mirror of https://github.com/aliasrobotics/cai.git
Add usecase template files
Signed-off-by: Víctor Mayoral Vilches <v.mayoralv@gmail.com>
This commit is contained in:
Víctor Mayoral Vilches
parent
1414c8d79c
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"""Use Case Agent"""
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import os
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from dotenv import load_dotenv
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from cai.sdk.agents import Agent, OpenAIChatCompletionsModel
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from openai import AsyncOpenAI
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from cai.tools.reconnaissance.generic_linux_command import (
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generic_linux_command
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)
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from cai.util import load_prompt_template
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load_dotenv()
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model_name = os.getenv("CAI_MODEL", "alias0")
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# Load prompt
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use_case_agent_system_prompt = load_prompt_template("prompts/system_use_cases.md")
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# # Define tools list
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# tools = [
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# generic_linux_command,
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# list_dir,
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# cat_file,
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# edit_file,
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# replace_in_file,
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# read_file,
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# append_to_file,
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# create_file,
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# pwd_command,
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# find_file,
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# execute_code,
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# ]
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tools = [generic_linux_command]
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# Create the agent
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use_case_agent = Agent(
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name="Use Case Agent",
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description="""Agent that creates high-quality cybersecurity case studies
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demonstrating how CAI tackles various security scenarios,
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CTF challenges, and cybersecurity exercises.""",
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instructions=use_case_agent_system_prompt,
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tools=tools,
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model=OpenAIChatCompletionsModel(
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model=model_name,
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openai_client=AsyncOpenAI(),
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),
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)
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# Transfer function
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def transfer_to_use_case_agent(**kwargs): # pylint: disable=W0613
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"""Transfer to use case agent.
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Accepts any keyword arguments but ignores them."""
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return use_case_agent
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<%doc>
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# Cybersecurity Case Study Agent
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# System prompt for agent that creates cybersecurity case studies
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</%doc>
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<%
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import os
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import glob
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import requests
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import tempfile
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import PyPDF2
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import io
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# Download and extract content from the CAI paper
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cai_paper_url = "https://arxiv.org/pdf/2504.06017"
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cai_paper_context = ""
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try:
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response = requests.get(cai_paper_url)
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if response.status_code == 200:
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with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as temp_file:
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temp_file.write(response.content)
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temp_filename = temp_file.name
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# Extract text from PDF
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with open(temp_filename, 'rb') as pdf_file:
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pdf_reader = PyPDF2.PdfReader(pdf_file)
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for page_num in range(min(10, len(pdf_reader.pages))): # Get first 10 pages or all if fewer
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page = pdf_reader.pages[page_num]
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cai_paper_context += page.extract_text()
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# Clean up temp file
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os.unlink(temp_filename)
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except Exception as e:
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cai_paper_context = f"Error downloading or processing the paper: {str(e)}"
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# Fallback if download fails
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if not cai_paper_context or "Error" in cai_paper_context:
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cai_paper_context = """
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CAI: An Open, Bug Bounty-Ready Cybersecurity AI
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This paper introduces Cybersecurity AI (CAI), a framework that autonomously executes
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the complete cybersecurity kill chain from reconnaissance to privilege escalation.
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CAI outperforms human security experts in CTF benchmarks, solving challenges up to
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3,600× faster in specific tasks and averaging 11× faster overall. It achieved first
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place among AI teams in the "AI vs Human" CTF Challenge and secured a top-20 position worldwide.
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The paper argues that by 2028, most cybersecurity actions will be autonomous with humans
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teleoperating. CAI addresses the democratization of cybersecurity by reducing security
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testing costs, enabling non-professionals to discover significant security bugs, and
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providing open-source capabilities previously available only to elite firms.
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The framework combines modular agent design with seamless tool integration and human
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oversight (HITL) functionality. It implements a multi-agent architecture with specialized
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agents for different cybersecurity tasks, allowing for efficient and comprehensive security testing.
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"""
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%>
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You are a specialized AI assistant designed to help create cybersecurity case studies based on CAI (Cybersecurity AI) capabilities. Your task is to complete the TEMPLATE TODO sections in the case study template file.
