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.
4237 vulnerabilities reference this CWE, most recent first.
GHSA-XXHW-CVH5-24PV
Vulnerability from github – Published: 2026-09-30 21:32 – Updated: 2026-09-30 21:32OpenClaw Windows Node before 2026.7.1 contains an allocation of resources without limits vulnerability in the gateway WebSocket transport that allows connected gateways to exhaust node memory. Attackers can send an unending sequence of WebSocket continuation frames without EndOfMessage to cause unbounded memory growth until the node process crashes.
{
"affected": [],
"aliases": [
"CVE-2026-101881"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-30T20:17:19Z",
"severity": "HIGH"
},
"details": "OpenClaw Windows Node before 2026.7.1 contains an allocation of resources without limits vulnerability in the gateway WebSocket transport that allows connected gateways to exhaust node memory. Attackers can send an unending sequence of WebSocket continuation frames without EndOfMessage to cause unbounded memory growth until the node process crashes.",
"id": "GHSA-xxhw-cvh5-24pv",
"modified": "2026-09-30T21:32:09Z",
"published": "2026-09-30T21:32:09Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/openclaw/openclaw-windows-node/security/advisories/GHSA-xxr2-xm56-9cw5"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-101881"
},
{
"type": "WEB",
"url": "https://github.com/openclaw/openclaw-windows-node/commit/1810e357aa0d0639099b347f31c746ba7d31512a"
},
{
"type": "WEB",
"url": "https://github.com/openclaw/openclaw-windows-node/blob/v0.6.12/src/OpenClaw.Shared/WebSocketClientBase.cs#L217-L263"
},
{
"type": "WEB",
"url": "https://github.com/openclaw/openclaw-windows-node/releases/tag/v2026.7.1"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/openclaw-windows-node-before-2026.7.1-denial-of-service"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/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-XXJ9-F6RV-M3X4
Vulnerability from github – Published: 2024-02-07 00:30 – Updated: 2025-11-04 22:13An issue was discovered in Django 3.2 before 3.2.24, 4.2 before 4.2.10, and Django 5.0 before 5.0.2. The intcomma template filter was subject to a potential denial-of-service attack when used with very long strings.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "Django"
},
"ranges": [
{
"events": [
{
"introduced": "3.2"
},
{
"fixed": "3.2.24"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "Django"
},
"ranges": [
{
"events": [
{
"introduced": "4.2"
},
{
"fixed": "4.2.10"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "Django"
},
"ranges": [
{
"events": [
{
"introduced": "5.0"
},
{
"fixed": "5.0.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2024-24680"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2024-02-07T17:32:14Z",
"nvd_published_at": "2024-02-06T22:16:15Z",
"severity": "HIGH"
},
"details": "An issue was discovered in Django 3.2 before 3.2.24, 4.2 before 4.2.10, and Django 5.0 before 5.0.2. The intcomma template filter was subject to a potential denial-of-service attack when used with very long strings.",
"id": "GHSA-xxj9-f6rv-m3x4",
"modified": "2025-11-04T22:13:17Z",
"published": "2024-02-07T00:30:25Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-24680"
},
{
"type": "WEB",
"url": "https://github.com/django/django/commit/16a8fe18a3b81250f4fa57e3f93f0599dc4895bc"
},
{
"type": "WEB",
"url": "https://github.com/django/django/commit/55519d6cf8998fe4c8f5c8abffc2b10a7c3d14e9"
},
{
"type": "WEB",
"url": "https://github.com/django/django/commit/572ea07e84b38ea8de0551f4b4eda685d91d09d2"
},
{
"type": "WEB",
"url": "https://github.com/django/django/commit/c1171ffbd570db90ca206c30f8e2b9f691243820"
},
{
"type": "WEB",
"url": "https://docs.djangoproject.com/en/5.0/releases/security"
},
{
"type": "PACKAGE",
"url": "https://github.com/django/django"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/django/PYSEC-2024-28.yaml"
},
{
"type": "WEB",
"url": "https://groups.google.com/forum/#%21forum/django-announce"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/D2JIRXEDP4ZET5KFMAPPYSK663Q52NEX"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/SN2PLJGYSAAG5KUVIUFJYKD3BLQ4OSN6"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZQJOMNRMVPCN5WMIZ7YSX5LQ7IR2NY4D"
