CWE-789 CVEs · Protected by Waratek

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Overview 6 matches · 0 exploited · 0 CISA KEV · 0 public PoC · 0 EPSS ≥ 0.5

6matches 0exploited0.0% 0CISA KEV0.0% 0public PoC0.0% 0EPSS ≥ 0.50.0%

Protected by Waratek 6 100.0% Rule in development 0 0.0% Mitigation candidate 0 0.0% No exploit published 0 0.0%

Critical 0 0.0% High 5 83.3% Medium 0 0.0% Low 0 0.0% None 0 0.0% Unknown 1 16.7%

blocked by ARMR today 6 100.0% partly blocked 0 0.0% not blocked 0 0.0% not established 0 0.0% unrecorded 0 0.0%

Exploited 0 0.0% Working exploit published 0 0.0% Proof of concept only 1 16.7% Forecast only 0 0.0% No public exploit 5 83.3%

split by StatusSeverityProtectionExploit maturity peak 2 / quarter

Q4 231 Protected by Waratek1 High1 blocked by ARMR today1 No public exploit1 Q1 240 Q2 240 Q3 240 Q4 240 Q1 250 Q2 250 Q3 250 Q4 250 Q1 261 Protected by Waratek1 High1 blocked by ARMR today1 Proof of concept only1 Q2 261 Protected by Waratek1 High1 blocked by ARMR today1 No public exploit1 Q3 261 Protected by Waratek1 High1 blocked by ARMR today1 No public exploit1 Q4 262 Protected by Waratek2 High1 Unknown1 blocked by ARMR today2 No public exploit2

Q4 23Q2 24Q4 24Q2 25Q4 25Q2 26Q4 26

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CVE	Severity	Published▾	Status	Exploitation	Description
CVE-2026-122096	Unknown	2026-11-11	Protected by Waratek	No public exploit	Uncontrolled Resource Consumption vulnerability in the sketch deserialization of Apache DataSketches Java. Several deserializers trusted sizes and counts declared in the serialized input. A small crafted input could make an application allocate an excessive amount of memory, leading to an OutOfMemoryError. A crafted string passed to FrequentLongsSketch.getInstance(String) could also make deserialization, or a later update, loop indefinitely. Only applications that deserialize sketches from untrusted sources are affected. This issue affects Apache DataSketches Java: through 9.0.0. Users are recommended to upgrade to version 9.1.0, which fixes the issue.
CVE-2026-111551	High	2026-10-28	Protected by Waratek	No public exploit	Several Kafka deserialization paths read a size or count field from input data and use it directly to allocate an array, collection, or byte buffer, with no check against the bytes actually available. A small crafted input can therefore declare a very large size and force a multi-gigabyte allocation. The affected paths are, in Kafka Streams, ProcessorRecordContext (header count), ChangedDeserializer (varint value length), CombinedKeySchema (foreign-key length, which could also produce a negative size), ProcessorMetadata (key length), and AssignmentInfo group-assignment decoding; and, in the clients library, ListDeserializer (used by ListSerde). Impact: An attacker can trigger the oversized allocation and raise an OutOfMemoryError that crashes the affected consumer or Kafka Streams thread; the broker and the rest of the cluster are unaffected. The reachable surface depends on the path: writing a crafted record to a Streams application's internal changelog or repartition topics, or committing crafted offset metadata to its consumer group (Streams internal paths); writing a crafted record to any topic that a consumer reads with ListSerde (ListDeserializer); or, as a member of a Streams consumer group, returning crafted assignment data when elected leader (AssignmentInfo). Because a crafted record remains in its topic, the error can recur on restart. The record size limit (max.message.bytes) does not help, because the allocation is driven by a size field rather than by the input's length. Mitigation: Upgrade to Apache Kafka 4.2.2, 4.3.2, or 4.4.0. Where authorization is enabled, restrict write access to each application's internal topics and consumer group to its own principal, and restrict who can produce to topics that are consumed with ListSerde. Resolution: From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: these deserialization paths validate the declared size against the remaining input before allocating and reject input that fails the check.
CVE-2026-67211	High	2026-09-11	Protected by Waratek	No public exploit	OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer Versions Affected: - 3.0.0-M4 - 3.0.0-M5 (The opennlp-spellcheck extension was introduced in 3.0.0-M4. Releases 1.x and 2.x do not contain the affected code.) Description: The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields (unigramCount and bigramCount) from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which either count field is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries. The oversized backing array is allocated on the first put() into that map, requesting 4–8 GB depending on whether compressed oops are in effect, and the load fails with an OutOfMemoryError. Because the count fields sit immediately after a fixed-size header (magic, format version, three UTF strings, the configuration fields, and the edit-distance identifier) the attacker pays no meaningful size cost to weaponize a payload: a file of well under 100 bytes plus a single real entry is sufficient to crash a JVM that loads it. Any code path that deserializes a SymSpell model is affected, including SymSpellModels.deserialize(InputStream), SymSpellModels.fromBytes(byte[]), classpath model loading via SymSpellModelResolver.resolveByLanguage(String), the CorrectTextTool command-line tool, and model-archive loading through the registered ArtifactSerializer. The opennlp-spellcheck extension ships in the official OpenNLP binary distribution. The practical impact is denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins. Mitigation: - 3.x users should upgrade to 3.0.0-M6. Note: The fix applies an upper bound to both count fields, checked before the map is pre-sized; counts that are negative or exceed the bound cause an IOException to be thrown and the read to fail fast with no large allocation. The bound is the existing AbstractModelReader.MAX_ENTRIES limit introduced earlie, which the current change promotes to public visibility so that serializers implementing their own binary format can share it. The default bound is 10,000,000, which is well above the entry counts of legitimate SymSpell dictionaries but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load larger dictionaries can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Note that this property is shared with the model-reader limit and raising it relaxes both. Users who cannot upgrade immediately should treat all SymSpell .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.
CVE-2026-42440	High	2026-05-04	Protected by Waratek	No public exploit	OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 before 2.5.9 before 3.0.0-M3 Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins. Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.
CVE-2026-21452	High	2026-01-02	Protected by Waratek	Proof of concept only	MessagePack for Java is a serializer implementation for Java. A denial-of-service vulnerability exists in versions prior to 0.9.11 when deserializing .msgpack files containing EXT32 objects with attacker-controlled payload lengths. While MessagePack-Java parses extension headers lazily, it later trusts the declared EXT payload length when materializing the extension data. When ExtensionValue.getData() is invoked, the library attempts to allocate a byte array of the declared length without enforcing any upper bound. A malicious .msgpack file of only a few bytes can therefore trigger unbounded heap allocation, resulting in JVM heap exhaustion, process termination, or service unavailability. This vulnerability is triggered during model loading / deserialization, making it a model format vulnerability suitable for remote exploitation. The vulnerability enables a remote denial-of-service attack against applications that deserialize untrusted .msgpack model files using MessagePack for Java. A specially crafted but syntactically valid .msgpack file containing an EXT32 object with an attacker-controlled, excessively large payload length can trigger unbounded memory allocation during deserialization. When the model file is loaded, the library trusts the declared length metadata and attempts to allocate a byte array of that size, leading to rapid heap exhaustion, excessive garbage collection, or immediate JVM termination with an OutOfMemoryError. The attack requires no malformed bytes, user interaction, or elevated privileges and can be exploited remotely in real-world environments such as model registries, inference services, CI/CD pipelines, and cloud-based model hosting platforms that accept or fetch .msgpack artifacts. Because the malicious file is extremely small yet valid, it can bypass basic validation and scanning mechanisms, resulting in complete service unavailability and potential cascading failures in production systems. Version 0.9.11 fixes the vulnerability.
CVE-2023-3171	High	2023-12-27	Protected by Waratek	No public exploit	A flaw was found in EAP-7 during deserialization of certain classes, which permits instantiation of HashMap and HashTable with no checks on resources consumed. This issue could allow an attacker to submit malicious requests using these classes, which could eventually exhaust the heap and result in a Denial of Service.

