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-762Q-JQWQ-G847
Vulnerability from github – Published: 2024-08-12 15:30 – Updated: 2025-11-04 00:31In the Linux kernel, the following vulnerability has been resolved:
mm: huge_memory: use !CONFIG_64BIT to relax huge page alignment on 32 bit machines
Yves-Alexis Perez reported commit 4ef9ad19e176 ("mm: huge_memory: don't force huge page alignment on 32 bit") didn't work for x86_32 [1]. It is because x86_32 uses CONFIG_X86_32 instead of CONFIG_32BIT.
!CONFIG_64BIT should cover all 32 bit machines.
[1] https://lore.kernel.org/linux-mm/CAHbLzkr1LwH3pcTgM+aGQ31ip2bKqiqEQ8=FQB+t2c3dhNKNHA@mail.gmail.com/
{
"affected": [],
"aliases": [
"CVE-2024-42258"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-08-12T15:15:20Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nmm: huge_memory: use !CONFIG_64BIT to relax huge page alignment on 32 bit machines\n\nYves-Alexis Perez reported commit 4ef9ad19e176 (\"mm: huge_memory: don\u0027t\nforce huge page alignment on 32 bit\") didn\u0027t work for x86_32 [1]. It is\nbecause x86_32 uses CONFIG_X86_32 instead of CONFIG_32BIT.\n\n!CONFIG_64BIT should cover all 32 bit machines.\n\n[1] https://lore.kernel.org/linux-mm/CAHbLzkr1LwH3pcTgM+aGQ31ip2bKqiqEQ8=FQB+t2c3dhNKNHA@mail.gmail.com/",
"id": "GHSA-762q-jqwq-g847",
"modified": "2025-11-04T00:31:11Z",
"published": "2024-08-12T15:30:54Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-42258"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7e1f4efb8d6140b2ec79bf760c43e1fc186e8dfc"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/89f2914dd4b47d2fad3deef0d700f9526d98d11f"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a5c399fe433a115e9d3693169b5f357f3194af0a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/d9592025000b3cf26c742f3505da7b83aedc26d5"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2025/01/msg00001.html"
}
],
"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-762V-RQ7Q-FF97
Vulnerability from github – Published: 2024-10-29 09:30 – Updated: 2024-11-04 21:25Mattermost versions 9.10.x <= 9.10.2, 9.11.x <= 9.11.1 and 9.5.x <= 9.5.9 fail to prevent detailed error messages from being displayed in Playbooks which allows an attacker to generate a large response and cause an amplified GraphQL response which in turn could cause the application to crash by sending a specially crafted request to Playbooks.
{
"affected": [
{
"package": {
"ecosystem": "Go",
"name": "github.com/mattermost/mattermost/server/v8"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "8.0.0-20240926115259-20ed58906adc"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2024-47401"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2024-10-29T16:13:16Z",
"nvd_published_at": "2024-10-29T09:15:07Z",
"severity": "MODERATE"
},
"details": "Mattermost versions 9.10.x \u003c= 9.10.2, 9.11.x \u003c= 9.11.1 and 9.5.x \u003c= 9.5.9 fail to\u00a0prevent detailed error messages from being displayed\u00a0in Playbooks which allows an attacker to generate a large response and cause an amplified GraphQL response which in turn could cause the application to crash by sending a specially crafted request to Playbooks.",
"id": "GHSA-762v-rq7q-ff97",
"modified": "2024-11-04T21:25:24Z",
"published": "2024-10-29T09:30:51Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-47401"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-762v-rq7q-ff97"
},
{
"type": "PACKAGE",
"url": "https://github.com/mattermost/mattermost"
},
{
"type": "WEB",
"url": "https://mattermost.com/security-updates"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Mattermost Server vulnerable to application crash from attacker-generated large response"
}
GHSA-764Q-GPMW-JCCJ
Vulnerability from github – Published: 2024-04-16 00:30 – Updated: 2024-04-16 00:30In lunary-ai/lunary version 1.0.0, an authorization flaw exists that allows unauthorized radar creation. The vulnerability stems from the lack of server-side checks to verify if a user is on a free account during the radar creation process, which is only enforced in the web UI. As a result, attackers can bypass the intended account upgrade requirement by directly sending crafted requests to the server, enabling the creation of an unlimited number of radars without payment.
