Bleichenbacher Attack in Adonisjs with Dynamodb

A Bleichenbacher attack is a cryptographic padding oracle technique originally described against RSA PKCS#1 v1.5–based encryption or signature schemes. In the context of an AdonisJS application using Amazon DynamoDB as a persistence layer, the vulnerability arises when error handling during decryption or token verification leaks information about padding validity. If AdonisJS code calls a KMS or library that performs decryption (for example, to validate JWTs, API tokens, or encrypted identifiers stored in DynamoDB), and the application distinguishes between a padding error and other errors, an attacker can iteratively send crafted ciphertexts and observe different responses or timing differences. DynamoDB itself does not perform decryption; it stores encrypted blobs or keys, but the way AdonisJS processes, logs, and reacts to decryption failures determines whether the service becomes an oracle.

Consider an endpoint that accepts an encrypted identifier, decrypts it using Node.js crypto with a key managed by AWS KMS, and then looks up the corresponding item in DynamoDB. If the endpoint returns a 400 for malformed input and a 401 specifically when padding validation fails, the distinction acts as an oracle. An attacker can craft adaptive chosen-ciphertext queries, submitting modified ciphertexts and observing status code differences to gradually recover plaintext. In AdonisJS, this can surface when using packages that perform JWT verification or custom encrypted payloads without constant-time error handling. DynamoDB stores the encrypted values, but the runtime behavior of the framework and its crypto dependencies determines whether the service participates in the oracle. Logging stack traces that include padding details, inconsistent error messages for decryption versus lookup failures, or timing differences in DynamoDB read latency combined with cryptographic processing can unintentionally amplify the signal available to an attacker.

Specific AdonisJS patterns that increase risk include using legacy crypto modules, not validating integrity before processing, and exposing stack traces in development or misconfigured production logs. If the application decrypts a user-controlled field retrieved from DynamoDB and compares MACs or padding in non-constant time, the framework inadvertently exposes a decision path that can be probed. The DynamoDB table may contain encrypted secrets or tokens; if the decryption routine is invoked with attacker-supplied data and the framework’s error handling is not uniform, the combination of AdonisJS, the crypto library, and DynamoDB-stored ciphertext creates a practical Bleichenbacher vector.

Remediation focuses on ensuring that error handling during decryption and DynamoDB interactions does not leak distinguishability. In AdonisJS, use a consistent error path for all failures related to token or ciphertext validation, and avoid branching logic that depends on padding correctness. Always use Node.js crypto APIs that verify integrity in constant time, and treat any decryption failure as an opaque invalid input.

Example: decrypting a payload retrieved from DynamoDB with uniform error handling.

import { createCipheriv, createDecipheriv, randomBytes } from 'node:crypto';

const algorithm = 'aes-256-gcm';
const key = Buffer.from(process.env.ENCRYPTION_KEY!, 'hex');

export function encryptData(plaintext: string): { ciphertext: string; iv: string; tag: string } {
  const iv = randomBytes(12);
  const cipher = createCipheriv(algorithm, key, iv);
  let encrypted = cipher.update(plaintext, 'utf8');
  encrypted = Buffer.concat([encrypted, cipher.final()]);
  return {
    ciphertext: encrypted.toString('base64'),
    iv: iv.toString('base64'),
    tag: cipher.getAuthTag().toString('base64'),
  };
}

export function decryptData(ciphertextBase64: string, ivBase64: string, tagBase64: string): string {
  try {
    const ciphertext = Buffer.from(ciphertextBase64, 'base64');
    const iv = Buffer.from(ivBase64, 'base64');
    const tag = Buffer.from(tagBase64, 'base64');
    const decipher = createDecipheriv(algorithm, key, iv);
    decipher.setAuthTag(tag);
    let decrypted = decipher.update(ciphertext);
    decrypted = Buffer.concat([decrypted, decipher.final()]);
    return decrypted.toString('utf8');
  } catch (err) {
    // Always return the same generic error to avoid leaking padding or MAC details
    throw new Error('invalid_request');
  }
}

DynamoDB usage with decryption in AdonisJS, ensuring that lookup errors and decryption errors are not distinguishable.

import { DynamoDBClient, GetItemCommand } from '@aws-sdk/client-dynamodb';
import { unmarshall } from '@aws-sdk/util-dynamodb';
import { decryptData } from './crypto';

const ddb = new DynamoDBClient({});

export async function getItemByEncryptedId(encryptedId: string) {
  try {
    const id = decryptData(encryptedId, encryptedId.substring(0, 24), encryptedId.substring(24));
    const cmd = new GetItemCommand({
      TableName: process.env.DYNAMO_TABLE!,
      Key: { pk: { S: id } },
    });
    const res = await ddb.send(cmd);
    if (!res.Item) {
      throw new Error('not_found');
    }
    return unmarshall(res.Item);
  } catch (err) {
    // Treat decryption failures, parse errors, and missing items uniformly
    throw new Error('not_found');
  }
}

Additional hardening steps include disabling verbose stack traces in production, using environment-controlled logging levels, and applying constant-time comparison libraries when handling MACs or signatures. For JWTs, prefer libraries that validate with built-in constant-time checks and avoid manual parsing that could expose padding errors. If you use middleBrick, you can verify that your endpoints do not leak distinguishability through its scans; the CLI allows you to run checks from the terminal with middlebrick scan <url>, and the GitHub Action can enforce security thresholds in CI/CD pipelines.