CVE

Severity

Published▾

Status

Exploitation

Description

CVE-2026-122096

Unknown

2026-11-11

Protected by Waratek

No public exploit

Uncontrolled Resource Consumption vulnerability in the sketch deserialization of Apache DataSketches Java. Several deserializers trusted sizes and counts declared in the serialized input. A small crafted input could make an application allocate an excessive amount of memory, leading to an OutOfMemoryError. A crafted string passed to FrequentLongsSketch.getInstance(String) could also make deserialization, or a later update, loop indefinitely. Only applications that deserialize sketches from untrusted sources are affected. This issue affects Apache DataSketches Java: through 9.0.0. Users are recommended to upgrade to version 9.1.0, which fixes the issue.

CVE-2026-111551

High

2026-10-28

Protected by Waratek

No public exploit

Several Kafka deserialization paths read a size or count field from input data and use it directly to allocate an array, collection, or byte buffer, with no check against the bytes actually available. A small crafted input can therefore declare a very large size and force a multi-gigabyte allocation. The affected paths are, in Kafka Streams, ProcessorRecordContext (header count), ChangedDeserializer (varint value length), CombinedKeySchema (foreign-key length, which could also produce a negative size), ProcessorMetadata (key length), and AssignmentInfo group-assignment decoding; and, in the clients library, ListDeserializer (used by ListSerde). Impact: An attacker can trigger the oversized allocation and raise an OutOfMemoryError that crashes the affected consumer or Kafka Streams thread; the broker and the rest of the cluster are unaffected. The reachable surface depends on the path: writing a crafted record to a Streams application's internal changelog or repartition topics, or committing crafted offset metadata to its consumer group (Streams internal paths); writing a crafted record to any topic that a consumer reads with ListSerde (ListDeserializer); or, as a member of a Streams consumer group, returning crafted assignment data when elected leader (AssignmentInfo). Because a crafted record remains in its topic, the error can recur on restart. The record size limit (max.message.bytes) does not help, because the allocation is driven by a size field rather than by the input's length. Mitigation: Upgrade to Apache Kafka 4.2.2, 4.3.2, or 4.4.0. Where authorization is enabled, restrict write access to each application's internal topics and consumer group to its own principal, and restrict who can produce to topics that are consumed with ListSerde. Resolution: From Apache Kafka 4.2.2, 4.3.2, and 4.4.0: these deserialization paths validate the declared size against the remaining input before allocating and reject input that fails the check.

