13 posts tagged with "AI"

内容速览​

• 适用场景： 已获授权的内部渗透测试、验证码逻辑验证和安全工具研发。
• 实现方法： 使用 ai.FunctionCall 与 ai.imageBase64 提取验证码，再分别接入基础脚本、并发任务和 Web Fuzzer 热加载流程。
• 关键结论： Yaklang 可以在不单独维护 OCR 服务的情况下完成图像识别与请求编排，但测试范围和并发强度必须遵循授权边界。

常见问题​

是否必须额外部署 OCR 服务？​

不需要。文中的方案通过已配置的 Yakit AI 网关调用多模态模型，并让模型按指定字段返回验证码文本。

这个流程可以用于任意网站吗？​

不可以。验证码识别和登录测试只能用于自有系统、靶场或已经获得明确授权的目标。

打开一个后台管理系统，一般来说可能仅仅是一个登录框。用户名、密码、验证码——三个输入框，一个提交按钮。密码可以爆破，用户名可以枚举，但验证码这个东西，一直是渗透测试中一个绕不开的拦路虎。

Content Overview​

• Target scenario: Plugin authors and platform maintainers who want their security plugins to be searched, selected, orchestrated, and executed by an AI Agent.
• How it works: Plugins, Tools, Blueprints, Skills, and Focus Mode are abstracted into a unified Capability set, then executed through layered intent routing, progressive disclosure, capability-catalog Grounding, and a unified dispatcher.
• Key conclusion: A plugin store is not only a script download entry point; it can also become the Agent's capability catalog. Discoverability, constraints, and result verification matter just as much as model capability.

FAQ​

Does integrating a plugin with AI require rewriting an adaptation layer?​

No. Following the flow described in this article, a plugin author only needs to enable "Expose to AI" and add an accurate plugin description to enter the unified capability catalog.

How does the system reduce the chance of the AI picking the wrong plugin?​

Capabilities first pass through intent routing and catalog Grounding, then the candidate set is progressively narrowed. The execution phase is still managed and verified by the unified dispatch pipeline.

Background​

In the past, if you wanted to expand the security capabilities of an AI, there were usually two approaches:

Quick Overview​

• Applicable scenarios: Security teams that need to perform phased code audits on large projects, organize vulnerability evidence, and get assisted SyntaxFlow rule generation.
• Implementation approach: First explore the project and build a scan plan, then audit by category, verify each piece of evidence one by one, generate a report, and produce PoC examples or rule suggestions driven by risk.
• Key conclusion: The value of AI lies in organizing exploration, verification, and reporting into a traceable workflow; high-risk conclusions and PoCs still need to be reviewed in isolated, authorized environments.

FAQ​

Does AI audit jump straight to vulnerability conclusions?​

No. The workflow described breaks project exploration, scan planning, categorized audit, evidence recording, and report generation into independent phases, requiring conclusions to trace back to specific evidence.

Can auto-generated PoCs be used directly against production targets?​

No. PoCs are intended for risk reproduction and verification within an authorized scope; their inputs, impact, and execution boundaries should be reviewed in an isolated environment.

As Memfit AI's new features expand and the related mechanisms mature, we can now extend existing AI capabilities to our other products. This article focuses on the new attempts we have made in the AI-powered extension of the IRify tool.

In the previous article Memfit AI Professional Memory: Before the Agent Acts, It Reads Through Your Knowledge Base, we solved the Agent's "knowledge" problem. Through the built-in knowledge-base system, the Agent — before executing an attack — can, like a senior expert, first review the enterprise's private testing and compliance documents, ensuring its actions never depart from the compliance baseline.

When an AI Agent runs into something it does not know​

In the previous article: Memfit AI: A Production-Grade AI Agent That Does Not Get Lost Over N Hours of Continuous Penetration Testing, we talked about how Memfit AI works autonomously like a real human penetration testing engineer — planning tasks, executing attacks, and dynamically adjusting strategy. It ran for more than two hours and produced 18 vulnerability findings and a full penetration testing report.

Does it sound like magic?

AI Agent 这两年正以不可思议的速度发展着。这种由大语言模型（LLM）驱动的自然交互模式，正在重塑静态应用安全测试（SAST）领域。基于 AI Agent 的自动化代码审计方案，已逐渐成为当前安全工程实践的重点探索方向。

在现有的 AI Agent 代码审计框架中，系统获取代码上下文的主要检索手段通常依赖 grep （纯文本正则检索）、tree-sitter （抽象语法树/AST 解析）以及 LSP （语言服务器协议）等工具。这些技术在处理基础的符号定位和局部代码提取时具有绝对效率优势：grep 负责极速的关键词定位，tree-sitter 负责精准的语法结构识别，而 LSP 则提供了“转到定义”或“查找引用”等基础语义导航能力。

然而，面对深度的安全审计需求时，这套组合拳显现出了结构性的局限。grep 仅能进行字面量匹配，无法解析逻辑依赖；基于 AST 的解析虽能识别静态特征，却难以跨越函数与物理文件边界；LSP 虽具备一定的语义关联能力，但本质上仍是“点对点”的静态跳转，它无法刻画变量在复杂控制流和赋值逻辑中的“生命周期”，也难以追踪数据在经过集合封装、对象透传或跨过程调用后的真实流转。

这是 AI Agent 相关能力第一次正式应用到 Yakit 的日常功能模块中。本次以 HTTP 历史流量分析 作为试点，在不改变用户原有使用习惯的前提下，引入一个“懂流量、能对话”的分析专家，用来辅助用户理解和分析已有流量数据。

本文主要介绍这个功能做了什么、怎么用，以及背后的一些实现思路。

在 AI 工具搜索、意图识别、记忆管理、知识扩展等场景中，核心需求往往是对大量模糊文本的语义进行高效检索。

传统的关键词匹配难以满足需求，因此需要通过向量化表示（embedding）与 向量数据库 来实现语义级别的近似搜索（Approximate Nearest Neighbor, ANN）。

背景​

大型模型的 AI 知识能力现今基本可以被认为“溢出”了，他的编码能力，知识都很强，但是上下文限制或者无法和用户偏好对其导致效果无法如人意，因此留给我们这种普通研发者的重要工作就是教会 AI 做事儿。