Vulnerability-Lookup

CVE-2026-75935 (GCVE-0-2026-75935)

Vulnerability from cvelistv5 – Published: 2026-08-18 19:33 – Updated: 2026-08-19 13:06

Title

Memory-amplification denial of service via declared-length preallocation in Amazon ion-java

Summary

Uncontrolled memory allocation in the binary Ion stream cursor in Amazon ion-java before 1.12.0 might allow remote actors to cause a denial of service via a crafted Ion binary document containing a declared-length field that causes excessive heap preallocation. To remediate this issue, users should upgrade to version 1.12.0.

Severity

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

8.7 (High)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

SSVC

Exploitation: none Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-19 13:06 UTC

CWE

CWE-789 - Memory allocation with excessive size value

Assigner

AMZN mitre root

References

3 references

URL	Tags
https://github.com/amazon-ion/ion-java/releases/t…	patch
https://aws.amazon.com/security/security-bulletin…	vendor-advisory
https://github.com/amazon-ion/ion-java/security/a…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version	CPE status
Amazon Ion	Amazon Ion Java	Affected: 1.9.0 , < 1.12.0 (custom) cpe:2.3:a:amazon_ion:amazon_ion_java::::::::

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-75935

Vulnerability from fkie_nvd - Published: 2026-08-18 20:17 - Updated: 2026-08-20 13:01

Severity

7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Summary

References

	URL	Tags
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://aws.amazon.com/security/security-bulletins/2026-083-aws/
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://github.com/amazon-ion/ion-java/releases/tag/v1.12.0
	ff89ba41-3aa1-4d27-914a-91399e9639e5	https://github.com/amazon-ion/ion-java/security/advisories/GHSA-822f-6gg9-whr5

Impacted products

	Vendor	Product	Version

JSON

To clipboard

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    {
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "url": "https://github.com/amazon-ion/ion-java/security/advisories/GHSA-822f-6gg9-whr5"
    }
  ],
  "sourceIdentifier": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
  "vulnStatus": "Awaiting Analysis",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-789"
        }
      ],
      "source": "ff89ba41-3aa1-4d27-914a-91399e9639e5",
      "type": "Secondary"
    }
  ]
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.