Denial of Service via Eager Array Allocation During Deserialization in Guava (CVE-2026-102554)
Published Last modified Sources: Msrc Update Guide, BSI, OSV, SUSE, Hersteller, CVE.org
What is affected, what to do
Update Google Guava to version 33.7.2.
- Affected
- Google Guava
lt 4.0..33.7.2
- Google Guava
- Fixed in
- Google Guava
33.7.2
- Google Guava
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Metrics
8.2
Priority
XIEM Effective CVSS: highest available CVSS rating from NVD and vendor data.
Source: Vendor (CNA), CVSS v4
8.2
CVSS v4
Severity High on the scale from 0 to 10.
Source: Msrc Update Guide, BSI, OSV, SUSE, Hersteller, CVE.org
0.4 %
EPSS score
Low — model estimates < 1% exploitation likelihood.
Source: FIRST EPSS, percentile 28.8
Show all metrics
- Severity
- High
Priority (0–325)
The priority score is a consolidated metric that condenses several individual signals into a single, action-guiding ranking: technical severity (CVSS), exploitation likelihood (EPSS), known-exploited status (KEV) and the context of the affected asset. The aim is to answer the only truly important question in vulnerability management: what first?
Read in glossary- 77.63 Methodology
Exploit maturity
An exploit is code or a technique that specifically uses a concrete vulnerability to force unintended behaviour — such as executing code or escalating privileges. It is the tool that turns a theoretical flaw into a practical attack.
Read in glossary- no public PoC known
XIEM Effective CVSS
XIEM® is the tiered managed-security operating model from NEOSEC and a registered trademark of NEOSEC GmbH. It extends classic SIEM — the central collection, normalisation and correlation of security-relevant events — with endpoint forensics, network visibility and deception technology, and consolidates them into three building tiers: Sentry, Orchestrate and Command.
Read in glossary- 8.2 Methodology
CVSS v4
CVSS is the globally established, open standard for assessing the technical severity of a vulnerability on a scale from 0 to 10. It describes how easily a flaw can be exploited and what damage it can cause — but not how likely its actual exploitation is. CVSS and the complementary EPSS deliberately answer different questions.
Read in glossary- 8.2
EPSS score
The EPSS score is the absolute model output for an individual vulnerability: the estimated probability of real-world exploitation within the next 30 days, expressed as a value between 0 and 1 (i.e. 0 % to 100 %). A score of 0.42 means „the model estimates a 42 % chance of real-world exploitation within 30 days“.
Read in glossary- 0.4 %
- Low — model estimates < 1% exploitation likelihood.
EPSS percentile
The EPSS percentile is the relative position of a vulnerability within all EPSS-scored CVEs, expressed as a value between 0 and 1. A percentile of 0.90 means „this CVE has a higher EPSS score than 90 % of all other scored CVEs“. Unlike the EPSS score, it is not a probability — but a positioning within a ranking.
Read in glossary- 28.8
- Elevated — this CVE ranks above at least 10% of all CVEs scored today.
KEV status
The KEV catalogue is a list maintained by the US authority CISA of vulnerabilities for which active exploitation in the wild has been confirmed. It answers not the question „how severe?“ but „is it already being attacked?“ — thereby sharply prioritising patching.
Read in glossary- —
- Published
- CISA assessment (SSVC)
- Exploitation: proof of concept · not automatable · Technical impact: partial
- Source: CISA Vulnrichment, as of 9 October 2026
CWE
CWE is a catalogue of typical weakness types in software and hardware — such as flawed input validation, unsafe memory handling or poor authentication. Where a CVE names a concrete vulnerability in a concrete product, a CWE describes the underlying class of defect.
Read in glossary- CWE-400, CWE-502, CWE-770
Detection rules
No NEOSEC detection rule is available for this vulnerability yet.
Classification under NIS2 and CRA
Rule-based classification from exploitation status and severity. It does not replace a case-by-case assessment.
NIS2
NIS2 is EU Directive (EU) 2022/2555 to raise the level of cybersecurity across the Union. It substantially broadens obligations compared with the first NIS directive: more sectors, mandatory risk management, staged reporting duties and personal accountability of senior management.