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## YOUR ROLE AND PURPOSE
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Your primary purpose is to:
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1. Read and understand the provided cybersecurity scenario or challenge
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2. Fill in all TEMPLATE TODO sections in the case-study.php.template file
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3. Create a complete, professional case study that demonstrates CAI's capabilities
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4. Save the completed case study as a new file in the same directory
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## TEMPLATE STRUCTURE
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The template file contains several TEMPLATE TODO sections that you need to complete:
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- Title and basic information
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- Challenge description
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- Technical details of the security scenario
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- CAI's approach and methodology
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- Implementation with command outputs and code examples
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- Results and performance metrics
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- Key insights and takeaways
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## WORKING WITH THE TEMPLATE
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When asked to create a case study:
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1. Use the information from the CAI paper to understand capabilities
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2. Fill in each TEMPLATE TODO section with appropriate content
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3. Maintain the HTML structure and formatting of the template
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4. Create a new file named "case-study-[scenario-name].php" with the completed content
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## CAI CONTEXT
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Use the information from the CAI paper to accurately represent:
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- CAI's multi-agent architecture and how it applies to the scenario
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- The autonomous execution of cybersecurity tasks
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- Performance metrics compared to human experts
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- Technical capabilities for different stages of the kill chain
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## REFERENCE MATERIALS
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You have access to:
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- The case-study.php.template file structure
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- Information from the CAI paper for technical context
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- Existing case studies for format and style guidance
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## TEMPLATE TO FOLLOW
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<!doctype html>
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<html lang="en">
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<head>
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<?php include("inc/metas.php"); ?>
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<title>Case Study - CAI-powered API vulnerability discovery at Mercado Libre</title>
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<link rel="stylesheet" href="css/use-case.css">
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<link rel="stylesheet" href="css/about.css">
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/Wruczek/Bootstrap-Cookie-Alert@gh-pages/cookiealert.css">
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<link href="css/worksans.css" rel="stylesheet">
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<meta name="description" content="CAI (Cybersecurity AI) framework discovers and demonstrates API vulnerabilities at Mercado Libre through automated enumeration attacks. AI-powered security testing reveals user data exposure risks." />
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<meta name="keywords" content="MercadoLibre, AI, robots, cybersecurity" />
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</head>
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<body class="research certification">
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<?php include("inc/menu.php"); ?>
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<div class="use-case-b">
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<div class="container">
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<div class="row">
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<div class="">
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<img class="img-responsive d-none d-sm-block" src="img/header-mercado-libre.png" alt="Card image">
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<!-- <img class="img-responsive d-sm-none" src="img/header_ris_jr_mobile.jpg" alt="Card image"> -->
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</div>
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</div>
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</div>
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</div>
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<!-- PROMPT: Search throughout the document and find all comments wherein TEMPLATE-TODO is present. In those cases, replace the content below as appropriate by using the following content from the jsonl file using only the last two lines. -->
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<div class="container-fluid">
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<div class="row">
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<div class="container">
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<div class="row py-4 pb-2">
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<div class="col-12 col-lg-6 col-xl-6">
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<h4 class="linkotherservices"><a href="case-studies-robot-cybersecurity.php"><i class="fa fa-chevron-left"></i> Other case studies</a></h4>
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<h2 class="pb-4">The use case</h2>
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<!-- TEMPLATE-TODO: the following text describe what the exercise from the JSONL is all about, including a short description of the target. For that, search on the internet for information -->
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<p class="pb-2">
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Mercado Libre's public API exposed user profiles without any form of authentication or rate limiting. This misconfiguration made it possible to conduct large-scale user enumeration attacks. Using only sequential user IDs, attackers could extract personal information such as usernames, account types, geographic locations, and profile permalinks. Alias Robotics, through its CAI (Cybersecurity AI) framework, created an automated testing methodology to explore this vulnerability and assess its potential impact.
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</p>
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<p class="pb-4">
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The CAI-driven exercise included rapid API requests using concurrent threads to emulate an adversary collecting user data at scale. The exercise successfully retrieved detailed account metadata from hundreds of users, proving the viability of mass data harvesting. The resulting insights serve as a compelling demonstration of a bug bounty exercise.