{
"affected": [],
"aliases": [
"CVE-2024-1666"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-04-16T00:15:10Z",
"severity": "HIGH"
},
"details": "In lunary-ai/lunary version 1.0.0, an authorization flaw exists that allows unauthorized radar creation. The vulnerability stems from the lack of server-side checks to verify if a user is on a free account during the radar creation process, which is only enforced in the web UI. As a result, attackers can bypass the intended account upgrade requirement by directly sending crafted requests to the server, enabling the creation of an unlimited number of radars without payment.",
"id": "GHSA-764q-gpmw-jccj",
"modified": "2024-04-16T00:30:33Z",
"published": "2024-04-16T00:30:33Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-1666"
},
{
"type": "WEB",
"url": "https://github.com/lunary-ai/lunary/commit/c57cd50fa0477fd2a2efe60810c0099eebd66f54"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/0f310501-b5b0-4be0-ae38-d6b836f71ff0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N",
"type": "CVSS_V3"
}
]
}
GHSA-76FG-MHRG-FMMG
Vulnerability from github – Published: 2022-08-27 00:00 – Updated: 2022-09-02 19:14A flaw was found in XNIO, specifically in the notifyReadClosed method. The issue revealed this method was logging a message to another expected end. This flaw allows an attacker to send flawed requests to a server, possibly causing log contention-related performance concerns or an unwanted disk fill-up. A fix for this issue is available on the 3.x branch of the repository.
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "org.jboss.xnio:xnio-all"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "3.8.7.Final"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-0084"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-02T19:14:08Z",
"nvd_published_at": "2022-08-26T18:15:00Z",
"severity": "HIGH"
},
"details": "A flaw was found in XNIO, specifically in the `notifyReadClosed` method. The issue revealed this method was logging a message to another expected end. This flaw allows an attacker to send flawed requests to a server, possibly causing log contention-related performance concerns or an unwanted disk fill-up. A fix for this issue is available on the `3.x` branch of the repository.",
"id": "GHSA-76fg-mhrg-fmmg",
"modified": "2022-09-02T19:14:08Z",
"published": "2022-08-27T00:00:44Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-0084"
},
{
"type": "WEB",
"url": "https://github.com/xnio/xnio/pull/291"
},
{
"type": "WEB",
"url": "https://github.com/xnio/xnio/commit/fdefb3b8b715d33387cadc4d48991fb1989b0c12"
},
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2022-0084"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2064226"
},
{
"type": "PACKAGE",
"url": "https://github.com/xnio/xnio"
}
],
"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": "XNIO `notifyReadClosed` method logging message to unexpected end"
}
GHSA-76GX-97CQ-65F5
Vulnerability from github – Published: 2026-02-11 18:31 – Updated: 2026-06-30 03:35A specially-crafted file can cause libjxl's decoder to write pixel data to uninitialized unallocated memory. Soon after that data from another uninitialized unallocated region is copied to pixel data.
This can be done by requesting color transformation of grayscale images to another grayscale color space. Buffers allocated for 1-float-per-pixel are used as if they are allocated for 3-float-per-pixel. That happens only if LCMS2 is used as CMS engine. There is another CMS engine available (selected by build flags).
