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CVE-2026-47891 – Denial of Service via maxInMemorySize Bypass – Spring Framework (multiple versions)

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Summary

CVE-2026-47891 is a resource allocation vulnerability affecting Spring Framework. In Spring WebFlux applications that use the Aalto XML processor to parse XML input, the maxInMemorySize limit is not correctly enforced, allowing unbounded memory consumption while processing XML content. The issue is rated CRITICAL with a CVSS v3.1 base score of 9.8.

Technical details

  • Root cause: when a Spring WebFlux application relies on the Aalto XML processor (via Jaxb2Decoder) to decode XML request bodies, the configured maxInMemorySize restriction, intended to cap the amount of data buffered in memory, is bypassed.
  • Trigger condition: an XML payload is submitted to an endpoint that decodes it through the affected WebFlux/Aalto XML decoding path.
  • Attack vector: network-based; no authentication or user interaction is required to submit the crafted request.
  • Impact: because the memory limit is not honored, an attacker can cause excessive memory allocation, leading to resource exhaustion and potential denial of service on the affected application.

Affected software

  • Spring Framework 7.0.0 through 7.0.8
  • Spring Framework 6.2.0 through 6.2.19
  • Spring Framework 6.1.0 through 6.1.28
  • Spring Framework 6.0.0 through 6.0.30
  • Spring Framework 5.3.0 through 5.3.49
  • Spring Framework 5.2.25.RELEASE and earlier

Severity

CVSS v3.1 Base Score: 9.8 (CRITICAL)
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Mitigation and recommended actions

  • Immediate: upgrade to the fixed release for your branch — Spring Framework 7.0.9 (open source); enterprise-supported customers on older lines should apply 7.0.8.1, 6.2.20, 6.1.29, 6.0.31, 5.3.50, or 5.2.26 as applicable to their support entitlement.
  • If no patch can be applied immediately: review any WebFlux endpoints that decode XML via the Aalto processor and restrict or disable XML payload decoding where not required, and apply external request-size/network-level controls to limit exposure until the upgrade is completed.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Raw response body: Caused by: org.springframework.data. (also matches at: org.springframework.data.), org.springframework.data.mapping.MappingException, org.springframework.data.repository.RepositoryCreationException, /WEB-INF/lib/spring-data-commons-<version>.jar, spring-data-commons-<version>.jar, /META-INF/MANIFEST.MF ... spring-data-commons

References

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How IONIX’s External Exposure Management Platform Detects and Validates
Zero-Days to Shrink MTTR

1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

Execute Exploit Validations

By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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  • Identification of potentially exposed assets to this CVE
  • Confirmation of verified exploitable assets
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