},
{
"type": "WEB",
"url": "https://www.djangoproject.com/weblog/2024/feb/06/security-releases"
}
],
"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:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Django denial-of-service attack in the intcomma template filter"
}
GHSA-XXPF-X8MQ-P6V4
Vulnerability from github – Published: 2022-11-01 19:00 – Updated: 2025-05-05 21:31Xenstore: guests can let run xenstored out of memory T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Malicious guests can cause xenstored to allocate vast amounts of memory, eventually resulting in a Denial of Service (DoS) of xenstored. There are multiple ways how guests can cause large memory allocations in xenstored: - - by issuing new requests to xenstored without reading the responses, causing the responses to be buffered in memory - - by causing large number of watch events to be generated via setting up multiple xenstore watches and then e.g. deleting many xenstore nodes below the watched path - - by creating as many nodes as allowed with the maximum allowed size and path length in as many transactions as possible - - by accessing many nodes inside a transaction
{
"affected": [],
"aliases": [
"CVE-2022-42316"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-11-01T13:15:00Z",
"severity": "MODERATE"
},
"details": "Xenstore: guests can let run xenstored out of memory T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Malicious guests can cause xenstored to allocate vast amounts of memory, eventually resulting in a Denial of Service (DoS) of xenstored. There are multiple ways how guests can cause large memory allocations in xenstored: - - by issuing new requests to xenstored without reading the responses, causing the responses to be buffered in memory - - by causing large number of watch events to be generated via setting up multiple xenstore watches and then e.g. deleting many xenstore nodes below the watched path - - by creating as many nodes as allowed with the maximum allowed size and path length in as many transactions as possible - - by accessing many nodes inside a transaction",
"id": "GHSA-xxpf-x8mq-p6v4",
"modified": "2025-05-05T21:31:14Z",
"published": "2022-11-01T19:00:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-42316"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/YTMITQBGC23MSDHUCAPCVGLMVXIBXQTQ"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/YZVXG7OOOXCX6VIPEMLFDPIPUTFAYWPE"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/ZLI2NPNEH7CNJO3VZGQNOI4M4EWLNKPZ"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YTMITQBGC23MSDHUCAPCVGLMVXIBXQTQ"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YZVXG7OOOXCX6VIPEMLFDPIPUTFAYWPE"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZLI2NPNEH7CNJO3VZGQNOI4M4EWLNKPZ"
},
{
"type": "WEB",
"url": "https://www.debian.org/security/2022/dsa-5272"
},
{
"type": "WEB",
"url": "https://xenbits.xenproject.org/xsa/advisory-326.txt"
},
{
"type": "WEB",
"url": "http://xenbits.xen.org/xsa/advisory-326.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-XXPH-C9WW-HJ94
Vulnerability from github – Published: 2026-10-27 14:27 – Updated: 2026-10-27 14:27CompactHashMap/CompactHashSet/MapMakerInternalMap native deserialization triggers uncontrolled memory allocation
Summary
CompactHashMap, CompactHashSet, and MapMakerInternalMap implement readObject(ObjectInputStream) by reading an attacker-controlled elementCount or initialCapacity from the stream. On the very first insertion during or after deserialization (for the CompactHash* collections, inside allocArrays()) or immediately during deserialization (for MapMakerInternalMap), the implementation eagerly allocates arrays sized to that full, unvalidated value (up to CompactHashing.MAX_SIZE = 2^30-1, ~1.07 billion, for example) before the rest of the stream's actual content has been consumed. A 72-byte malicious serialized stream containing a single tiny element can force a multi-gigabyte allocation and crash a JVM with OutOfMemoryError, dynamically confirmed against the current release.