{
"affected": [],
"aliases": [
"CVE-2026-1837"
],
"database_specific": {
"cwe_ids": [
"CWE-770",
"CWE-787",
"CWE-805"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-02-11T16:16:04Z",
"severity": "HIGH"
},
"details": "A specially-crafted file can cause libjxl\u0027s decoder to write pixel data to uninitialized unallocated memory. Soon after that data from another uninitialized unallocated region is copied to pixel data.\n\nThis can be done by requesting color transformation of grayscale images to another grayscale color space. Buffers allocated for 1-float-per-pixel are used as if they are allocated for 3-float-per-pixel. That happens only if LCMS2 is used as CMS engine. There is another CMS engine available (selected by build flags).",
"id": "GHSA-76gx-97cq-65f5",
"modified": "2026-06-30T03:35:34Z",
"published": "2026-02-11T18:31:28Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-1837"
},
{
"type": "WEB",
"url": "https://github.com/libjxl/libjxl/issues/4549"
},
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2026-1837"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2438974"
},
{
"type": "WEB",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-1837.json"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/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-76JM-35GR-HWCM
Vulnerability from github – Published: 2026-07-08 03:30 – Updated: 2026-07-08 03:30sshd in OpenSSH before 10.4 allows remote attackers to cause a denial of service (resource consumption from excessive authentication attempts) because MaxAuthTries was mishandled for GSSAPIAuthentication.
{
"affected": [],
"aliases": [
"CVE-2026-60000"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-07-08T01:16:29Z",
"severity": "LOW"
},
"details": "sshd in OpenSSH before 10.4 allows remote attackers to cause a denial of service (resource consumption from excessive authentication attempts) because MaxAuthTries was mishandled for GSSAPIAuthentication.",
"id": "GHSA-76jm-35gr-hwcm",
"modified": "2026-07-08T03:30:27Z",
"published": "2026-07-08T03:30:27Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-60000"
},
{
"type": "WEB",
"url": "https://marc.info/?l=openssh-unix-dev\u0026m=178333966933090\u0026w=2"
},
{
"type": "WEB",
"url": "https://www.openssh.org/releasenotes.html#10.4p1"
},
{
"type": "WEB",
"url": "https://www.openwall.com/lists/oss-security/2026/07/06/5"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
}
]
}
GHSA-76RM-5XVP-J3V7
Vulnerability from github – Published: 2024-07-09 12:30 – Updated: 2024-07-09 12:30A vulnerability has been identified in SINEMA Remote Connect Server (All versions < V3.2 SP1). Affected applications do not properly handle log rotation. This could allow an unauthenticated remote attacker to cause a denial of service condition through resource exhaustion on the device.
{
"affected": [],
"aliases": [
"CVE-2024-39876"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-07-09T12:15:20Z",
"severity": "MODERATE"
},
"details": "A vulnerability has been identified in SINEMA Remote Connect Server (All versions \u003c V3.2 SP1). Affected applications do not properly handle log rotation. This could allow an unauthenticated remote attacker to cause a denial of service condition through resource exhaustion on the device.",
"id": "GHSA-76rm-5xvp-j3v7",
"modified": "2024-07-09T12:30:58Z",
"published": "2024-07-09T12:30:58Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-39876"
},
{
"type": "WEB",
"url": "https://cert-portal.siemens.com/productcert/html/ssa-381581.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/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-76RV-2R9V-C5M6
Vulnerability from github – Published: 2026-02-25 22:31 – Updated: 2026-02-25 22:31Summary
All rate limit buckets for a single entity share the same DynamoDB partition key (namespace/ENTITY#{id}). A high-traffic entity can exceed DynamoDB's per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition.
Details
Each acquire() call performs a TransactWriteItems (or UpdateItem in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB's adaptive capacity may not redistribute fast enough, causing ProvisionedThroughputExceededException.
