CWE-916: Use of Password Hash With Insufficient Computational Effort

How it's found

Use of Password Hash With Insufficient Computational Effort is typically found by tracing untrusted input from where it enters the system to the point where it is used without the check or neutralization this weakness describes, combining manual code review with dynamic testing.

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 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.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")

Vulnerable vs. safe

# UpdateUserLogin returns True on success, False otherwisehasher = hashlib.new('md5')hasher.update(Password)hashedPassword = hasher.digest()return updateUserLogin(userName,hashedPassword)def storePassword(userName,Password):

# UpdateUserLogin returns True on success, False otherwisehasher = hashlib.new('md5',b'SaltGoesHere')hasher.update(Password)hashedPassword = hasher.digest()return updateUserLogin(userName,hashedPassword)def storePassword(userName,Password):

Consequences

• 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.

Mitigations

• 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.
• 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.

Where this fits in a TurboPentest engagement

This weakness is not covered by the automated black-box pentest. IntegSec pentesters cover it in a manual engagement.

Frequently asked questions

Related CWEs

• Base weakness
  CWE-328: Use of Weak Hash
• Class weakness
  CWE-327: Use of a Broken or Risky Cryptographic Algorithm