Details
CompactHashMap.readObject() (guava/src/com/google/common/collect/CompactHashMap.java):
private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException {
stream.defaultReadObject();
int elementCount = stream.readInt();
if (elementCount < 0) {
throw new InvalidObjectException("Invalid size: " + elementCount);
}
init(elementCount);
for (int i = 0; i < elementCount; i++) {
K key = (K) stream.readObject();
V value = (V) stream.readObject();
put(key, value);
}
}
elementCount is only checked for being non-negative; there is no upper bound and no relationship enforced between the declared count and how much data actually follows in the stream. init(elementCount) (line 251) just stores the value (clamped to [1, CompactHashing.MAX_SIZE], i.e. up to 2^30-1):
void init(int expectedSize) {
Preconditions.checkArgument(expectedSize >= 0, "Expected size must be >= 0");
this.metadata = Ints.constrainToRange(expectedSize, 1, CompactHashing.MAX_SIZE);
}
The real allocation is deferred to allocArrays(), which fires lazily on the first put() call — i.e. after only one key/value pair has actually been read off the wire:
int allocArrays() {
...
int expectedSize = metadata;
int buckets = CompactHashing.tableSize(expectedSize);
this.table = CompactHashing.createTable(buckets);
setHashTableMask(buckets - 1);
this.entries = new int[expectedSize];
this.keys = new Object[expectedSize];
this.values = new Object[expectedSize];
return expectedSize;
}
With expectedSize near CompactHashing.MAX_SIZE (2^30-1), this single call allocates a hash table plus three expectedSize-length arrays — multiple gigabytes — triggered by a stream that has, at that point, only supplied one small real element. CompactHashSet.readObject()/allocArrays() have the identical shape. CompactLinkedHashMap/CompactLinkedHashSet extend these classes and inherit the same unguarded path.
The problem with MapMakerInternalMap is similar, with a size read from the serialized data and stored in a MapMaker instance for later user during deserialization, when the MapMaker is passed back to MapMakerInternalMap and segments are allocated by calling newSegment, whose implementations (example) create Segment instances (example) that allocate arrays of the requested capacity through the newTable helper.
This is the same amplification shape as the already-fixed CVE-2018-10237 (AtomicDoubleArray/CompoundOrdering: attacker declares a large size, library eagerly allocates before validating against actual stream content). That fix rewrote AtomicDoubleArray.readObject() to grow an ImmutableLongArray.Builder incrementally per element actually read, rather than pre-allocating from the declared size — Guava's own HashBiMap.readObject() documents the same defensive principle explicitly in a comment: init(16); // resist hostile attempts to allocate gratuitous heap. CompactHashMap/CompactHashSet do not follow either of these two patterns already present elsewhere in the same codebase.
Reachability: All classes are top-level, implements ... Serializable, and require no special classpath setup beyond Guava being present (near-ubiquitous on JVM backends). A native Java deserialization attacker names the class directly in a hand-crafted stream — no particular Guava factory method needs to have been used by the victim application, exactly as with the precedent CVE.
PoC
Full source and run instructions in the attached poc-guava-compacthash-dos.zip (Maven project, guava:33.7.1-jre — the current latest release — as the only dependency). The PoC:
- Uses reflection only to legitimately obtain a
CompactHashSetinstance (it is package-private; a real attacker does not need this step, since they craft the serialized bytes directly rather than calling Guava's own serialization code). - Serializes one set with 1 element and one with 2 elements, diffs the two byte streams to locate the exact offset of the
elementCountfield (byte-precise, not guessed). - Patches that one 4-byte field in the 1-element stream from
1to0x3FFFFFFF(CompactHashing.MAX_SIZE), leaving the rest of the 72-byte stream (one real"A"element) untouched. - Feeds the resulting 72-byte malicious stream into a plain
new ObjectInputStream(...).readObject()call against the real, unmodifiedguava-33.7.1-jre.jar.
