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Overview 14 matches · 0 protected · 0 exploited · 0 CISA KEV · 0 public PoC · 0 EPSS ≥ 0.5
14matches 0protected0.0% 0exploited0.0% 0CISA KEV0.0% 0public PoC0.0% 0EPSS ≥ 0.50.0%
Protected by Waratek 0 0.0% Rule in development 0 0.0% Mitigation candidate 0 0.0% No exploit published 14 100.0%
Critical 0 0.0% High 14 100.0% Medium 0 0.0% Low 0 0.0% None 0 0.0% Unknown 0 0.0%
blocked by ARMR today 0 0.0% partly blocked 0 0.0% not blocked 14 100.0% not established 0 0.0% unrecorded 0 0.0%
Exploited 0 0.0% Working exploit published 0 0.0% Proof of concept only 3 21.4% Forecast only 0 0.0% No public exploit 11 78.6%
split by peak 9 / quarter
Q1 25Q2 25Q3 25Q4 25Q1 26Q2 26Q3 26Q4 26
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CVE Severity Published▾ Status Exploitation Description
CVE-2026-102477 High 2026-10-15 No exploit published No public exploit In Eclipse Vert.x versions [4.0.0, 4.5.34] or [5.0.0,5.0.12] or [5.1.0,5.1.9] or [5.2.0, 5.2.0], a gRPC endpoint does have a configurable per message maximum size in bytes, however the maximum size applies to the received bytes. When applied to a compressed message, this only applies to the compressed size and not the actual size of the uncompressed content. This can be used to force a gRPC endpoint to allocate more bytes than the value defined by the configuration, making the endpoint vulnerable to compression bomb attacks. The impacted component is io.vertx:vertx-gprc-server.
CVE-2026-102695 High 2026-10-14 No exploit published No public exploit OmniFaces is a utility library for Faces. In OmniFaces versions before 1.14.3, 2.7.33, 3.14.23, 4.7.12, and 5.4.2, CombinedResourceHandler accepts a structurally valid deflated and base64 encoded combined resource ID without verifying that the server issued it. A forged ID can cause unbounded decoded output, add unique reconstructed IDs to a process-lifetime cache, or combine resources other than stylesheets and scripts. Because jakarta.faces.RESOURCE_EXCLUDES is not applied by this custom resolution path, a forged ID can disclose Facelets source from the webapp resource tree or META-INF/resources. In a deployment configured with the wildcard CDN mapping, a forged ID that selects omnifaces.graphic can also cause a blind, deployment-dependent server-side fetch whose destination derives partly from the request Host header. The flaw does not provide arbitrary file read or establish arbitrary-scheme or arbitrary-destination SSRF. The shared authenticity failure permits memory exhaustion and limited resource disclosure. This issue is fixed in versions 1.14.3, 2.7.33, 3.14.23, 4.7.12, and 5.4.2.
CVE-2026-78384 High 2026-10-09 No exploit published No public exploit CompressionUtils.inflate() decompressed attacker-controlled DEFLATE data with no output-size cap. A small (~KB) crafted payload could expand to gigabytes on the heap. Reachable via JWE decryption when zip=DEF (e.g. JoseSessionTokenProvider with RSA-OAEP key wrap) and via SAML redirect/POST binding token inflation — in both cases decompression happens before/independent of trust validation. Fix: Added a configurable maximum inflated-size cap (default 10 MiB, org.apache.cxf.compression-max-inflated-size system property) to CompressionUtils.inflate(); aborts with DataFormatException once exceeded. Users are recommended to upgrade to versions 4.2.4 or 4.1.9 or 3.6.13, which fix this issue.
CVE-2026-107227 High 2026-10-07 No exploit published No public exploit The AsyncHttpClient (AHC) library allows Java applications to easily execute HTTP requests and asynchronously process HTTP responses. From 2.2.0 until 3.0.14, WebSocket permessage-deflate decompression is unbounded when compression is enabled. The inbound pipeline aggregates compressed frames before WebSocketClientCompressionHandler inflates them, so webSocketMaxFrameSize and webSocketMaxBufferSize do not bound decompressed output. A malicious WebSocket peer can send a small compressed message that expands to a very large Netty buffer and exhausts JVM heap. This issue is fixed in version 3.0.14.
CVE-2026-47321 High 2026-09-21 No exploit published No public exploit The CompressionFilter class uses ZLib to deflate and inflate data sent and received. When we inflate incoming data, the filter does not control the resulting size, and create a buffer no matter what. Some compressed data may have a compression ration greater than 1 thousand, leading to an exhaustion of the application memory, as we don't control the deflated size. The fix adds such a control by allowing the application developer to provide a fixed size limit, which when reached throws an exception. It also allows the user to provide a compression ratio that should not be exceeded, protected the application from small inflated files that inflate in gigantic files, but with a grace limit for the resulting size (1Mb) to avoid false positive (like a very small file inflating with a high ratio, but resulting with a acceptable size, like a few thousands bytes) For application using this feature, it is highly recommended to create the CompressionFilter and to pass the maximum limit as a forth constructor parameter, maxDecompressedSize: public CompressionFilter(final boolean compressInbound, final boolean compressOutbound, final int compressionLevel, final int maxDecompressedSize)Optionally one can also provide a maxDecompressRatio fifth parameter, and a decompressRatioMinSize sixth parameter to allow small inflated files with a high compression ratio to still be accepted. Here are the additional constructor: public CompressionFilter(final boolean compressInbound, final boolean compressOutbound, final int compressionLevel, final int maxDecompressedSize, final long maxDecompressRatio, final long decompressRatioMinSize) Also note that a fluent API has been added to spare the users the pain to call a constructor with that many parameters:  CompressionFilter compressionFilter = new CompressionFilter()     .setCompressionLevel(Zlib.COMPRESSION_MAX)   .setMaxDecompressedSize(1_000_000)   .setMaxDecompressRatio(100).   .setDecompressRatioMinSize(100_000);  Applications using Apache MINA are advised to upgrade and configure their CompressionFilter instance.
