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Wycheproof · trailofbits | DecimalAI

Securityby trailofbits

Wycheproof

Wycheproof provides test vectors for validating cryptographic implementations.

Never made a case worse

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

77%95%of test cases passed +18 pts

Without skill17 of 22

With skill21 of 22

4 fixed17 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/7/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen fetching Wycheproof test vectors in a CI pipeline from the official google/wycheproof GitHub repository, which directory path contains the stable v1 JSON test vector files?

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 trailofbits-wycheproof
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("trailofbits-wycheproof")
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 trailofbits-wycheproof

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/trailofbits-wycheproof/SKILL.md https://app.decimal.ai/s/trailofbits-wycheproof/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/trailofbits-wycheproof/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/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/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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("trailofbits-wycheproof")
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/trailofbits-wycheproof/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/trailofbits-wycheproof/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 1 other file is 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 trailofbits-wycheproof

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/trailofbits-wycheproof/SKILL.md https://app.decimal.ai/s/trailofbits-wycheproof/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/trailofbits-wycheproof/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/trailofbits-wycheproof/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/trailofbits-wycheproof.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/7/2026
Source: trailofbits/skills ↗
License: CC-BY-SA-4.0

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

Wycheproof

Wycheproof is an extensive collection of test vectors designed to verify the correctness of cryptographic implementations and test against known attacks. Originally developed by Google, it is now a community-managed project where contributors can add test vectors for specific cryptographic constructions.

Background

Key Concepts

Concept	Description
Test vector	Input/output pair for validating crypto implementation correctness
Test group	Collection of test vectors sharing attributes (key size, IV size, curve)
Result flag	Indicates if test should pass (valid), fail (invalid), or is acceptable
Edge case testing	Testing for known vulnerabilities and attack patterns

Why This Matters

Cryptographic implementations are notoriously difficult to get right. Even small bugs can:

Expose private keys
Allow signature forgery
Enable message decryption
Create consensus problems when different implementations accept/reject the same inputs

Wycheproof has found vulnerabilities in major libraries including OpenJDK's SHA1withDSA, Bouncy Castle's ECDHC, and the elliptic npm package.

When to Use

Apply Wycheproof when:

Testing cryptographic implementations (AES-GCM, ECDSA, ECDH, RSA, etc.)
Validating that crypto code handles edge cases correctly
Verifying implementations against known attack vectors
Setting up CI/CD for cryptographic libraries
Auditing third-party crypto code for correctness

Consider alternatives when:

Testing for timing side-channels (use constant-time testing tools instead)
Finding new unknown bugs (use fuzzing instead)
Testing custom/experimental cryptographic algorithms (Wycheproof only covers established algorithms)

Quick Reference

Scenario	Recommended Approach	Notes
AES-GCM implementation	Use `aes_gcm_test.json`	316 test vectors across 44 test groups
ECDSA verification	Use `ecdsa_*_test.json` for specific curves	Tests signature malleability, DER encoding
ECDH key exchange	Use `ecdh_*_test.json`	Tests invalid curve attacks
RSA signatures	Use `rsa_*_test.json`	Tests padding oracle attacks
ChaCha20-Poly1305	Use `chacha20_poly1305_test.json`	Tests AEAD implementation

Testing Workflow

Phase 1: Setup                 Phase 2: Parse Test Vectors
┌─────────────────┐          ┌─────────────────┐
│ Add Wycheproof  │    →     │ Load JSON file  │
│ as submodule    │          │ Filter by params│
└─────────────────┘          └─────────────────┘
         ↓                            ↓
Phase 4: CI Integration        Phase 3: Write Harness
┌─────────────────┐          ┌─────────────────┐
│ Auto-update     │    ←     │ Test valid &    │
│ test vectors    │          │ invalid cases   │
└─────────────────┘          └─────────────────┘

Repository Structure

The Wycheproof repository is organized as follows:

┣ 📜 README.md       : Project overview
┣ 📂 doc             : Documentation
┣ 📂 java            : Java JCE interface testing harness
┣ 📂 javascript      : JavaScript testing harness
┣ 📂 schemas         : Test vector schemas
┣ 📂 testvectors     : Test vectors
┗ 📂 testvectors_v1  : Updated test vectors (more detailed)

The essential folders are testvectors and testvectors_v1. While both contain similar files, testvectors_v1 includes more detailed information and is recommended for new integrations.

