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security-authOfficialpython

Signing And Verifying (Official Sample)

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by A2A Project

Official A2A python sample agent: Signing And Verifying

1,329 starsUpdated 2026-02-22apache-2.0
Quality Score59/100
Community
70
Freshness
100
Official
100
Skills
10
Protocol
30
🔒 Security
20

Getting Started

1Clone the repository
$ git clone https://github.com/a2aproject/a2a-samples
2Navigate to the project
$ cd a2a-samples/samples/python/agents/signing_and_verifying
3Install dependencies
$ pip install -r requirements.txt
4Run the agent
$ python main.py

README

Signing and Verifying Example

Signed agent used as an example for AgentCard signing and verifying.

Read more about signing and verifying AgentCards here: Agent Card Signing.

Getting started

  1. Start the server

    uv run .
    
  2. Run the test client

    uv run test_client.py
    

Build Container Image

Agent can also be built using a container file.

  1. Navigate to the directory samples/python/agents/signing_and_verifying directory:
cd samples/python/agents/signing_and_verifying
  1. Build the container file

    podman build . -t signing_and_verifying-a2a-server
    

[!Tip]
Podman is a drop-in replacement for docker which can also be used in these commands.

  1. Run your container

    podman run -p 9999:9999 signing_and_verifying-a2a-server
    

Validate

To validate in a separate terminal, run the A2A client:

cd samples/python/hosts/cli
uv run . --agent http://localhost:9999

Disclaimer

Important: The sample code provided is for demonstration purposes and illustrates the mechanics of the Agent-to-Agent (A2A) protocol. When building production applications, it is critical to treat any agent operating outside of your direct control as a potentially untrusted entity.

All data received from an external agent—including but not limited to its AgentCard, messages, artifacts, and task statuses—should be handled as untrusted input. For example, a malicious agent could provide an AgentCard containing crafted data in its fields (e.g., description, name, skills.description). If this data is used without sanitization to construct prompts for a Large Language Model (LLM), it could expose your application to prompt injection attacks. Failure to properly validate and sanitize this data before use can introduce security vulnerabilities into your application.

Developers are responsible for implementing appropriate security measures, such as input validation and secure handling of credentials to protect their systems and users.

Capabilities

StreamingPush NotificationsMulti-TurnAuth: none
official-samplepython

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