如何在48小时内对你的AI代理进行红队测试——一种实用方法论
我们发布了用于人工智能红队评估的方法论。48小时，4个阶段，6个攻击优先领域。
这不是理论——这是我们针对具有工具访问权限的生产AI代理所采用的框架。核心见解是：AI红队测试所需的方法论与传统渗透测试不同。攻击面不同（自然语言输入、工具集成、外部数据流），而利用模式也不同（攻击链包括提示注入、工具滥用、数据外泄或权限提升）。
48小时框架：
1. 侦察（2小时）——绘制接口、工具、数据流和现有防御的地图。具有文件系统和数据库访问权限的代理与聊天机器人是根本不同的目标。
2. 自动扫描（4小时）——在6个优先领域进行系统性测试：直接提示注入、系统提示提取、越狱、工具滥用、间接注入（RAG/网络）和视觉/多模态攻击。建立基线。
3. 手动利用（8小时）——确认发现，构建攻击链，测试防御边界。单个漏洞的组合：提示注入 -> 工具滥用 -> 数据外泄是一个常见的链条。
4. 验证与报告（2小时）——可重复性、业务影响、严重性、抵抗评分。
运行这些测试的一些观察：
- 我们的分类法中存在62种提示注入技术。大多数团队只测试少数几种。基本的那些（“忽略之前的指示”）也是最先被阻止的。
- 工具滥用是造成真正损害的地方。参数注入、范围逃逸和工具链结合将成功的提示注入转化为未经授权的数据库查询、文件访问或API调用。
- 间接注入被低估。如果你的AI读取外部内容（RAG、网络搜索），那么这些内容就是攻击面。在数百万个文档中，5个被污染的文档可以实现高攻击成功率。
- 架构决定优先级。仅聊天的应用需要首先进行提示注入测试。RAG应用需要首先进行间接注入测试。具有工具的代理需要首先进行工具滥用测试。
该方法论参考了我们开源的122个攻击向量的分类法：https://github.com/tachyonicai/tachyonic-heuristics
完整文章：https://tachyonicai.com/blog/how-to-red-team-ai-agent/
OWASP LLM前10名伴随指南：https://tachyonicai.com/blog/owasp-llm-top-10-guide/
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We published the methodology we use for AI red team assessments. 48 hours, 4 phases, 6 attack priority areas.<p>This isn't theoretical — it's the framework we run against production AI agents with tool access. The core insight: AI red teaming requires different methodology than traditional penetration testing. The attack surface is different (natural language inputs, tool integrations, external data flows), and the exploitation patterns are different (attack chains that compose prompt injection into tool abuse, data exfiltration, or privilege escalation).<p>The 48-hour framework:<p>1. Reconnaissance (2h) — Map interfaces, tools, data flows, existing defenses. An agent with file system and database access is a fundamentally different target than a chatbot.<p>2. Automated Scanning (4h) — Systematic tests across 6 priorities: direct prompt injection, system prompt extraction, jailbreaks, tool abuse, indirect injection (RAG/web), and vision/multimodal attacks. Establishes a baseline.<p>3. Manual Exploitation (8h) — Confirm findings, build attack chains, test defense boundaries. Individual vulnerabilities compose: prompt injection -> tool abuse -> data exfiltration is a common chain.<p>4. Validation & Reporting (2h) — Reproducibility, business impact, severity, resistance score.<p>Some observations from running these:<p>- 62 prompt injection techniques exist in our taxonomy. Most teams test for a handful. The basic ones ("ignore previous instructions") are also the first to be blocked.<p>- Tool abuse is where the real damage happens. Parameter injection, scope escape, and tool chaining turn a successful prompt injection into unauthorized database queries, file access, or API calls.<p>- Indirect injection is underappreciated. If your AI reads external content (RAG, web search), that content is an attack surface. 5 poisoned documents among millions can achieve high attack success rates.<p>- Architecture determines priority. Chat-only apps need prompt injection testing first. RAG apps need indirect injection first. Agents with tools need tool abuse testing first.<p>The methodology references our open-source taxonomy of 122 attack vectors: https://github.com/tachyonicai/tachyonic-heuristics<p>Full post: https://tachyonicai.com/blog/how-to-red-team-ai-agent/<p>OWASP LLM Top 10 companion guide: https://tachyonicai.com/blog/owasp-llm-top-10-guide/