The library has no built-in mitigation:
- No partition key sharding/salting
- No write coalescing or batching
- No client-side admission control before hitting DynamoDB
- RateLimiterUnavailable is raised but the caller has already been delayed
Impact
- Availability: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify
- Fairness: Other entities sharing the same DynamoDB partition may experience collateral throttling
- Multi-tenant risk: In a shared LLM proxy scenario, one tenant's burst traffic could degrade service for others
Reproduction
- Create an entity with high rate limits (e.g., 100,000 rpm)
- Send sustained traffic at 1,000+ req/sec to a single entity
- Observe DynamoDB
ThrottledRequestsCloudWatch metric increasing - Observe
acquire()latency spikes andRateLimiterUnavailableexceptions
Remediation Design: Pre-Shard Buckets
- Move buckets to
PK={ns}/BUCKET#{entity}#{resource}#{shard}, SK=#STATE— one partition per (entity, resource, shard) - Auto-inject
wcu:1000reserved limit on every bucket — tracks DynamoDB partition write pressure in-band (name may change during implementation) - Shard doubling (1→2→4→8) triggered by client on
wcuexhaustion or proactively by aggregator - Shard 0 at suffix
#0is source of truth forshard_count. Aggregator propagates to other shards - Original limits stored on bucket, effective limits derived:
original / shard_count. Infrastructure limits (wcu) not divided - Shard selection: random/round-robin. On application limit exhaustion, retry on another shard (max 2 retries)
- Lazy shard creation on first access
- Bucket discovery via GSI3 (KEYS_ONLY) + BatchGetItem. GSI2 for resource aggregation unchanged
- Cascade: parent unaware, protected by own
wcu - Aggregator: parse new PK format, key by shard_id, effective limits for refill, filter
wcufrom snapshots - Clean break migration: schema version bump, old buckets ignored, new buckets created on first access
- $0.625/M preserved on hot path
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 0.10.0"
},
"package": {
"ecosystem": "PyPI",
"name": "zae-limiter"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.10.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-27695"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-02-25T22:31:10Z",
"nvd_published_at": "2026-02-25T15:20:52Z",
"severity": "MODERATE"
},
"details": "## Summary\n\nAll rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB\u0027s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity \u2014 and potentially co-located entities in the same partition.\n\n## Details\n\nEach `acquire()` call performs a `TransactWriteItems` (or `UpdateItem` in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB\u0027s adaptive capacity may not redistribute fast enough, causing `ProvisionedThroughputExceededException`.\n\nThe library has no built-in mitigation:\n- No partition key sharding/salting\n- No write coalescing or batching\n- No client-side admission control before hitting DynamoDB\n- `RateLimiterUnavailable` is raised but the caller has already been delayed\n\n## Impact\n\n- **Availability**: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify\n- **Fairness**: Other entities sharing the same DynamoDB partition may experience collateral throttling\n- **Multi-tenant risk**: In a shared LLM proxy scenario, one tenant\u0027s burst traffic could degrade service for others\n\n## Reproduction\n\n1. Create an entity with high rate limits (e.g., 100,000 rpm)\n2. Send sustained traffic at 1,000+ req/sec to a single entity\n3. Observe DynamoDB `ThrottledRequests` CloudWatch metric increasing\n4. Observe `acquire()` latency spikes and `RateLimiterUnavailable` exceptions\n\n## Remediation Design: Pre-Shard Buckets\n\n- Move buckets to `PK={ns}/BUCKET#{entity}#{resource}#{shard}, SK=#STATE` \u2014 one partition per (entity, resource, shard)\n- Auto-inject `wcu:1000` reserved limit on every bucket \u2014 tracks DynamoDB partition write pressure in-band (name may change during implementation)\n- Shard doubling (1\u21922\u21924\u21928) triggered by client on `wcu` exhaustion or proactively by aggregator\n- Shard 0 at suffix `#0` is source of truth for `shard_count`. Aggregator propagates to other shards\n- Original limits stored on bucket, effective limits derived: `original / shard_count`. Infrastructure limits (`wcu`) not divided\n- Shard selection: random/round-robin. On application limit exhaustion, retry on another shard (max 2 retries)\n- Lazy shard creation on first access\n- Bucket discovery via GSI3 (KEYS_ONLY) + BatchGetItem. GSI2 for resource aggregation unchanged\n- Cascade: parent unaware, protected by own `wcu`\n- Aggregator: parse new PK format, key by shard_id, effective limits for refill, filter `wcu` from snapshots\n- Clean break migration: schema version bump, old buckets ignored, new buckets created on first access\n- **$0.625/M preserved on hot path**",
