CVE-2026-85721 (GCVE-0-2026-85721)
Vulnerability from cvelistv5 – Published: 2026-09-17 15:55 – Updated: 2026-09-17 17:22
VLAI
EPSS
VEX
Title
AsyncHttpClient: Unbounded HTTP/1.1 response decompression enables a decompression-bomb denial of service
Summary
The AsyncHttpClient (AHC) library allows Java applications to easily execute HTTP requests and asynchronously process HTTP responses. From 2.0.0 until 2.16.1 and 3.0.12, automatic response decompression on the HTTP/1.1 path uses ChannelManager.newHttpContentDecompressor() to install Http1ContentDecompressor without a cumulative output-size limit. A hostile or compromised server, or an attacker who can alter a response in transit, can send a small gzip, deflate, or snappy response that expands across chunks until the client exhausts its heap and raises OutOfMemoryError; brotli and zstd are also affected when their optional codecs are present. In versions 3.0.8 through 3.0.10, the HTTP/2 decompressor is also unbounded, so switching protocols does not mitigate the issue on those releases. A limit applied to each decode call is insufficient because the response can be delivered as many small chunks, so the fixed implementation tracks total decompressed bytes for the whole response. This issue is fixed in versions 2.16.1 and 3.0.12.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 17:22 UTC
CWE
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/AsyncHttpClient/async-http-cli… | x_refsource_CONFIRM |
| https://github.com/AsyncHttpClient/async-http-cli… | x_refsource_MISC |
| https://github.com/AsyncHttpClient/async-http-cli… | x_refsource_MISC |
| https://github.com/AsyncHttpClient/async-http-cli… | x_refsource_MISC |
| https://github.com/AsyncHttpClient/async-http-cli… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| AsyncHttpClient | async-http-client |
Affected:
>= 2.0.0, < 2.16.1
Affected: >= 3.0.0, < 3.0.12 |
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.
- 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.
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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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