Supported Algorithms

Wycheproof provides test vectors for a wide range of cryptographic algorithms:

Category	Algorithms
Symmetric Encryption	AES-GCM, AES-EAX, ChaCha20-Poly1305
Signatures	ECDSA, EdDSA, RSA-PSS, RSA-PKCS1
Key Exchange	ECDH, X25519, X448
Hashing	HMAC, HKDF
Curves	secp256k1, secp256r1, secp384r1, secp521r1, ed25519, ed448

Test File Structure

Each JSON test file tests a specific cryptographic construction. All test files share common attributes:

"algorithm"         : The name of the algorithm tested
"schema"            : The JSON schema (found in schemas folder)
"generatorVersion"  : The version number
"numberOfTests"     : The total number of test vectors in this file
"header"            : Detailed description of test vectors
"notes"             : In-depth explanation of flags in test vectors
"testGroups"        : Array of one or multiple test groups

Test Groups

Test groups group sets of tests based on shared attributes such as:

Key sizes
IV sizes
Public keys
Curves

This classification allows extracting tests that meet specific criteria relevant to the construction being tested.

Test Vector Attributes

Shared Attributes

All test vectors contain four common fields:

tcId: Unique identifier for the test vector within a file
comment: Additional information about the test case
flags: Descriptions of specific test case types and potential dangers (referenced in notes field)
result: Expected outcome of the test

The result field can take three values:

Result	Meaning
valid	Test case should succeed
acceptable	Test case is allowed to succeed but contains non-ideal attributes
invalid	Test case should fail

Unique Attributes

Unique attributes are specific to the algorithm being tested:

Algorithm	Unique Attributes
AES-GCM	`key`, `iv`, `aad`, `msg`, `ct`, `tag`
ECDH secp256k1	`public`, `private`, `shared`
ECDSA	`msg`, `sig`, `result`
EdDSA	`msg`, `sig`, `pk`

Implementation Guide

Phase 1: Add Wycheproof to Your Project

Option 1: Git Submodule (Recommended)

Adding Wycheproof as a git submodule ensures automatic updates:

git submodule add https://github.com/C2SP/wycheproof.git

Option 2: Fetch Specific Test Vectors

If submodules aren't possible, fetch specific JSON files:

#!/bin/bash

TMP_WYCHEPROOF_FOLDER=".wycheproof/"
TEST_VECTORS=('aes_gcm_test.json' 'aes_eax_test.json')
BASE_URL="https://raw.githubusercontent.com/C2SP/wycheproof/master/testvectors_v1/"

# Create wycheproof folder
mkdir -p $TMP_WYCHEPROOF_FOLDER

# Request all test vector files if they don't exist
for i in "${TEST_VECTORS[@]}"; do
  if [ ! -f "${TMP_WYCHEPROOF_FOLDER}${i}" ]; then
    curl -o "${TMP_WYCHEPROOF_FOLDER}${i}" "${BASE_URL}${i}"
    if [ $? -ne 0 ]; then
      echo "Failed to download ${i}"
      exit 1
    fi
  fi
done

Phase 2: Parse Test Vectors

Identify the test file for your algorithm and parse the JSON:

Python Example:

import json

def load_wycheproof_test_vectors(path: str):
    testVectors = []
    try:
        with open(path, "r") as f:
            wycheproof_json = json.loads(f.read())
    except FileNotFoundError:
        print(f"No Wycheproof file found at: {path}")
        return testVectors