"id": "GHSA-76rv-2r9v-c5m6",
"modified": "2026-02-25T22:31:10Z",
"published": "2026-02-25T22:31:10Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/zeroae/zae-limiter/security/advisories/GHSA-76rv-2r9v-c5m6"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-27695"
},
{
"type": "WEB",
"url": "https://github.com/zeroae/zae-limiter/commit/481ce44d818d66e31d8837bc48519660ce4c267f"
},
{
"type": "PACKAGE",
"url": "https://github.com/zeroae/zae-limiter"
},
{
"type": "WEB",
"url": "https://github.com/zeroae/zae-limiter/releases/tag/v0.10.1"
}
],
"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"
}
],
"summary": "zae-limiter: DynamoDB hot partition throttling enables per-entity Denial of Service"
}
GHSA-777C-7FJR-54VF
Vulnerability from github – Published: 2026-06-04 14:21 – Updated: 2026-06-12 19:24Summary
Axios versions 1.7.0 through 1.15.x did not enforce configured request and response size limits when requests were sent with the fetch adapter. Applications that selected adapter: 'fetch', or ran in environments where axios resolved to the fetch adapter, could receive or send bodies larger than maxContentLength or maxBodyLength despite those limits being explicitly configured.
This can cause resource exhaustion in server-side usage when a malicious or compromised server returns an oversized response, when an attacker can supply a large data: URL, or when an application forwards attacker-controlled request bodies through axios while relying on maxBodyLength as a boundary.
Impact
The impact is availability-only. Affected applications may process, buffer, or transmit data beyond the configured limit, potentially exhausting memory, CPU, or network resources.
This does not affect axios’s default unlimited behaviour by itself: maxContentLength and maxBodyLength default to -1. The vulnerability exists when an application has configured finite limits and expects axios to enforce them.
Server-side runtimes are the primary concern. Browser impact is generally constrained by the browser process and browser fetch behavior, and should not be described as server process exhaustion.
Affected Functionality
Affected functionality includes requests using the built-in fetch adapter with finite maxContentLength or maxBodyLength values.
Relevant configurations include:
adapter: 'fetch'adapter: ['fetch', ...]whenfetchis selected- environments where neither
xhrnorhttpis available and axios falls back tofetch - custom fetch environments configured through
env.fetch
Unaffected functionality includes:
- Node.js default
httpadapter enforcement - versions before the fetch adapter was introduced
- configurations that do not rely on finite axios size limits
Technical Details
In vulnerable versions, lib/adapters/fetch.js destructured request config without maxContentLength or maxBodyLength. The adapter dispatched fetch() and then materialized the response through text(), arrayBuffer(), blob(), or related resolvers without checking the configured response limit.
The fix in e5540dc added:
maxContentLengthandmaxBodyLengthreads inlib/adapters/fetch.js- upfront
data:URL decoded-size checks - outbound body-size checks before dispatch
Content-Lengthresponse pre-checks- streaming response enforcement
- fallback checks for environments without
ReadableStream - regression tests in
tests/unit/adapters/fetch.test.js
Proof of Concept of Attack
import http from 'node:http';
import axios from 'axios';
const server = http.createServer((req, res) => {
let received = 0;
req.on('data', chunk => {
received += chunk.length;
});
req.on('end', () => {
res.end(JSON.stringify({ received }));
});
});
await new Promise(resolve => server.listen(0, resolve));
const url = `http://127.0.0.1:${server.address().port}/`;
await axios.post(url, 'A'.repeat(2 * 1024 * 1024), {
adapter: 'fetch',
maxBodyLength: 1024
});
// Vulnerable versions succeed and the server receives 2097152 bytes.
// Fixed versions reject with ERR_BAD_REQUEST.
server.close();
Workarounds
Use the Node.js http adapter for server-side requests where finite size limits are security-relevant.