CVE-2026-67211

High

2026-09-11

Protected by Waratek

No public exploit

OOM Denial of Service via Unbounded Map Pre-Sizing in Apache OpenNLP SymSpellModelSerializer Versions Affected: - 3.0.0-M4 - 3.0.0-M5 (The opennlp-spellcheck extension was introduced in 3.0.0-M4. Releases 1.x and 2.x do not contain the affected code.) Description: The SymSpellModelSerializer.create() method reads two 32-bit signed integer count fields (unigramCount and bigramCount) from a binary SymSpell model stream and passes each value directly to LinkedHashMap.newLinkedHashMap() after validating only that it is non-negative. No upper bound is applied, so the count is fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which either count field is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) causes the map to be pre-sized to a capacity of 2^30 entries. The oversized backing array is allocated on the first put() into that map, requesting 4–8 GB depending on whether compressed oops are in effect, and the load fails with an OutOfMemoryError. Because the count fields sit immediately after a fixed-size header (magic, format version, three UTF strings, the configuration fields, and the edit-distance identifier) the attacker pays no meaningful size cost to weaponize a payload: a file of well under 100 bytes plus a single real entry is sufficient to crash a JVM that loads it. Any code path that deserializes a SymSpell model is affected, including SymSpellModels.deserialize(InputStream), SymSpellModels.fromBytes(byte[]), classpath model loading via SymSpellModelResolver.resolveByLanguage(String), the CorrectTextTool command-line tool, and model-archive loading through the registered ArtifactSerializer. The opennlp-spellcheck extension ships in the official OpenNLP binary distribution. The practical impact is denial of service against processes that load SymSpell model files from untrusted or semi-trusted origins. Mitigation: - 3.x users should upgrade to 3.0.0-M6. Note: The fix applies an upper bound to both count fields, checked before the map is pre-sized; counts that are negative or exceed the bound cause an IOException to be thrown and the read to fail fast with no large allocation. The bound is the existing AbstractModelReader.MAX_ENTRIES limit introduced earlie, which the current change promotes to public visibility so that serializers implementing their own binary format can share it. The default bound is 10,000,000, which is well above the entry counts of legitimate SymSpell dictionaries but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load larger dictionaries can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Note that this property is shared with the model-reader limit and raising it relaxes both. Users who cannot upgrade immediately should treat all SymSpell .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

CVE-2026-42440

High

2026-05-04

Protected by Waratek

No public exploit

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader Versions Affected: before 1.9.5 before 2.5.9 before 3.0.0-M3 Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins. Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

CVE-2026-21452

High

2026-01-02

Protected by Waratek

Proof of concept only

MessagePack for Java is a serializer implementation for Java. A denial-of-service vulnerability exists in versions prior to 0.9.11 when deserializing .msgpack files containing EXT32 objects with attacker-controlled payload lengths. While MessagePack-Java parses extension headers lazily, it later trusts the declared EXT payload length when materializing the extension data. When ExtensionValue.getData() is invoked, the library attempts to allocate a byte array of the declared length without enforcing any upper bound. A malicious .msgpack file of only a few bytes can therefore trigger unbounded heap allocation, resulting in JVM heap exhaustion, process termination, or service unavailability. This vulnerability is triggered during model loading / deserialization, making it a model format vulnerability suitable for remote exploitation. The vulnerability enables a remote denial-of-service attack against applications that deserialize untrusted .msgpack model files using MessagePack for Java. A specially crafted but syntactically valid .msgpack file containing an EXT32 object with an attacker-controlled, excessively large payload length can trigger unbounded memory allocation during deserialization. When the model file is loaded, the library trusts the declared length metadata and attempts to allocate a byte array of that size, leading to rapid heap exhaustion, excessive garbage collection, or immediate JVM termination with an OutOfMemoryError. The attack requires no malformed bytes, user interaction, or elevated privileges and can be exploited remotely in real-world environments such as model registries, inference services, CI/CD pipelines, and cloud-based model hosting platforms that accept or fetch .msgpack artifacts. Because the malicious file is extremely small yet valid, it can bypass basic validation and scanning mechanisms, resulting in complete service unavailability and potential cascading failures in production systems. Version 0.9.11 fixes the vulnerability.

CVE-2023-3171

High

2023-12-27

Protected by Waratek

No public exploit

A flaw was found in EAP-7 during deserialization of certain classes, which permits instantiation of HashMap and HashTable with no checks on resources consumed. This issue could allow an attacker to submit malicious requests using these classes, which could eventually exhaust the heap and result in a Denial of Service.