    # Attributes that need hex-to-bytes conversion
    convert_attr = {"key", "aad", "iv", "msg", "ct", "tag"}

    for testGroup in wycheproof_json["testGroups"]:
        # Filter test groups based on implementation constraints
        if testGroup["ivSize"] < 64 or testGroup["ivSize"] > 1024:
            continue

        for tv in testGroup["tests"]:
            # Convert hex strings to bytes
            for attr in convert_attr:
                if attr in tv:
                    tv[attr] = bytes.fromhex(tv[attr])
            testVectors.append(tv)

    return testVectors

JavaScript Example:

const fs = require('fs').promises;

async function loadWycheproofTestVectors(path) {
  const tests = [];

  try {
    const fileContent = await fs.readFile(path);
    const data = JSON.parse(fileContent.toString());

    data.testGroups.forEach(testGroup => {
      testGroup.tests.forEach(test => {
        // Add shared test group properties to each test
        test['pk'] = testGroup.publicKey.pk;
        tests.push(test);
      });
    });
  } catch (err) {
    console.error('Error reading or parsing file:', err);
    throw err;
  }

  return tests;
}

Phase 3: Write Testing Harness

Create test functions that handle both valid and invalid test cases.

Python/pytest Example:

import pytest
from cryptography.hazmat.primitives.ciphers.aead import AESGCM

tvs = load_wycheproof_test_vectors("wycheproof/testvectors_v1/aes_gcm_test.json")

@pytest.mark.parametrize("tv", tvs, ids=[str(tv['tcId']) for tv in tvs])
def test_encryption(tv):
    try:
        aesgcm = AESGCM(tv['key'])
        ct = aesgcm.encrypt(tv['iv'], tv['msg'], tv['aad'])
    except ValueError as e:
        # Implementation raised error - verify test was expected to fail
        assert tv['result'] != 'valid', tv['comment']
        return

    if tv['result'] == 'valid':
        assert ct[:-16] == tv['ct'], f"Ciphertext mismatch: {tv['comment']}"
        assert ct[-16:] == tv['tag'], f"Tag mismatch: {tv['comment']}"
    elif tv['result'] == 'invalid' or tv['result'] == 'acceptable':
        assert ct[:-16] != tv['ct'] or ct[-16:] != tv['tag']

@pytest.mark.parametrize("tv", tvs, ids=[str(tv['tcId']) for tv in tvs])
def test_decryption(tv):
    try:
        aesgcm = AESGCM(tv['key'])
        decrypted_msg = aesgcm.decrypt(tv['iv'], tv['ct'] + tv['tag'], tv['aad'])
    except ValueError:
        assert tv['result'] != 'valid', tv['comment']
        return
    except InvalidTag:
        assert tv['result'] != 'valid', tv['comment']
        assert 'ModifiedTag' in tv['flags'], f"Expected 'ModifiedTag' flag: {tv['comment']}"
        return

    assert tv['result'] == 'valid', f"No invalid test case should pass: {tv['comment']}"
    assert decrypted_msg == tv['msg'], f"Decryption mismatch: {tv['comment']}"

JavaScript/Mocha Example:

const assert = require('assert');

function testFactory(tcId, tests) {
  it(`[${tcId + 1}] ${tests[tcId].comment}`, function () {
    const test = tests[tcId];
    const ed25519 = new eddsa('ed25519');
    const key = ed25519.keyFromPublic(toArray(test.pk, 'hex'));

    let sig;
    if (test.result === 'valid') {
      sig = key.verify(test.msg, test.sig);
      assert.equal(sig, true, `[${test.tcId}] ${test.comment}`);
    } else if (test.result === 'invalid') {
      try {
        sig = key.verify(test.msg, test.sig);
      } catch (err) {
        // Point could not be decoded
        sig = false;
      }
      assert.equal(sig, false, `[${test.tcId}] ${test.comment}`);
    }
  });
}

// Generate tests for all test vectors
for (var tcId = 0; tcId < tests.length; tcId++) {
  testFactory(tcId, tests);
}

Phase 4: CI Integration

Ensure test vectors stay up to date by:

