CVE Vulnerabilities

CVE-2022-24041

Use of Password Hash With Insufficient Computational Effort

Published: May 10, 2022 | Modified: May 20, 2022
CVSS 3.x
6.5
MEDIUM
Source:
NVD
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
CVSS 2.x
4 MEDIUM
AV:N/AC:L/Au:S/C:P/I:N/A:N
RedHat/V2
RedHat/V3
Ubuntu

A vulnerability has been identified in Desigo DXR2 (All versions < V01.21.142.5-22), Desigo PXC3 (All versions < V01.21.142.4-18), Desigo PXC4 (All versions < V02.20.142.10-10884), Desigo PXC5 (All versions < V02.20.142.10-10884). The web application stores the PBKDF2 derived key of users passwords with a low iteration count. An attacker with user profile access privilege can retrieve the stored password hashes of other accounts and then successfully perform an offline cracking attack and recover the plaintext passwords of other users.

Weakness

The software generates a hash for a password, but it uses a scheme that does not provide a sufficient level of computational effort that would make password cracking attacks infeasible or expensive.

Extended Description

Many password storage mechanisms compute a hash and store the hash, instead of storing the original password in plaintext. In this design, authentication involves accepting an incoming password, computing its hash, and comparing it to the stored hash. Many hash algorithms are designed to execute quickly with minimal overhead, even cryptographic hashes. However, this efficiency is a problem for password storage, because it can reduce an attacker’s workload for brute-force password cracking. If an attacker can obtain the hashes through some other method (such as SQL injection on a database that stores hashes), then the attacker can store the hashes offline and use various techniques to crack the passwords by computing hashes efficiently. Without a built-in workload, modern attacks can compute large numbers of hashes, or even exhaust the entire space of all possible passwords, within a very short amount of time, using massively-parallel computing (such as cloud computing) and GPU, ASIC, or FPGA hardware. In such a scenario, an efficient hash algorithm helps the attacker. There are several properties of a hash scheme that are relevant to its strength against an offline, massively-parallel attack:

Note that the security requirements for the software may vary depending on the environment and the value of the passwords. Different schemes might not provide all of these properties, yet may still provide sufficient security for the environment. Conversely, a solution might be very strong in preserving one property, which still being very weak for an attack against another property, or it might not be able to significantly reduce the efficiency of a massively-parallel attack.

Potential Mitigations

  • Use an adaptive hash function that can be configured to change the amount of computational effort needed to compute the hash, such as the number of iterations (“stretching”) or the amount of memory required. Some hash functions perform salting automatically. These functions can significantly increase the overhead for a brute force attack compared to intentionally-fast functions such as MD5. For example, rainbow table attacks can become infeasible due to the high computing overhead. Finally, since computing power gets faster and cheaper over time, the technique can be reconfigured to increase the workload without forcing an entire replacement of the algorithm in use.
  • Some hash functions that have one or more of these desired properties include bcrypt [REF-291], scrypt [REF-292], and PBKDF2 [REF-293]. While there is active debate about which of these is the most effective, they are all stronger than using salts with hash functions with very little computing overhead.
  • Note that using these functions can have an impact on performance, so they require special consideration to avoid denial-of-service attacks. However, their configurability provides finer control over how much CPU and memory is used, so it could be adjusted to suit the environment’s needs.

References