exploits adaptive padding validation in encryption schemes, commonly RSA PKCS#1 v1.5. In a NestJS application using DynamoDB as the persistence layer, the risk arises not from DynamoDB itself, but from how encrypted data is handled before it reaches DynamoDB and how responses are returned to the client.

Consider a login or token verification flow where a JWT or encrypted session token is decrypted in NestJS using an RSA private key, then the payload (e.g., user identifier) is used to query DynamoDB. If the decryption routine reveals whether a ciphertext is valid based on timing differences or error messages (e.g., BadPaddingException vs. other errors), an attacker can iteratively send manipulated ciphertexts and observe responses to gradually decrypt data or forge tokens. DynamoDB stores the user record keyed by a user ID extracted after successful decryption; the service then returns a 200 OK for a valid user or a 404/401 for invalid cases, unintentionally signaling validity to the attacker.

In this stack, the attack surface includes:

• Unauthenticated API endpoints that accept encrypted or signed tokens as input and perform decryption before authorization checks.
• Error handling that distinguishes between malformed ciphertext, invalid padding, and missing DynamoDB items, enabling timing or behavioral side channels.
• DynamoDB queries that rely on decrypted identifiers; if the query returns different metadata or timing when the item exists, it can amplify the signal to the attacker.

For example, an attacker who intercepts a token can modify bytes and send many requests to the NestJS endpoint. If each request returns slightly different response times or status codes, the attacker can infer when padding becomes valid, eventually revealing the plaintext without ever having the private key. This compromises confidentiality and can lead to privilege escalation if the decrypted payload contains roles or permissions stored alongside user data in DynamoDB.

Remediation focuses on making decryption and DynamoDB lookup behavior constant-time and opaque to the attacker, and ensuring the application does not leak validity through errors or timing.

1. Constant-time decryption and verification

Use a cryptographic library that supports constant-time operations or ensure your validation logic does not branch on secret-dependent conditions. For RSA decryption with PKCS#1 v1.5, prefer using a library that throws the same generic error for any decryption failure.

import { Injectable } from '@nestjs/common';
import { randomBytes, createDecipheriv, createHash } from 'crypto';

@Injectable()
export class CryptoService {
  // Example using a constant-time comparison after decryption
  async decryptAndVerify(ciphertext: Buffer, privateKeyPem: string): Promise {
    // Use a modern algorithm where possible; if using RSA, prefer OAEP.
    // This is illustrative; ensure your key sizes and padding align with current standards.
    const publicKey = this.extractPublicKey(privateKeyPem); // implement safely
    // In practice, use Node's crypto with OAEP which is less prone to padding oracles.
    const decipher = createDecipheriv('aes-256-gcm', publicKey, iv); // illustrative
    let decoded = decipher.update(ciphertext);
    decoded = Buffer.concat([decoded, decipher.final()]);
    return decoded;
  }

  private extractPublicKey(pem: string): Buffer {
    // Normalize and extract key material safely
    return Buffer.from(pem.replace(/-----[^-]+-----/g, '').replace(/\s+/g, ''), 'base64');
  }
}

2. Safe DynamoDB access patterns

When querying DynamoDB, avoid conditional responses that reveal item existence. Use a consistent response shape and ensure errors from DynamoDB do not bubble up as distinct validation failures.

import { Injectable } from '@nestjs/common';
import { DynamoDBDocumentClient, GetCommand, QueryCommand } from '@aws-sdk/lib-dynamodb';
import { DynamoDB } from '@aws-sdk/client-dynamodb';

@Injectable()
export class UserService {
  private readonly ddb = DynamoDBDocumentClient.from(new DynamoDB({}));

  async getUserByToken(tokenId: string): Promise {
    // Always perform the query regardless of token validity checks earlier
    // Use a GSI if you query by token; ensure the index exists and is provisioned
    const command = new QueryCommand({
      TableName: process.env.USERS_TABLE,
      IndexName: 'tokenIndex',
      KeyConditionExpression: 'token = :token',
      ExpressionAttributeValues: { ':token': tokenId },
    });

    try {
      const response = await this.ddb.send(command);
      // Return a uniform shape; do not distinguish missing vs error via status codes to client
      return {
        found: response.Items && response.Items.length > 0,
        item: response.Items ? response.Items[0] : null,
      };
    } catch (err) {
      // Log the error internally, but return a generic response to avoid signaling
      console.error('DynamoDB error in getUserByToken', err);
      return { found: false, item: null };
    }
  }
}

3. API response hardening

Ensure endpoints that could be targets return the same HTTP status code and similar timing characteristics for valid and invalid inputs. Use generic error messages and avoid detailed validation feedback.

// In your controller
@Post('verify')
async verifyToken(@Body() dto: VerifyDto) {
  const user = await this.userService.getUserByToken(dto.token);
  if (!user.found) {
    // Return a 200 with a generic payload to avoid signaling existence via status
    return { ok: false, message: 'Invalid credentials' };
  }
  // Proceed with authentication logic
  return { ok: true, user };
}

4. Complementary measures

• Rotate keys regularly and use RSA-OAEP instead of PKCS#1 v1.5 where feasible.
• Enable DynamoDB encryption at rest (AWS-managed KMS) and enforce TLS in transit.
• Instrument timing metrics internally to detect anomalous request patterns that could indicate probing.