Using git submodules: Update submodule in CI before running tests
Fetching latest vectors: Run fetch script before test execution
Scheduled updates: Set up weekly/monthly updates to catch new test vectors

Common Vulnerabilities Detected

Wycheproof test vectors are designed to catch specific vulnerability patterns:

Vulnerability	Description	Affected Algorithms	Example CVE
Signature malleability	Multiple valid signatures for same message	ECDSA, EdDSA	CVE-2024-42459
Invalid DER encoding	Accepting non-canonical DER signatures	ECDSA	CVE-2024-42460, CVE-2024-42461
Invalid curve attacks	ECDH with invalid curve points	ECDH	Common in many libraries
Padding oracle	Timing leaks in padding validation	RSA-PKCS1	Historical OpenSSL issues
Tag forgery	Accepting modified authentication tags	AES-GCM, ChaCha20-Poly1305	Various implementations

Signature Malleability: Deep Dive

Problem: Implementations that don't validate signature encoding can accept multiple valid signatures for the same message.

Example (EdDSA): Appending or removing zeros from signature:

Valid signature:   ...6a5c51eb6f946b30d
Invalid signature: ...6a5c51eb6f946b30d0000  (should be rejected)

How to detect:

# Add signature length check
if len(sig) != 128:  # EdDSA signatures must be exactly 64 bytes (128 hex chars)
    return False

Impact: Can lead to consensus problems when different implementations accept/reject the same signatures.

Related Wycheproof tests:

EdDSA: tcId 37 - "removing 0 byte from signature"
ECDSA: tcId 06 - "Legacy: ASN encoding of r misses leading 0"

Case Study: Elliptic npm Package

This case study demonstrates how Wycheproof found three CVEs in the popular elliptic npm package (3000+ dependents, millions of weekly downloads).

Overview

The elliptic library is an elliptic-curve cryptography library written in JavaScript, supporting ECDH, ECDSA, and EdDSA. Using Wycheproof test vectors on version 6.5.6 revealed multiple vulnerabilities:

CVE-2024-42459: EdDSA signature malleability (appending/removing zeros)
CVE-2024-42460: ECDSA DER encoding - invalid bit placement
CVE-2024-42461: ECDSA DER encoding - leading zero in length field

Methodology

Identify supported curves: ed25519 for EdDSA
Find test vectors: testvectors_v1/ed25519_test.json
Parse test vectors: Load JSON and extract tests
Write test harness: Create parameterized tests
Run tests: Identify failures
Analyze root causes: Examine implementation code
Propose fixes: Add validation checks

Key Findings

EdDSA Issue (CVE-2024-42459):

Missing signature length validation
Allowed trailing zeros in signatures
Fix: Add if(sig.length !== 128) return false;

ECDSA Issue 1 (CVE-2024-42460):

Missing check for first bit being zero in DER-encoded r and s values
Fix: Add if ((data[p.place] & 128) !== 0) return false;

ECDSA Issue 2 (CVE-2024-42461):

DER length field accepted leading zeros
Fix: Add if(buf[p.place] === 0x00) return false;

Impact

All three vulnerabilities allowed multiple valid signatures for a single message, leading to consensus problems across implementations.

Lessons learned:

Wycheproof catches subtle encoding bugs
Reusable test harnesses pay dividends
Test vector comments and flags help diagnose issues
Even popular libraries benefit from systematic test vector validation

Advanced Usage

Tips and Tricks

Tip	Why It Helps
Filter test groups by parameters	Focus on test vectors relevant to your implementation constraints
Use test vector flags	Understand specific vulnerability patterns being tested
Check the `notes` field	Get detailed explanations of flag meanings
Test both encrypt/decrypt and sign/verify	Ensure bidirectional correctness
Run tests in CI	Catch regressions and benefit from new test vectors
Use parameterized tests	Get clear failure messages with tcId and comment

