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CWE-789
Memory Allocation with Excessive Size Value
The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.
CVE-2026-40894 (GCVE-0-2026-40894)
Vulnerability from cvelistv5 – Published: 2026-04-23 18:03 – Updated: 2026-04-23 19:22
VLAI
EPSS
VEX
Title
OpenTelemetry dotnet: Excessive memory allocation when parsing OpenTelemetry propagation headers
Summary
OpenTelemetry dotnet is a dotnet telemetry framework. In OpenTelemetry.Api 0.5.0-beta.2 to 1.15.2 and OpenTelemetry.Extensions.Propagators 1.3.1 to 1.15.2, The implementation details of the baggage, B3 and Jaeger processing code in the OpenTelemetry.Api and OpenTelemetry.Extensions.Propagators NuGet packages can allocate excessive memory when parsing which could create a potential denial of service (DoS) in the consuming application. This vulnerability is fixed in 1.15.3.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_CONFIRM |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
Impacted products
3 products
| Vendor | Product | Version | |
|---|---|---|---|
| open-telemetry | opentelemetry-dotnet |
Affected:
>= 0.5.0-beta.2, < 1.15.3
|
|
| open-telemetry | OpenTelemetry.Api |
Affected:
>= 0.5.0-beta.2, < 1.15.3
|
|
| open-telemetry | OpenTelemetry.Extensions.Propagators |
Affected:
>= 1.3.1, < 1.15.3
|
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CVE-2026-41178 (GCVE-0-2026-41178)
Vulnerability from cvelistv5 – Published: 2026-06-04 14:38 – Updated: 2026-06-04 15:46
VLAI
EPSS
VEX
Title
OpenTelemetry-Go's baggage parsing no longer caps raw header length
Summary
OpenTelemetry-Go is the Go implementation of OpenTelemetry. Versions 1.41.0 and 1.43.0 removed raw-length rejection and it causes `Parse` to process arbitrarily large/invalid baggage headers and log errors, enabling DoS via oversized inputs. Versions 1.42.0 and 1.44.0 fix the issue.
Severity
5.3 (Medium)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/open-telemetry/opentelemetry-g… | x_refsource_CONFIRM |
| https://github.com/open-telemetry/opentelemetry-g… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| open-telemetry | go.opentelemetry.io/otel/baggage |
Affected:
= 1.41.0
Affected: = 1.43.0 |
|
| open-telemetry | go.opentelemetry.io/otel/propagation |
Affected:
= 1.41.0
Affected: = 1.43.0 |
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CVE-2026-41312 (GCVE-0-2026-41312)
Vulnerability from cvelistv5 – Published: 2026-04-22 21:02 – Updated: 2026-04-23 13:45
VLAI
EPSS
VEX
Title
pypdf: Manipulated FlateDecode predictor parameters can exhaust RAM
Summary
pypdf is a free and open-source pure-python PDF library. An attacker who uses a vulnerability present in versions prior to 6.10.2 can craft a PDF which leads to the RAM being exhausted. This requires accessing a stream compressed using `/FlateDecode` with a `/Predictor` unequal 1 and large predictor parameters. This has been fixed in pypdf 6.10.2. As a workaround, one may apply the changes from the patch manually.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/py-pdf/pypdf/security/advisori… | x_refsource_CONFIRM |
| https://github.com/py-pdf/pypdf/pull/3734 | x_refsource_MISC |
| https://github.com/py-pdf/pypdf/commit/ac734dab4e… | x_refsource_MISC |
| https://github.com/py-pdf/pypdf/releases/tag/6.10.2 | x_refsource_MISC |
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CVE-2026-41314 (GCVE-0-2026-41314)
Vulnerability from cvelistv5 – Published: 2026-04-22 21:08 – Updated: 2026-04-23 14:21
VLAI
EPSS
VEX
Title
pypdf: Manipulated FlateDecode image dimensions can exhaust RAM
Summary
pypdf is a free and open-source pure-python PDF library. An attacker who uses a vulnerability present in versions prior to 6.10.2 can craft a PDF which leads to the RAM being exhausted. This requires accessing an image using `/FlateDecode` with large size values. This has been fixed in pypdf 6.10.2. As a workaround, one may apply the changes from the patch manually.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/py-pdf/pypdf/security/advisori… | x_refsource_CONFIRM |
| https://github.com/py-pdf/pypdf/pull/3734 | x_refsource_MISC |
| https://github.com/py-pdf/pypdf/commit/ac734dab4e… | x_refsource_MISC |
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CVE-2026-42146 (GCVE-0-2026-42146)
Vulnerability from cvelistv5 – Published: 2026-05-04 17:53 – Updated: 2026-05-06 13:44
VLAI
EPSS
VEX
Title
CImg Library: Uncontrolled memory allocation via nb_colors field in _load_bmp
Summary
CImg Library is a C++ library for image processing. Prior to commit c3aacf5, the nb_colors field read from the BMP file header is used directly to compute an allocation size without validating it against the remaining file size. A crafted BMP file with a large nb_colors value triggers an out-of-memory condition, crashing any application that uses CImg to load untrusted BMP files. This issue has been patched via commit c3aacf5.