CVE-2026-89059 High 2026-09-18 No exploit published Proof of concept only A flaw was found in RESTEasy's IIOImageProvider, which decodes attacker-supplied image request bodies without enforcing any limit on the declared image dimensions or pixel count. A remote, unauthenticated attacker can send a small crafted image declaring enormous dimensions to trigger a very large memory allocation, exhausting the JVM heap and resulting in a denial of service.
CVE-2026-85721 High 2026-09-17 No exploit published No public exploit 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.
CVE-2026-81875 High 2026-09-16 No exploit published Proof of concept only HAPI FHIR is a complete implementation of the HL7 FHIR standard for healthcare interoperability in Java. Prior to version 6.9.12, SHCParser in org.hl7.fhir.r5/src/main/java/org/hl7/fhir/r5/elementmodel/SHCParser.java can consume attacker-controlled Smart Health Card JWT content whose header contains zip: "DEF" and whose small raw-DEFLATE payload expands to a very large value. SHCParser.decodeJWT() passes the decoded payload to SHCParser.inflate(), which accumulates all decompressed bytes in a ByteArrayOutputStream without an output-size limit before JSON parsing, and SHCParser.decompress() contains the same unbounded pattern. An application or validator service that accepts attacker-supplied SHC content can therefore suffer excessive heap allocation, severe garbage-collection pressure, request failure, process instability, or process termination. This issue is fixed in version 6.9.12.
CVE-2026-70469 High 2026-09-16 No exploit published No public exploit 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.
CVE-2026-53659 High 2026-09-14 No exploit published No public exploit http4k is a functional toolkit for Kotlin HTTP applications. Prior to 4.51.0.0, 5.42.0.0, and 6.49.0.0, ServerFilters.GZip, RequestFilters.GunZip, and the underlying Gzip request-body decompression functions impose no limit on decompressed size. An unauthenticated client can send a small gzip-encoded request body that expands to gigabytes, exhausting the JVM heap and denying service to other clients. The fix uses SizeLimitedInputStream to enforce a default 10 MiB limit, causes ServerFilters.GZip and RequestFilters.GunZip to return 413 Request Entity Too Large, and causes other decompression paths to throw SizeLimitExceededException. This issue is fixed in versions 4.51.0.0, 5.42.0.0, and 6.49.0.0.
CVE-2026-54556 High 2026-08-26 No exploit published No public exploit Http4s is a Scala interface for HTTP services. Prior to 0.23.35 and 1.0.0-M47, an unauthenticated HTTP/2 peer can cause an out-of-memory denial of service in the Ember backend with HTTP/2 enabled. The Hpack wrapper in ember-core/shared/src/main/scala/org/http4s/ember/core/h2/Hpack.scala concatenates HEADERS and CONTINUATION frame fragments and decodes them into a single List, but maxHeaderSize accounting does not include indexed headers or HPACK per-header overhead. A small compressed header block can therefore expand into a much larger decoded representation that remains in memory for processing. Servers exposed to untrusted HTTP/2 traffic and clients directed to an untrusted HTTP/2 server are affected, and concurrent malicious connections can exhaust the process heap. This issue is fixed in versions 0.23.35 and 1.0.0-M47.
CVE-2026-68981 High 2026-08-03 No exploit published No public exploit Apache NiFi 1.5.0 through 2.10.0 support gzip-encoded HTTP requests for the application REST API using a Jersey encoding filter. The framework enforced a configurable maximum request size on the compressed payload rather than the decompressed output, allowing a malicious client to send crafted requests that could consume excessive amounts of memory. Upgrading to Apache NiFi 2.11.0 is the recommended mitigation, which relocates response compression to Jetty Server and disables decompression of gzip-encoded HTTP requests.
CVE-2026-48586 High 2026-07-27 No exploit published No public exploit Improper Handling of Highly Compressed Data (Data Amplification) vulnerability in Apache Thrift C++, Java, Python, Go, D, C/GLib bindings. This issue affects Apache Thrift: before 0.24.0. Users are recommended to upgrade to version 0.24.0, which fixes the issue.
CVE-2024-7765 High 2025-03-20 No exploit published Proof of concept only In h2oai/h2o-3 version 3.46.0.2, a vulnerability exists where uploading and repeatedly parsing a large GZIP file can cause a denial of service. The server becomes unresponsive due to memory exhaustion and a large number of concurrent slow-running jobs. This issue arises from the improper handling of highly compressed data, leading to significant data amplification.