Validate or cap attacker-controlled request bodies before passing them to axios.
Reject or strictly allowlist attacker-controlled URL schemes, especially data: URLs, before calling axios.
{
"affected": [
{
"package": {
"ecosystem": "npm",
"name": "axios"
},
"ranges": [
{
"events": [
{
"introduced": "1.7.0"
},
{
"fixed": "1.16.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-44488"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-06-04T14:21:37Z",
"nvd_published_at": "2026-06-11T17:16:32Z",
"severity": "HIGH"
},
"details": "## Summary\n\nAxios versions `1.7.0` through `1.15.x` did not enforce configured request and response size limits when requests were sent with the `fetch` adapter. Applications that selected `adapter: \u0027fetch\u0027`, or ran in environments where axios resolved to the fetch adapter, could receive or send bodies larger than `maxContentLength` or `maxBodyLength` despite those limits being explicitly configured.\n\nThis can cause resource exhaustion in server-side usage when a malicious or compromised server returns an oversized response, when an attacker can supply a large `data:` URL, or when an application forwards attacker-controlled request bodies through axios while relying on `maxBodyLength` as a boundary.\n\n## Impact\n\nThe impact is availability-only. Affected applications may process, buffer, or transmit data beyond the configured limit, potentially exhausting memory, CPU, or network resources.\n\nThis does not affect axios\u2019s default unlimited behaviour by itself: `maxContentLength` and `maxBodyLength` default to `-1`. The vulnerability exists when an application has configured finite limits and expects axios to enforce them.\n\nServer-side runtimes are the primary concern. Browser impact is generally constrained by the browser process and browser fetch behavior, and should not be described as server process exhaustion.\n\n## Affected Functionality\n\nAffected functionality includes requests using the built-in `fetch` adapter with finite `maxContentLength` or `maxBodyLength` values.\n\nRelevant configurations include:\n\n- `adapter: \u0027fetch\u0027`\n- `adapter: [\u0027fetch\u0027, ...]` when `fetch` is selected\n- environments where neither `xhr` nor `http` is available and axios falls back to `fetch`\n- custom fetch environments configured through `env.fetch`\n\nUnaffected functionality includes:\n\n- Node.js default `http` adapter enforcement\n- versions before the fetch adapter was introduced\n- configurations that do not rely on finite axios size limits\n\n## Technical Details\n\nIn vulnerable versions, `lib/adapters/fetch.js` destructured request config without `maxContentLength` or `maxBodyLength`. The adapter dispatched `fetch()` and then materialized the response through `text()`, `arrayBuffer()`, `blob()`, or related resolvers without checking the configured response limit.\n\nThe fix in `e5540dc` added:\n\n- `maxContentLength` and `maxBodyLength` reads in `lib/adapters/fetch.js`\n- upfront `data:` URL decoded-size checks\n- outbound body-size checks before dispatch\n- `Content-Length` response pre-checks\n- streaming response enforcement\n- fallback checks for environments without `ReadableStream`\n- regression tests in `tests/unit/adapters/fetch.test.js`\n\n## Proof of Concept of Attack\n\n```js\nimport http from \u0027node:http\u0027;\nimport axios from \u0027axios\u0027;\n\nconst server = http.createServer((req, res) =\u003e {\n let received = 0;\n\n req.on(\u0027data\u0027, chunk =\u003e {\n received += chunk.length;\n });\n\n req.on(\u0027end\u0027, () =\u003e {\n res.end(JSON.stringify({ received }));\n });\n});\n\nawait new Promise(resolve =\u003e server.listen(0, resolve));\nconst url = `http://127.0.0.1:${server.address().port}/`;\n\nawait axios.post(url, \u0027A\u0027.repeat(2 * 1024 * 1024), {\n adapter: \u0027fetch\u0027,\n maxBodyLength: 1024\n});\n\n// Vulnerable versions succeed and the server receives 2097152 bytes.\n// Fixed versions reject with ERR_BAD_REQUEST.