CVE-2026-70469 (GCVE-0-2026-70469)
Vulnerability from cvelistv5 – Published: 2026-09-16 19:38 – Updated: 2026-09-16 20:12
VLAI
EPSS
VEX
Title
Apache NiFi: Improper Handling of Case Sensitivity for Content-Encoding in HTTP Requests
Summary
Apache NiFi 2.11.0 disabled support for gzip-encoded HTTP requests for the application REST API and rejected requests that included the standard Content-Encoding header indicating gzip encoding. The framework enforcement filter did not check multiple instances of the Content-Encoding header and did not reject non-standard identifiers for gzip encoding, allowing a malicious client to send crafted requests that could consume excessive amounts of memory. Upgrading to Apache NiFi 2.12.0 is the recommended mitigation, which disables decompression of gzip-encoded HTTP requests regardless of header number or encoding identifiers.
Severity
No CVSS data available.
CWE
- CWE-409 - Improper Handling of Highly Compressed Data (Data Amplification)
Assigner
References
2 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Apache Software Foundation | Apache NiFi |
Affected:
2.11.0
(semver)
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
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.
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- 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.
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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.
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.
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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.
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