Actual run output:
### -Xmx8g (generous heap) ###
Located size field at offset 63, value=1 (expected 1)
Malicious stream size: 72 bytes
Declared elementCount: 1073741823 (~1.07 billion)
Real payload: 1 tiny string element ("A")
=== RESULT ===
Exception: java.lang.OutOfMemoryError: Java heap space
Time to allocate+fail: 4013 ms
Heap used before: 3 MB
Heap used after (at failure point): 4103 MB
JVM max heap (-Xmx): 8192 MB
### -Xmx128m (ordinary service-sized heap) ###
Located size field at offset 63, value=1 (expected 1)
Malicious stream size: 72 bytes
Declared elementCount: 1073741823 (~1.07 billion)
Real payload: 1 tiny string element ("A")
=== RESULT ===
Exception: java.lang.OutOfMemoryError: Java heap space
Time to allocate+fail: 24 ms
Heap used before: 4 MB
Heap used after (at failure point): 2 MB
JVM max heap (-Xmx): 128 MB
A 72-byte input forces ~4.1 GB of real allocation before failing on an 8 GB heap, and crashes a 128 MB heap in 24 ms — over 50,000x amplification between input size and forced allocation.
To reproduce: mvn -q compile dependency:build-classpath -Dmdep.outputFile=cp.txt && java -Xmx128m -cp "target/classes:$(cat cp.txt)" poc.Poc
Impact
Uncontrolled resource consumption (CWE-770 / CWE-400) via native Java deserialization: any application that runs ObjectInputStream.readObject() on data reachable by an attacker, and has Guava on its classpath (true for a large fraction of the JVM ecosystem), can be crashed with a single ~72-byte payload, regardless of what type the application itself expected to deserialize.
Severity
CVSS 3.1: AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H — identical vector to CVE-2018-10237, the precedent this is a sibling of. Recomputed with the full formula: exploitability = 8.22×0.85×0.44×0.85×0.85 = 2.221; ISS = 1-(1×1×0.44) = 0.56; impact = 6.42×0.56 = 3.595; base = roundup(5.816) = 5.9 (Medium).
Affected
com.google.guava:guava, all editions (-jre/-android) — confirmed on the current latest release, 33.7.1-jre. CompactHashMap/CompactHashSet/CompactLinkedHashMap/CompactLinkedHashSet have had this same readObject/allocArrays shape for a long time (probably since they were added in Guava 23.6), and similarly for MapMakerInternalMap (which was introduced in Guava 4.0); not tested against older individual releases.
Suggested fix
Apply the same defense already used elsewhere in this codebase: either clamp the initial init() capacity to a small constant regardless of the declared elementCount (as HashBiMap.readObject() already does — init(16); // resist hostile attempts to allocate gratuitous heap) and grow incrementally as elements are actually read, or grow the backing arrays incrementally per iteration of the deserialization loop (as the fixed AtomicDoubleArray.readObject() does via ImmutableLongArray.Builder) instead of eagerly sizing to the full attacker-declared count on first insert.
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 33.7.1"
},
"package": {
"ecosystem": "Maven",
"name": "com.google.guava:guava"
},
"ranges": [
{
"events": [
{
"introduced": "4.0"
},
{
"fixed": "33.7.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-102554"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-502",
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-10-27T14:27:53Z",
"nvd_published_at": "2026-10-09T16:17:20Z",
"severity": "HIGH"
},
"details": "# CompactHashMap/CompactHashSet/MapMakerInternalMap native deserialization triggers uncontrolled memory allocation\n\n## Summary\n\n`CompactHashMap`, `CompactHashSet`, and `MapMakerInternalMap` implement `readObject(ObjectInputStream)` by reading an attacker-controlled `elementCount` or `initialCapacity` from the stream. On the very first insertion during or after deserialization (for the `CompactHash*` collections, inside `allocArrays()`) or immediately during deserialization (for `MapMakerInternalMap`), the implementation eagerly allocates arrays sized to that full, unvalidated value (up to `CompactHashing.MAX_SIZE` = 2^30-1, ~1.07 billion, for example) before the rest of the stream\u0027s actual content has been consumed. A 72-byte malicious serialized stream containing a single tiny element can force a multi-gigabyte allocation and crash a JVM with `OutOfMemoryError`, dynamically confirmed against the current release.