Severity
5.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/GreycLab/CImg/security/advisor… | x_refsource_CONFIRM |
| https://github.com/GreycLab/CImg/issues/477 | x_refsource_MISC |
| https://github.com/GreycLab/CImg/commit/c3aacf5b9… | x_refsource_MISC |
| https://github.com/GreycLab/CImg/releases/tag/v.3.7.5 | x_refsource_MISC |
Impacted products
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CVE-2026-42154 (GCVE-0-2026-42154)
Vulnerability from cvelistv5 – Published: 2026-05-04 18:13 – Updated: 2026-07-09 12:09
VLAI
EPSS
VEX
Title
Prometheus: remote read endpoint allows denial of service via crafted snappy payload
Summary
Prometheus is an open-source monitoring system and time series database. Prior to versions 3.5.3 and 3.11.3, the remote read endpoint (/api/v1/read) does not validate the declared decoded length in a snappy-compressed request body before allocating memory. An unauthenticated attacker can send a small payload that causes a huge heap allocation per request. Under concurrent load this can exhaust available memory and crash the Prometheus process. This issue has been patched in versions 3.5.3 and 3.11.3.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
17 references
Impacted products
32 products
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CVE-2026-42189 (GCVE-0-2026-42189)
Vulnerability from cvelistv5 – Published: 2026-05-08 19:49 – Updated: 2026-05-11 14:23
VLAI
EPSS
VEX
Title
Russh: Pre-auth DoS via unbounded allocation in keyboard-interactive auth
Summary
Russh is a Rust SSH client & server library. Prior to version 0.60.1, a pre-authentication denial-of-service vulnerability exists in the server's keyboard-interactive authentication handler. A malicious client can crash any russh-based server that implements keyboard-interactive auth (e.g., for 2FA/TOTP) with a single malformed packet, requiring no credentials. This issue has been patched in version 0.60.1.
Severity
7.5 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/Eugeny/russh/security/advisori… | x_refsource_CONFIRM |
| https://github.com/Eugeny/russh/commit/6c3c80a9b6… | x_refsource_MISC |
| https://github.com/Eugeny/russh/releases/tag/v0.60.1 | x_refsource_MISC |
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CVE-2026-42241 (GCVE-0-2026-42241)
Vulnerability from cvelistv5 – Published: 2026-05-07 18:52 – Updated: 2026-05-08 21:30
VLAI
EPSS
VEX
Title
ParquetSharp: Possible Stack Overflow When Reading a ParquetFile with Large Decimal Type Width
Summary
ParquetSharp is a .NET library for reading and writing Apache Parquet files. From version 18.1.0 to before version 23.0.0.1, DecimalConverter.ReadDecimal makes a stackalloc using what might be an attacker-supplied value. If an attacker declares a decimal column with some unreasonable width, this could lead to a stack overflow. In a service environment, this would potentially take down a service. This affects applications using ParquetSharp to read untrusted Parquet files in a network service. This issue has been patched in version 23.0.0.1.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/G-Research/ParquetSharp/securi… | x_refsource_CONFIRM |
| https://github.com/G-Research/ParquetSharp/releas… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| G-Research | ParquetSharp |
Affected:
>= 18.1.0, < 23.0.0.1
|
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CVE-2026-42348 (GCVE-0-2026-42348)
Vulnerability from cvelistv5 – Published: 2026-05-12 18:01 – Updated: 2026-05-13 19:32
VLAI
EPSS
VEX
Title
OpAMP client reads unbounded HTTP response bodies
Summary
OpenTelemetry.OpAmp.Client is the OpAMP client for OpenTelemetry .NET. Prior to 0.2.0-alpha.1, when receiving responses from the OpAMP server over HTTP, the OpAMP client allocates an unbounded buffer to read all bytes from the server, with no upper-bound on the number of bytes consumed. This could cause memory exhaustion in the consuming application if the configured OpAMP server is attacker-controlled (or a network attacker can MitM the connection) and an extremely large body is returned in the response. This vulnerability is fixed in 0.2.0-alpha.1.