\n\nserver.close();\n```\n\n## Workarounds\n\nUse the Node.js `http` adapter for server-side requests where finite size limits are security-relevant.\n\nValidate or cap attacker-controlled request bodies before passing them to axios.\n\nReject or strictly allowlist attacker-controlled URL schemes, especially `data:` URLs, before calling axios.\n\n\u003cdetails\u003e\n\u003csummary\u003eOriginal Report\u003c/summary\u003e\n\n### Summary\nWhen Axios is used with adapter: \u0027fetch\u0027, configured body/response size limits are not enforced. This allows oversized uploads/downloads (including data: URLs) despite explicit limits, which can lead to memory/resource exhaustion in server-side usage.\n\n### Details\nmaxBodyLength and maxContentLength are not applied in the fetch adapter flow:\n - lib/adapters/fetch.js (146-160): config destructuring does not include these controls.\n - lib/adapters/fetch.js (220-234): request is dispatched with fetch() without request-size enforcement.\n - lib/adapters/fetch.js (267-283): response is materialized via text(), arrayBuffer(), blob(), etc. without response-size checks.\nBy contrast, the HTTP adapter enforces both limits.\n\n### PoC\n Environment:\n - Axios main at commit f7a4ee2\n - Node v24.2.0\n\nSteps:\n 1. Start an HTTP server that counts received bytes and echoes {received}.\n 2. Send 2 MiB with:\n - adapter: \u0027fetch\u0027\n - maxBodyLength: 1024\n 3. Request a 4 KiB data: URL with:\n - adapter: \u0027fetch\u0027\n - maxContentLength: 16\n\nExpected secure behavior: both requests rejected.\n Observed:\n - Upload: success, server received 2097152\n - data: response: success, length 4096\n\n### Impact\nType: DoS / resource exhaustion due to limit bypass.\nImpacted: applications using Axios fetch adapter as a server-side security control boundary for untrusted request/response sizes.\n\u003c/details\u003e\n\n---",
"id": "GHSA-777c-7fjr-54vf",
"modified": "2026-06-12T19:24:51Z",
"published": "2026-06-04T14:21:37Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/axios/axios/security/advisories/GHSA-777c-7fjr-54vf"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44488"
},
{
"type": "WEB",
"url": "https://github.com/axios/axios/pull/10795"
},
{
"type": "WEB",
"url": "https://github.com/axios/axios/pull/10796"
},
{
"type": "PACKAGE",
"url": "https://github.com/axios/axios"
},
{
"type": "WEB",
"url": "https://github.com/axios/axios/releases/tag/v1.16.0"
}
],
"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": "Allocation of Resources Without Limits or Throttling in Axios"
}
GHSA-77FQ-6X6C-CQ7Q
Vulnerability from github – Published: 2022-05-13 01:02 – Updated: 2022-05-13 01:02It was found in Ceph versions before 13.2.4 that authenticated ceph RGW users can cause a denial of service against OMAPs holding bucket indices.
{
"affected": [],
"aliases": [
"CVE-2018-16846"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2019-01-15T18:29:00Z",
"severity": "MODERATE"
},
"details": "It was found in Ceph versions before 13.2.4 that authenticated ceph RGW users can cause a denial of service against OMAPs holding bucket indices.",
"id": "GHSA-77fq-6x6c-cq7q",
"modified": "2022-05-13T01:02:10Z",
"published": "2022-05-13T01:02:10Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-16846"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2019:2538"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2019:2541"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2018-16846"
},
{
"type": "WEB",
"url": "https://ceph.com/releases/13-2-4-mimic-released"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2019/03/msg00002.html"
},
{
"type": "WEB",
"url": "https://lists.debian.org/debian-lts-announce/2021/08/msg00013.html"
},
{
"type": "WEB",
"url": "https://usn.ubuntu.com/4035-1"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-04/msg00100.html"
}
],
"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"
}
]
}
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.