\n\n## Details\n\n`CompactHashMap.readObject()` (`guava/src/com/google/common/collect/CompactHashMap.java`):\n\n```java\nprivate void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException {\n stream.defaultReadObject();\n int elementCount = stream.readInt();\n if (elementCount \u003c 0) {\n throw new InvalidObjectException(\"Invalid size: \" + elementCount);\n }\n init(elementCount);\n for (int i = 0; i \u003c elementCount; i++) {\n K key = (K) stream.readObject();\n V value = (V) stream.readObject();\n put(key, value);\n }\n}\n```\n\n`elementCount` is only checked for being non-negative; there is no upper bound and no relationship enforced between the declared count and how much data actually follows in the stream. `init(elementCount)` (line 251) just stores the value (clamped to `[1, CompactHashing.MAX_SIZE]`, i.e. up to 2^30-1):\n\n```java\nvoid init(int expectedSize) {\n Preconditions.checkArgument(expectedSize \u003e= 0, \"Expected size must be \u003e= 0\");\n this.metadata = Ints.constrainToRange(expectedSize, 1, CompactHashing.MAX_SIZE);\n}\n```\n\nThe real allocation is deferred to `allocArrays()`, which fires lazily on the *first* `put()` call \u2014 i.e. after only one key/value pair has actually been read off the wire:\n\n```java\nint allocArrays() {\n ...\n int expectedSize = metadata;\n int buckets = CompactHashing.tableSize(expectedSize);\n this.table = CompactHashing.createTable(buckets);\n setHashTableMask(buckets - 1);\n this.entries = new int[expectedSize];\n this.keys = new Object[expectedSize];\n this.values = new Object[expectedSize];\n return expectedSize;\n}\n```\n\nWith `expectedSize` near `CompactHashing.MAX_SIZE` (2^30-1), this single call allocates a hash `table` plus three `expectedSize`-length arrays \u2014 multiple gigabytes \u2014 triggered by a stream that has, at that point, only supplied one small real element. `CompactHashSet.readObject()`/`allocArrays()` have the identical shape. `CompactLinkedHashMap`/`CompactLinkedHashSet` extend these classes and inherit the same unguarded path.\n\nThe problem with `MapMakerInternalMap` is similar, with [a size read from the serialized data and stored in a `MapMaker` instance](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L2848-L2851) for [later user during deserialization](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L2897), when [the `MapMaker` is passed back to `MapMakerInternalMap`](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMaker.java#L287) and [segments are allocated](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L173-L200) by [calling `newSegment`](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L1143), whose implementations ([example](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L443)) create `Segment` instances ([example](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L2019)) that [allocate arrays of the requested capacity](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L1234) through [the `newTable`](https://github.com/google/guava/blob/1278ad9c2257f48dc82fb7b7c625ab563605aa24/guava/src/com/google/common/collect/MapMakerInternalMap.java#L1262) helper.\n\nThis is the same amplification shape as the already-fixed **CVE-2018-10237** (`AtomicDoubleArray`/`CompoundOrdering`: attacker declares a large size, library eagerly allocates before validating against actual stream content). That fix rewrote `AtomicDoubleArray.readObject()` to grow an `ImmutableLongArray.Builder` incrementally per element actually read, rather than pre-allocating from the declared size \u2014 Guava\u0027s own `HashBiMap.readObject()` documents the same defensive principle explicitly in a comment: `init(16); // resist hostile attempts to allocate gratuitous heap`. `CompactHashMap`/`CompactHashSet` do not follow either of these two patterns already present elsewhere in the same codebase.