jamarius-fortson/agent-adversarial-tester
GitHub: jamarius-fortson/agent-adversarial-tester
面向Agentic AI的高性能安全编排框架，提供AI驱动的对抗测试与高保真评估。
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# 🛡️ agent-adversarial-tester
**Enterprise-Grade Adversarial Security Framework for Agentic AI.**
A high-performance security orchestration engine designed to red-team AI agents before they reach production. Beyond simple testing, we provide AI-driven attack evolution and high-fidelity judging mapped to global security benchmarks.
[](LICENSE)
[](https://www.python.org/downloads/)
[](https://owasp.org/)
[](#-architecture-excellence)
[Quick Start](#-quick-start) · [Core Architecture](#-core-architecture) · [Advanced AI Features](#-advanced-ai-features) · [Reporting](#-premium-reporting) · [OWASP Mapping](#-owasp-agentic-top-10-mapping)
## 🏛️ Core Architecture
This framework follows the **Single Responsibility Principle (SRP)** and **Dependency Inversion**, ensuring every modular component is independently testable and extensible.
```
graph LR
H[RedTeam Harness] --> A[Adaptive Adversary]
H --> T[Agent Target]
T --> R[Response Capture]
R --> D[Heuristic Detectors]
D --> J[LLM Security Judge]
J --> F[High-Fidelity Finding]
F --> Rep[Premium Reporting]
```
## 🔥 Why Security Matters for Agents
In the era of agentic AI, prompt injection is a **Remote Code Execution (RCE)** level risk.
Agents use tools: databases, internal APIs, and private customer context. A single malicious instruction can hijack these tools, leading to massive data exfiltration or system-wide misuse. **agent-adversarial-tester** provides the defensive barrier needed for enterprise-grade AI deployment.
## ⚡ Quick Start
### 1. Install
```
pip install agent-adversarial-tester
```
### 2. Implement the Target Adapter
Create a thin adapter for your agent so it can be audited:
```
from agent_adversarial_tester import AgentTarget
class FinancialAgentTarget(AgentTarget):
def setup(self):
# Initialize your agent (LangGraph, CrewAI, etc.)
self.agent = load_production_agent()
async def invoke(self, message: str) -> str:
# Return response text
return await self.agent.run(message)
def get_tool_calls(self):
# Record and return tool call metadata
return self.agent.last_trace.tool_calls
```
### 3. Run the Audit
```
# 🚀 High-Fidelity AI Audit (adaptive attacks + security judge)
export OPENAI_API_KEY="sk-..."
agent-redteam run --target my_module:FinancialAgentTarget --llm-judge --adaptive --format html
```
## 💎 Premium Reporting & Observability
- **Unified Security Report**: Stunning glassmorphism-styled HTML reports for stakeholders.
- **Traceability Logs**: Each attack turn is logged with full context in JSON for reproduction and debugging.
- **Cost Analysis**: Pre-scan token and USD cost estimation via `--dry-run`.
## 🎯 Attack Surface (OWASP ASI Mapped)
| OWASP ID | Category | Threat Profile |
|----------|----------|----------------|
| **ASI01** | Goal Hijacking | AI is tricked into adopting a malicious persona (e.g., DAN). |
| **ASI02** | Tool Misuse | Adversarial steering triggers destructive tool calls or SQLi. |
| **ASI03** | Prompt Injection | Overwriting system instructions with higher-priority user commands. |
| **ASI08** | Data Leakage | Exfiltration of PII, internal context, or credentials. |
## 🤝 Contributing & Standards
Built to the highest engineering standards by global AI security experts. We welcome contributions to our [Attack Pack Registry](src/agent_adversarial_tester/attacks/).标签：agentic安全测试, AI代理安全, AI安全, C2, Chat Copilot, FAANG标准工程, FTP漏洞扫描, LLM安全裁判, OWASP AI安全索引, Python, 企业级安全框架, 依赖倒置, 全球安全基准, 单责任原则, 启发式检测器, 安全编排, 对抗测试, 提示注入防御, 攻击演化, 无后门, 源代码安全, 生产前安全验证, 远程代码执行防御, 逆向工具, 高保真评估