Read in glossary- High technical severity: assess and remediate the vulnerability in vulnerability handling under Art. 21(2)(e) NIS2 and document the decision.
CRA
The CRA is EU Regulation (EU) 2024/2847 setting horizontal cybersecurity requirements for „products with digital elements“ — from industrial controllers and software to connected consumer devices. For the first time manufacturers must ensure security across the entire product lifecycle, handle vulnerabilities and provide security updates.
Read in glossary- From 11.12.2027, Annex I Part II CRA requires manufacturers of affected products with digital elements to handle vulnerabilities without delay and provide security updates.
ISO/IEC 27001
ISO/IEC 27001 is the internationally leading standard for an information security management system (ISMS). It describes a risk-based, process-oriented framework to systematically plan, implement, review and improve information security.
Read in glossary- Control A.8.8, management of technical vulnerabilities: assess relevance to your systems, plan remediation and keep evidence.
Description
CompactHashMap/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
Source: Microsoft Security Update Guide, BSI CSAF, BSI WID Portal, OSV (Open Source Vulnerabilities), suse_csaf, GitHub Security Advisories, cvelistv5, NVD (NIST)
Affected products
The following versions are affected. Older releases of the same product line are also vulnerable unless stated otherwise.
| Affected versions | Fixed in | ||
|---|---|---|---|
| Guava |
|
| |
| IBM | SPSS |
|
|
| maven | com.google.guava:guava |
|
|
Vendors and products
Weakness classes (CWE)
CWE-400Uncontrolled Resource Consumption
Class
The product does not properly control the allocation and maintenance of a limited resource.
Entry at MITRE CWE-400CWE-502Deserialization of Untrusted Data
Base
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
Entry at MITRE CWE-502CWE-770Allocation of Resources Without Limits or Throttling
Base
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.
Entry at MITRE CWE-770
References & sources
- github.comgoogle/guava/security/advisories/GHSA-xxph-c9ww-hj94web
- github.comgoogle/guava/commit/b931fe9d6d5cf00bc55714ad3308d086f71850feweb
- github.comgoogle/guava/releases/tag/v33.7.2web
- NVD (NIST)vuln/detail/CVE-2026-102554advisory
- github.comgoogle/guavapackage
Changes to this CVE
Who changed this CVE and when: NVD (analysis, affected products, CVSS), the CVE publisher (CNA) and additional providers such as CISA (ADP).
Record modifiedCISA ADP
CISA assessment (SSVC): exploitation: proof of concept, automatable: no
Show 2 original changes
Individual changes of this event as published by NVD Type Action Before After Reference added —https://github.com/google/guava/security/advisories/GHSA-xxph-c9ww-hj94SSVC assessment added —{"id":"CVE-2026-102554","role":"CISA Coordinator","options":[{"exploitation":"poc"},{"automatable":"no"},{"technicalImpact":"partial"}],"version":"2.0.3","timestamp":"2026-10-09T18:09:30.467336Z"}Source identifier: 134c704f-9b21-4f2e-91b3-4a467353bcc0
Receivedgoogle.com
CVSS V4.0: AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
Weakness type: CWE-502, CWE-770
Description added or changed
Show 8 original changes
Individual changes of this event as published by NVD Type Action Before After Description added —Allocation of resources without limits or throttling (CWE-770) during Java object deserialization in Google Guava versions 4.0 through 33.7.1 allows an attacker to cause a Denial of Service via OutOfMemoryError. When deserializing CompactHashMap, CompactHashSet, or MapMakerInternalMap instances, Guava eagerly allocates an array based on a caller-specified size parameter without throttling, permitting memory exhaustion from crafted serialization streams.CVSS added —AV:N/AC:L/AT:P/PR:N/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:XWeakness type (CWE) added —CWE-502Weakness type (CWE) added —CWE-770Affected versions (publisher) added —New affected value received. <a href="https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/102xxx/CVE-2026-102554.json">CVE-2026-102554</a>Reference added —https://github.com/google/guava/commit/b931fe9d6d5cf00bc55714ad3308d086f71850feReference added —https://github.com/google/guava/releases/tag/v33.7.2Reference added —https://github.com/google/guava/security/advisories/GHSA-xxph-c9ww-hj94Source identifier: cve-coordination@google.com
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