\n\n**Reachability**: All classes are top-level, `implements ... Serializable`, and require no special classpath setup beyond Guava being present (near-ubiquitous on JVM backends). A native Java deserialization attacker names the class directly in a hand-crafted stream \u2014 no particular Guava factory method needs to have been used by the victim application, exactly as with the precedent CVE.\n\n## PoC\n\nFull source and run instructions in the attached `poc-guava-compacthash-dos.zip` (Maven project, `guava:33.7.1-jre` \u2014 the current latest release \u2014 as the only dependency). The PoC:\n\n1. Uses reflection only to legitimately obtain a `CompactHashSet` instance (it is package-private; a real attacker does not need this step, since they craft the serialized bytes directly rather than calling Guava\u0027s own serialization code).\n2. Serializes one set with 1 element and one with 2 elements, diffs the two byte streams to locate the exact offset of the `elementCount` field (byte-precise, not guessed).\n3. Patches that one 4-byte field in the 1-element stream from `1` to `0x3FFFFFFF` (`CompactHashing.MAX_SIZE`), leaving the rest of the 72-byte stream (one real `\"A\"` element) untouched.\n4. Feeds the resulting 72-byte malicious stream into a plain `new ObjectInputStream(...).readObject()` call against the real, unmodified `guava-33.7.1-jre.jar`.\n\nActual run output:\n\n```\n### -Xmx8g (generous heap) ###\nLocated size field at offset 63, value=1 (expected 1)\nMalicious stream size: 72 bytes\nDeclared elementCount: 1073741823 (~1.07 billion)\nReal payload: 1 tiny string element (\"A\")\n=== RESULT ===\nException: java.lang.OutOfMemoryError: Java heap space\nTime to allocate+fail: 4013 ms\nHeap used before: 3 MB\nHeap used after (at failure point): 4103 MB\nJVM max heap (-Xmx): 8192 MB\n\n### -Xmx128m (ordinary service-sized heap) ###\nLocated size field at offset 63, value=1 (expected 1)\nMalicious stream size: 72 bytes\nDeclared elementCount: 1073741823 (~1.07 billion)\nReal payload: 1 tiny string element (\"A\")\n=== RESULT ===\nException: java.lang.OutOfMemoryError: Java heap space\nTime to allocate+fail: 24 ms\nHeap used before: 4 MB\nHeap used after (at failure point): 2 MB\nJVM max heap (-Xmx): 128 MB\n```\n\nA 72-byte input forces ~4.1 GB of real allocation before failing on an 8 GB heap, and crashes a 128 MB heap in 24 ms \u2014 over 50,000x amplification between input size and forced allocation.\n\nTo reproduce: `mvn -q compile dependency:build-classpath -Dmdep.outputFile=cp.txt \u0026\u0026 java -Xmx128m -cp \"target/classes:$(cat cp.txt)\" poc.Poc`\n\n## Impact\n\nUncontrolled resource consumption (CWE-770 / CWE-400) via native Java deserialization: any application that runs `ObjectInputStream.readObject()` on data reachable by an attacker, and has Guava on its classpath (true for a large fraction of the JVM ecosystem), can be crashed with a single ~72-byte payload, regardless of what type the application itself expected to deserialize.\n\n## Severity\n\nCVSS 3.1: `AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H` \u2014 identical vector to CVE-2018-10237, the precedent this is a sibling of. Recomputed with the full formula: exploitability = 8.22\u00d70.85\u00d70.44\u00d70.85\u00d70.85 = 2.221; ISS = 1-(1\u00d71\u00d70.44) = 0.56; impact = 6.42\u00d70.56 = 3.595; base = roundup(5.816) = **5.9 (Medium)**.\n\n## Affected\n\n`com.google.guava:guava`, all editions (`-jre`/`-android`) \u2014 confirmed on the current latest release, 33.7.1-jre. `CompactHashMap`/`CompactHashSet`/`CompactLinkedHashMap`/`CompactLinkedHashSet` have had this same `readObject`/`allocArrays` shape for a long time (probably since they were added in Guava 23.6), and similarly for `MapMakerInternalMap` (which was introduced in Guava 4.0); not tested against older individual releases.\n\n## Suggested fix\n\nApply the same defense already used elsewhere in this codebase: either clamp the initial `init()` capacity to a small constant regardless of the declared `elementCount` (as `HashBiMap.readObject()` already does \u2014 `init(16); // resist hostile attempts to allocate gratuitous heap`) and grow incrementally as elements are actually read, or grow the backing arrays incrementally per iteration of the deserialization loop (as the fixed `AtomicDoubleArray.readObject()` does via `ImmutableLongArray.Builder`) instead of eagerly sizing to the full attacker-declared count on first insert.",
