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.
View on MITREMany 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.
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.
No mitigation information available for this CWE.
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)
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
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.)
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.
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.
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.
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.
Router does not use a salt with a hash, making it easier to crack passwords.
View DetailsRouter does not use a salt with a hash, making it easier to crack passwords.
View DetailsDatabase server uses the username for a salt when encrypting passwords, simplifying brute force attacks.
View DetailsServer uses a constant salt when encrypting passwords, simplifying brute force attacks.
View Detailschain: product generates predictable MD5 hashes using a constant value combined with username, allowing authentication bypass.
View DetailsCWE-916: Use of Password Hash With Insufficient Computational Effort is a Common Weakness Enumeration (CWE) entry maintained by MITRE. 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.
If exploited, CWE-916 (Use of Password Hash With Insufficient Computational Effort) it can compromise Access Control, leading to outcomes such as Bypass Protection Mechanism and Gain Privileges or Assume Identity.
CWE-916 can be detected using Manual Static Analysis - Source Code, Automated Static Analysis - Source Code and Architecture or Design Review. Combining automated tooling with manual review typically yields the best coverage.
CWE-916 commonly affects Not Language-Specific. Note that weaknesses are often language-agnostic patterns, so secure coding practices apply broadly.
MITRE documents real CVEs mapped to CWE-916, including CVE-2008-1526, CVE-2006-1058, CVE-2008-4905, CVE-2002-1657 and CVE-2001-0967. You can look up the full details of each CVE, including CVSS scores and remediation guidance, on our CVE Lookup tool.
A CWE (Common Weakness Enumeration) like CWE-916 describes a category of software weakness — the underlying flaw type. A CVE (Common Vulnerabilities and Exposures) identifies a specific, real-world vulnerability in a particular product. In short, a CWE is the kind of mistake, and a CVE is an instance of that mistake being found in software.