Severity
5.9 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-789 - Memory Allocation with Excessive Size Value
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_CONFIRM |
| https://github.com/open-telemetry/opentelemetry-d… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| open-telemetry | opentelemetry-dotnet-contrib |
Affected:
< 0.2.0-alpha.1
|
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CVE-2026-42440 (GCVE-0-2026-42440)
Vulnerability from cvelistv5 – Published: 2026-05-04 16:40 – Updated: 2026-07-03 12:05
VLAI
EPSS
VEX
Title
Apache OpenNLP: OOM DoS via Unbounded Array Allocation in AbstractModelReader
Summary
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.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://lists.apache.org/thread/s8xlkx1gqbxfsq48p… | vendor-advisory |
| http://www.openwall.com/lists/oss-security/2026/0… | |
| https://access.redhat.com/security/cve/CVE-2026-42440 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2466494 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
5 products
| Vendor | Product | Version | |
|---|---|---|---|
| Apache Software Foundation | Apache OpenNLP |
Affected:
2.0 , < 2.5.9
(semver)
Affected: 3.0.0-M1 , < 3.0.0-M3 (semver) Affected: 0 , < 1.9.5 (semver) |
|
| Red Hat | Red Hat Fuse 7 |
cpe:/a:redhat:jboss_fuse:7 |
|
| Red Hat | Red Hat JBoss Enterprise Application Platform Expansion Pack |
cpe:/a:redhat:jbosseapxp |
|
| Red Hat | Red Hat Data Grid 8 |
cpe:/a:redhat:jboss_data_grid:8 |
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai |
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"value": "\u003cp\u003e\u003cb\u003eOOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader\u0026nbsp;\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVersions Affected:\u003c/b\u003e\u0026nbsp;\u003c/p\u003ebefore 1.9.5\u003cbr\u003e\u003cp\u003ebefore 2.5.9\u003c/p\u003e\u003cp\u003ebefore 3.0.0-M3\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescription:\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003ccode\u003eAbstractModelReader\u003c/code\u003e methods \u003ccode\u003egetOutcomes()\u003c/code\u003e, \u003ccode\u003egetOutcomePatterns()\u003c/code\u003e, and \u003ccode\u003egetPredicates()\u003c/code\u003e each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (\u003ccode\u003enew String[numOutcomes]\u003c/code\u003e, \u003ccode\u003enew int[numOCTypes][]\u003c/code\u003e, \u003ccode\u003enew String[NUM_PREDS]\u003c/code\u003e) 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.\u003c/p\u003e\n\u003cp\u003eA crafted \u003ccode\u003e.bin\u003c/code\u003e model file in which any of these count fields is set to \u003ccode\u003eInteger.MAX_VALUE\u003c/code\u003e (or any value large enough to exhaust the available heap) triggers an \u003ccode\u003eOutOfMemoryError\u003c/code\u003e 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, \u003ccode\u003egetOutcomes()\u003c/code\u003e 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 \u003ccode\u003e.bin\u003c/code\u003e model is affected, including direct use of \u003ccode\u003eGenericModelReader\u003c/code\u003e and any higher-level component that delegates to it during model load.\u003c/p\u003e\n\u003cp\u003eThe practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eMitigation:\u003c/b\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e2.x users should upgrade to 2.5.9.\u003c/li\u003e\n\u003cli\u003e3.x users should upgrade to 3.0.0-M3.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cb\u003eNote:\u003c/b\u003e 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 \u003ccode\u003eIllegalArgumentException\u003c/code\u003e 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 \u003ccode\u003eOPENNLP_MAX_ENTRIES\u003c/code\u003e system property to the desired positive integer (e.g. \u003ccode\u003e-DOPENNLP_MAX_ENTRIES=50000000\u003c/code\u003e); invalid or non-positive values fall back to the default.\u003c/p\u003e\n\u003cp\u003eUsers who cannot upgrade immediately should treat all \u003ccode\u003e.bin\u003c/code\u003e 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.\u0026nbsp;\u003c/p\u003e"
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Mitigation
Phases: Implementation, Architecture and Design
Description:
- Perform adequate input validation against any value that influences the amount of memory that is allocated. Define an appropriate strategy for handling requests that exceed the limit, and consider supporting a configuration option so that the administrator can extend the amount of memory to be used if necessary.
Mitigation
Phase: Operation
Description:
- Run your program using system-provided resource limits for memory. This might still cause the program to crash or exit, but the impact to the rest of the system will be minimized.
No CAPEC attack patterns related to this CWE.