Common Mistakes

Mistake	Why It's Wrong	Correct Approach
Only testing valid cases	Misses vulnerabilities where invalid inputs are accepted	Test all result types: valid, invalid, acceptable
Ignoring "acceptable" result	Implementation might have subtle bugs	Treat acceptable as warnings worth investigating
Not filtering test groups	Wastes time on unsupported parameters	Filter by keySize, ivSize, etc. based on your implementation
Not updating test vectors	Miss new vulnerability patterns	Use submodules or scheduled fetches
Testing only one direction	Encrypt/sign might work but decrypt/verify fails	Test both operations

Related Skills

Tool Skills

Skill	Primary Use in Wycheproof Testing
pytest	Python testing framework for parameterized tests
mocha	JavaScript testing framework for test generation
constant-time-testing	Complement Wycheproof with timing side-channel testing
cryptofuzz	Fuzz-based crypto testing to find additional bugs

Technique Skills

Skill	When to Apply
coverage-analysis	Ensure test vectors cover all code paths in crypto implementation
property-based-testing	Test mathematical properties (e.g., encrypt/decrypt round-trip)
fuzz-harness-writing	Create harnesses for crypto parsers (complements Wycheproof)

Related Domain Skills

Skill	Relationship
crypto-testing	Wycheproof is a key tool in comprehensive crypto testing methodology
fuzzing	Use fuzzing to find bugs Wycheproof doesn't cover (new edge cases)

Skill Dependency Map

                    ┌─────────────────────┐
                    │    wycheproof       │
                    │   (this skill)      │
                    └──────────┬──────────┘
                               │
           ┌───────────────────┼───────────────────┐
           │                   │                   │
           ▼                   ▼                   ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│  pytest/mocha   │ │ constant-time   │ │   cryptofuzz    │
│ (test framework)│ │   testing       │ │   (fuzzing)     │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
         │                   │                   │
         └───────────────────┼───────────────────┘
                             │
                             ▼
              ┌──────────────────────────┐
              │   Technique Skills       │
              │ coverage, harness, PBT   │
              └──────────────────────────┘

Resources

Official Repository

Wycheproof GitHub Repository

The official repository contains:

All test vectors in testvectors/ and testvectors_v1/
JSON schemas in schemas/
Reference implementations in Java and JavaScript
Documentation in doc/

Real-World Examples

pycryptodome

The pycryptodome library integrates Wycheproof test vectors in their test suite, demonstrating best practices for Python crypto implementations.

Community Resources

C2SP Community - Cryptographic specifications and standards community maintaining Wycheproof
Wycheproof issues tracker - Report bugs in test vectors or suggest new constructions

Summary

Wycheproof is an essential tool for validating cryptographic implementations against known attack vectors and edge cases. By integrating Wycheproof test vectors into your testing workflow:

Catch subtle encoding and validation bugs
Prevent signature malleability issues
Ensure consistent behavior across implementations
Benefit from community-contributed test vectors
Protect against known cryptographic vulnerabilities

The investment in writing a reusable testing harness pays dividends through continuous validation as new test vectors are added to the Wycheproof repository.

When it activates

Automatically, when a task matches. Use when testing crypto code for known attacks and edge cases.

About this skill

Automatic

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Source

Imported

trailofbits / skills

plugins/testing-handbook-skills/skills/wycheproof/SKILL.md

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Wycheproof

Background

Key Concepts

Why This Matters

When to Use

Quick Reference

Testing Workflow

Repository Structure

Supported Algorithms

Test File Structure

Test Groups

Test Vector Attributes

Shared Attributes

Unique Attributes

Implementation Guide

Phase 1: Add Wycheproof to Your Project

Phase 2: Parse Test Vectors

Phase 3: Write Testing Harness

Phase 4: CI Integration

Common Vulnerabilities Detected

Signature Malleability: Deep Dive

Case Study: Elliptic npm Package

Overview

Methodology

Key Findings

Impact

Advanced Usage

Tips and Tricks

Common Mistakes

Related Skills

Tool Skills

Technique Skills

Related Domain Skills

Skill Dependency Map

Resources

Official Repository

Real-World Examples

Community Resources

Summary

When it activates

SkillSafety

Source

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