A flaw in V8s string hashing mechanism causes integer-like strings to be hashed to their numeric value, making hash collisions trivially predictable. By crafting a request that causes many such collisions in V8s internal string table, an attacker can significantly degrade performance of the Node.js process.
The most common trigger is any endpoint that calls JSON.parse() on attacker-controlled input, as JSON parsing automatically internalizes short strings into the affected hash table.
This vulnerability affects 20.x, 22.x, 24.x, and 25.x.
Weakness
The product uses an algorithm that produces a digest (output value) that does not meet security expectations for a hash function that allows an adversary to reasonably determine the original input (preimage attack), find another input that can produce the same hash (2nd preimage attack), or find multiple inputs that evaluate to the same hash (birthday attack).
Affected Software
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Red Hat Enterprise Linux 10 | RedHat | nodejs24-1:24.14.1-2.el10_1 | * |
| Red Hat Enterprise Linux 8 | RedHat | nodejs:24-8100020260408131901.6d880403 | * |
| Red Hat Enterprise Linux 9 | RedHat | nodejs:24-9070020260402152654.rhel9 | * |
| Red Hat Hardened Images | RedHat | nodejs24-main-24.14.1-4.hum1 | * |
| Red Hat Hardened Images | RedHat | nodejs25-main-25.9.0-1.hum1 | * |
| Red Hat Hardened Images | RedHat | nodejs20-main-20.20.0-7.1.hum1 | * |
| Red Hat Hardened Images | RedHat | nodejs22-main-22.22.0-1.3.hum1 | * |
| Nodejs | Ubuntu | esm-apps/xenial | * |
| Nodejs | Ubuntu | upstream | * |
Extended Description
A hash function is defined as an algorithm that maps arbitrarily sized data into a fixed-sized digest (output) such that the following properties hold:
Building on this definition, a cryptographic hash function must also ensure that a malicious actor cannot leverage the hash function to have a reasonable chance of success at determining any of the following:
What is regarded as “reasonable” varies by context and threat model, but in general, “reasonable” could cover any attack that is more efficient than brute force (i.e., on average, attempting half of all possible combinations). Note that some attacks might be more efficient than brute force but are still not regarded as achievable in the real world. Any algorithm that does not meet the above conditions will generally be considered weak for general use in hashing. In addition to algorithmic weaknesses, a hash function can be made weak by using the hash in a security context that breaks its security guarantees. For example, using a hash function without a salt for storing passwords (that are sufficiently short) could enable an adversary to create a “rainbow table” [REF-637] to recover the password under certain conditions; this attack works against such hash functions as MD5, SHA-1, and SHA-2.
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.