CVE-2026-18401

NVD	https://nvd.nist.gov/vuln/detail/CVE-2026-18401
CWE	https://cwe.mitre.org/data/definitions/770.html

The non-blocking (asynchronous) JSON parser in jackson-core does not enforce the maxNumberLength constraint defined in StreamReadConstraints (default: 1000 characters). An attacker able to submit JSON to an application that uses the async parser API can supply a number token of arbitrary length, leading to excessive memory allocation and potential CPU exhaustion, resulting in a denial of service.

The synchronous parser enforces this limit correctly, so the constraint is applied inconsistently depending on which parsing API the application uses.

Root cause: the async parsing path in NonBlockingUtf8JsonParserBase and related classes never invokes the number length validation methods. Number parsing methods such as _finishNumberIntegralPart() accumulate digits into the TextBuffer without any length check, then call _valueComplete() to finalize the token. _valueComplete() does not call resetInt() or resetFloat(), which are the methods in ParserBase where validateIntegerLength() and validateFPLength() are performed. Because that validation step is skipped, maxNumberLength is never enforced on the async code path.

Impact: an attacker sending a JSON document containing an arbitrarily long number to an application using the async parser (for example a Spring WebFlux or other reactive application) can cause unbounded allocation in the TextBuffer and an OutOfMemoryError. If the application subsequently calls getBigIntegerValue() or getDecimalValue(), the JVM may additionally be tied up in O(n^2) BigInteger parsing, causing CPU-based denial of service.

No privileges or user interaction beyond the ability to submit data for parsing are required.

This issue affects com.fasterxml.jackson.core:jackson-core from version 2.15.0 through 2.18.5 and from 2.19.0 through 2.21.0, and tools.jackson.core:jackson-core from 3.0.0 through 3.0.x.

Versions prior to 2.15.0 are not affected, because StreamReadConstraints – which defines the maxNumberLength setting – was first introduced in jackson-core 2.15.0, so no such constraint exists to be bypassed in earlier releases. Note that GHSA-72hv-8253-57qq records the lower bound of the affected 2.x range as 2.0.0.

Weakness

The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.

Potential Mitigations

Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
Mitigation of resource exhaustion attacks requires that the target system either:
The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.
The second solution can be difficult to effectively institute – and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery.
Ensure that all failures in resource allocation place the system into a safe posture.
Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems.
When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users.
Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).

Allocation of Resources Without Limits or Throttling

Weakness

Potential Mitigations

References

CVE-2026-18401

Allocation of Resources Without Limits or Throttling

Weakness

Potential Mitigations

Related Attack Patterns

References