"id": "GHSA-xxph-c9ww-hj94",
"modified": "2026-10-27T14:27:53Z",
"published": "2026-10-27T14:27:53Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/google/guava/security/advisories/GHSA-xxph-c9ww-hj94"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-102554"
},
{
"type": "WEB",
"url": "https://github.com/google/guava/commit/b931fe9d6d5cf00bc55714ad3308d086f71850fe"
},
{
"type": "PACKAGE",
"url": "https://github.com/google/guava"
},
{
"type": "WEB",
"url": "https://github.com/google/guava/releases/tag/v33.7.2"
}
],
"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:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Google Guava: CompactHashMap/CompactHashSet/MapMakerInternalMap deserialization triggers uncontrolled memory allocation"
}
GHSA-XXPV-GWRV-58XV
Vulnerability from github – Published: 2023-04-11 21:31 – Updated: 2025-02-11 18:31Aten PE8108 2.4.232 is vulnerable to denial of service (DOS).
{
"affected": [],
"aliases": [
"CVE-2023-25414"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2023-04-11T21:15:00Z",
"severity": "MODERATE"
},
"details": "Aten PE8108 2.4.232 is vulnerable to denial of service (DOS).",
"id": "GHSA-xxpv-gwrv-58xv",
"modified": "2025-02-11T18:31:11Z",
"published": "2023-04-11T21:31:01Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25414"
},
{
"type": "WEB",
"url": "https://www.pentagrid.ch/en/blog/multiple-vulnerabilities-in-aten-PE8108-power-distribution-unit"
}
],
"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"
}
]
}
GHSA-XXQJ-X2PV-X5JJ
Vulnerability from github – Published: 2024-07-03 18:48 – Updated: 2024-07-05 18:34Uncontrolled Resource Consumption vulnerability in MESbook 20221021.03 version. An unauthenticated remote attacker can use the "message" parameter to inject a payload with dangerous JavaScript code, causing the application to loop requests on itself, which could lead to resource consumption and disable the application.
{
"affected": [],
"aliases": [
"CVE-2024-6427"
],
"database_specific": {
"cwe_ids": [
"CWE-400",
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-07-03T12:15:03Z",
"severity": "HIGH"
},
"details": "Uncontrolled Resource Consumption vulnerability in MESbook\u00a020221021.03 version. An unauthenticated remote attacker can use the \"message\" parameter to inject a payload with dangerous JavaScript code, causing the application to loop requests on itself, which could lead to resource consumption and disable the application.",
"id": "GHSA-xxqj-x2pv-x5jj",
"modified": "2024-07-05T18:34:17Z",
"published": "2024-07-03T18:48:14Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-6427"
},
{
"type": "WEB",
"url": "https://www.incibe.es/en/incibe-cert/notices/aviso-sci/multiple-vulnerabilities-mesbook"
}
],
"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-XXXP-8X92-F69V
Vulnerability from github – Published: 2023-06-22 15:30 – Updated: 2024-04-04 05:01An issue in the BLOBcmp component of MonetDB Server v11.45.17 and v11.46.0 allows attackers to cause a Denial of Service (DoS) via crafted SQL statements.
{
"affected": [],
"aliases": [
"CVE-2023-36367"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2023-06-22T14:15:09Z",
"severity": "HIGH"
},
"details": "An issue in the BLOBcmp component of MonetDB Server v11.45.17 and v11.46.0 allows attackers to cause a Denial of Service (DoS) via crafted SQL statements.",
"id": "GHSA-xxxp-8x92-f69v",
"modified": "2024-04-04T05:01:00Z",
"published": "2023-06-22T15:30:24Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-36367"
},
{
"type": "WEB",
"url": "https://github.com/MonetDB/MonetDB/issues/7380"
}
],
"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"
}
]
}
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.