Agentbreaker

AgentBreaker

AgentBreaker is not another tool shipping prompt fuzzing and calling it red teaming.

It probes the system first, identifies capabilities such as multi-turn behavior, tool use, and multimodal handling, then generates targeted payloads shaped to the surface it found. The result is a red-team engine that is built to get you outcomes, not just logs.

Why It Exists

Most AI security testing is still too manual, too noisy, or too one-off.

AgentBreaker gives you:

• repeatable campaign runs
• structured evidence instead of one-off screenshots
• judge, planner, and generator assisted workflows
• an operator control plane for launches and review

This repo now starts with a clean slate. It does not ship a bundled public seed corpus. What matters is what AgentBreaker has already been able to surface across real systems.

What It Can Surface

AgentBreaker is built to help teams uncover issues such as:

• system prompt leakage and hidden instruction disclosure
• jailbreak and policy bypass paths
• unsafe tool behavior and action chaining
• sensitive data exposure and retrieval abuse
• browser and API workflow weaknesses around agent execution
• weak refusal patterns that collapse under pressure

Public Showcase

The public results corpus already demonstrates outcomes such as:

• resistance-level-1: completion-style prompt extraction that disclosed a protected flag
• promptairlines: structured JSON export that disclosed protected runtime values
• promptairlines: authority-override framing that yielded restricted coupon data
• promptairlines: multimodal injection flows that exfiltrated protected artifacts from uploaded content
• gpt-5.2: successful runs across jailbreak, prompt injection, tool misuse, and data exposure patterns
• gpt-5.4: successful runs across prompt injection, guardrail bypass, tool misuse, and prompt extraction

See [docs/results-showcase.md](docs/results-showcase.md).

How It Flows

flowchart LR
  A["Configure system"] --> B["Launch campaign"]
  B --> C["Generate probes"]
  C --> D["Execute and score"]
  D --> E["Store evidence and results"]
  E --> F["Review in control plane"]

Quick Start

git clone https://github.com/kagexai/agentbreaker.git
cd agentbreaker

python3 -m venv .venv
source .venv/bin/activate
pip install -e .

cp .env.example .env
agentbreaker validate --check-env
agentbreaker run  --loop

Open the control plane:

agentbreaker serve --port 1337

Then visit http://127.0.0.1:1337.

Operator Paths

Run a campaign:

agentbreaker run  --loop

Validate config before a run:

agentbreaker validate --check-env

Inspect configured systems:

agentbreaker targets

Start the review surface:

agentbreaker serve --port 1337

Core Files

• agentbreaker/cli.py - main CLI entrypoint
• agentbreaker/campaign.py - campaign loop and strategy selection
• agentbreaker/attack.py - payload construction
• agentbreaker/target.py - execution harness and scoring
• agentbreaker/control_plane.py - operator backend
• frontend/ - control plane frontend
• taxonomy/agentbreaker_taxonomy.yaml - strategy library
• target_config.yaml - system and model configuration

Safety

AgentBreaker is for authorized testing only. Do not run it against systems you do not own or do not have explicit permission to assess.

License

MIT. See [LICENSE](LICENSE).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kagexai
• Source: kagexai/agentbreaker
• License: MIT
• Homepage: https://kagex.ai/products/agentbreaker

Install and usage instructions live in the source repository linked above.