The product 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.

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:

The amount of CPU time required to compute the hash ("stretching")
The amount of memory required to compute the hash ("memory-hard" operations)
Including a random value, along with the password, as input to the hash computation ("salting")
Given a hash, there is no known way of determining an input (e.g., a password) that produces this hash value, other than by guessing possible inputs ("one-way" hashing)
Relative to the number of all possible hashes that can be generated by the scheme, there is a low likelihood of producing the same hash for multiple different inputs ("collision resistance")

Note that the security requirements for the product 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.

Primary

Architecture and Design
REALIZATION: This weakness is caused during implementation of an architectural security tactic.

Access Control
Bypass Protection Mechanism
Gain Privileges or Assume Identity
If an attacker can gain access to the hashes, then the lack of sufficient computational effort will make it easier to conduct brute force attacks using techniques such as rainbow tables, or specialized hardware such as GPUs, which can be much faster than general-purpose CPUs for computing hashes.

Automated Static Analysis - Binary or Bytecode

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Bytecode Weakness Analysis - including disassembler + source code weakness analysis
Binary Weakness Analysis - including disassembler + source code weakness analysis

SOAR Partial

Manual Static Analysis - Binary or Bytecode

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Binary / Bytecode disassembler - then use manual analysis for vulnerabilities & anomalies

SOAR Partial

Manual Static Analysis - Source Code

According to SOAR [REF-1479], the following detection techniques may be useful:

Highly cost effective:

Focused Manual Spotcheck - Focused manual analysis of source
Manual Source Code Review (not inspections)

High

Automated Static Analysis - Source Code

According to SOAR [REF-1479], the following detection techniques may be useful:

Highly cost effective:

Source code Weakness Analyzer
Context-configured Source Code Weakness Analyzer

High

Automated Static Analysis

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Configuration Checker

SOAR Partial

Architecture or Design Review

According to SOAR [REF-1479], the following detection techniques may be useful:

Highly cost effective:

Formal Methods / Correct-By-Construction

Cost effective for partial coverage:

Inspection (IEEE 1028 standard) (can apply to requirements, design, source code, etc.)

High

Architecture and Design

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.

High

Implementation
Architecture and Design
When using industry-approved techniques, use them correctly. Don't cut corners by skipping resource-intensive steps (CWE-325). These steps are often essential for preventing common attacks.

In this example, a new user provides a new username and password to create an account. The program hashes the new user's password then stores it in a database.

def storePassword(userName,Password):hasher = hashlib.new('md5')hasher.update(Password)hashedPassword = hasher.digest()

# UpdateUserLogin returns True on success, False otherwise
return updateUserLogin(userName,hashedPassword)

While it is good to avoid storing a cleartext password, the program does not provide a salt to the hashing function, thus increasing the chances of an attacker being able to reverse the hash and discover the original password if the database is compromised.
Fixing this is as simple as providing a salt to the hashing function on initialization:

def storePassword(userName,Password):hasher = hashlib.new('md5',b'SaltGoesHere')hasher.update(Password)hashedPassword = hasher.digest()

# UpdateUserLogin returns True on success, False otherwise
return updateUserLogin(userName,hashedPassword)

Note that regardless of the usage of a salt, the md5 hash is no longer considered secure, so this example still exhibits CWE-327.

CVE-2008-1526
Router does not use a salt with a hash, making it easier to crack passwords.
https://www.cve.org/CVERecord?id=CVE-2008-1526

CVE-2006-1058
Router does not use a salt with a hash, making it easier to crack passwords.
https://www.cve.org/CVERecord?id=CVE-2006-1058

CVE-2008-4905
Blogging software uses a hard-coded salt when calculating a password hash.
https://www.cve.org/CVERecord?id=CVE-2008-4905

CVE-2002-1657
Database server uses the username for a salt when encrypting passwords, simplifying brute force attacks.
https://www.cve.org/CVERecord?id=CVE-2002-1657

CVE-2001-0967
Server uses a constant salt when encrypting passwords, simplifying brute force attacks.
https://www.cve.org/CVERecord?id=CVE-2001-0967

CVE-2005-0408
chain: product generates predictable MD5 hashes using a constant value combined with username, allowing authentication bypass.
https://www.cve.org/CVERecord?id=CVE-2005-0408

Allowed
This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

CWE Content Team
MITRE
2013-01-28
2.4
2013-02-21
Created with input from members of the secure password hashing community.

CWE Content Team
MITRE
2014-02-18
updated Potential_Mitigations, References

CWE Content Team
MITRE
2014-07-30
updated Detection_Factors

CWE Content Team
MITRE
2017-01-19
updated Relationships

CWE Content Team
MITRE
2017-11-08
updated Modes_of_Introduction, References, Relationships

CWE Content Team
MITRE
2019-01-03
updated Description

CWE Content Team
MITRE
2019-06-20
updated Related_Attack_Patterns, Relationships

CWE Content Team
MITRE
2020-02-24
updated Relationships

CWE Content Team
MITRE
2021-10-28
updated Relationships

CWE Content Team
MITRE
2023-01-31
updated Description

CWE Content Team
MITRE
2023-04-27
updated References, Relationships

CWE Content Team
MITRE
2023-06-29
updated Mapping_Notes, Relationships

CWE Content Team
MITRE
2024-02-29
4.14
2024-02-29
updated Demonstrative_Examples

CWE Content Team
MITRE
2025-09-09
4.18
2025-09-09
updated Detection_Factors, References

CWE Content Team
MITRE
2025-12-11
4.19
2025-12-11
updated Relationships