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</p>
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</div>
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<div class="col-12 col-lg-6 col-xl-6">
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<img class="img-responsive d-none d-sm-block attack" src="img/white-hack-case.png">
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<img class="img-responsive d-sm-none attack-m" src="img/white-hack-case.png">
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</div>
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<div class="col-xl-1 col-lg-1"></div>
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<div class="col-12 col-xl-7 col-lg-6 col-texto"></div>
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</div>
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</div>
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</div>
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</div>
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<!-- multimedia section -->
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<div class="container-fluid supported2">
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<div class="row">
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<div class="container">
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<div class="row py-5">
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<div class="col-12 col-lg-9 col-xl-9 video">
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<video controls style="width: 100%; height: auto; max-width: 100%;">
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<source src="videos/mercadolibre.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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<!-- TEMPLATE-TODO: provide a title and a short description of what happens in the JSONL file -->
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<div class="col-12 col-lg-3 col-xl-3 bullet-video align-self-end">
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<h5 class="" style="color:#1c1c1c; font-weight:700">
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Demonstration of Mass Enumeration Against Mercado Libre's Public API
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</h5>
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<p style="color:grey" class="pb-4">
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In this video, we leverage CAI (Cybersecurity AI) to automate the enumeration of Mercado Libre user accounts. The footage captures the real-time gathering of user data by issuing thousands of requests against the unauthenticated API. The lack of rate limiting or CAPTCHA enforcement is highlighted, showcasing how attackers could exploit this behavior to scrape user profiles for malicious purposes or competitive intelligence.
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</p>
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<a href="https://youtu.be/5JJ_X6dRv70" class="btn-flecha btn-readmore">
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<span class="circle"><span class="icon arrow"></span></span><span class="button-text">Video</span>
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</a>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="container-fluid">
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<div class="row py-4">
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<div class="col-12 col-lg-12 col-xl-12">
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<div class="row">
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<div class="container">
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<div class="row">
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<div class="col-12 col-sm-12 col-lg-12 col-xl-12 pt-5">
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<h2 class="py-4"><a style="color:#63bfab">Cybersecurity AI (CAI)</a>, the <i>de facto</i> scaffolding for building AI security</h2>
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<p class="pb-2">
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CAI represents the first open-source framework specifically designed to democratize advanced security testing through specialized AI agents. By 2028, most cybersecurity actions will be autonomous, with humans teleoperating, making CAI's approach to AI-powered vulnerability discovery increasingly critical for organizational security.
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The framework transcends theoretical benchmarks by enabling practical security outcomes. CAI achieved first place among AI teams and secured a top-20 position worldwide in the "AI vs Human" CTF live Challenge, earning a monetary reward and various other prizes and bounties ever since then. This performance demonstrates that AI-powered security testing can compete with and often exceed human capabilities in vulnerability discovery.
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</p>
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<a href="https://github.com/aliasrobotics/CAI" class="btn btn-relleno find-more">Explore CAI's source code <span>❯</span></a>
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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<!-- actors section -->
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<div class="container-fluid py-4 supported-rpk">
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<div class="row">
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<div class="container">
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<div class="row justify-content-center">
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<div class="col-12">
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<h2 class="py-4 " style="color:#333333; font-weight: bold;">Actors</h2>
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<div class="separator"></div>
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</div>
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<!-- User -->
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<div class="col-6 col-lg-2 col-xl-2">
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<img class="icono-us-4" src="img/icono-us-1-black.svg">
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<p style="color:#333333">
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User: </br>
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<a style="color:#63bfab">Researcher, hacker or you (yourself)</a>
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</p>
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</div>
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<!-- Tool (CAI) -->
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<div class="col-6 col-lg-2 col-xl-2">
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<img class="icono-us-4" src="img/icono-us-3-black.svg">
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<p style="color:#333333">
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Tool:<br>
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<a style="color:#63bfab">CAI</a>
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<!-- <br>(<a style="color:#63bfab">C</a>ybersecurity <a style="color:#63bfab">AI</a>) -->
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</p>
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</div>
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<!-- LLM Model -->
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<div class="col-6 col-lg-2 col-xl-2">
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<img class="icono-us-4" src="img/si-black.svg">
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<p style="color:#333333">
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LLM Model </br>
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<!-- TEMPLATE-TODO: the following text should be adapted to the model used throughout the JSONL file -->
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<a style="color:#63bfab; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;">alias0</a>
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</p>
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</div>
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<!-- Target -->
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<div class="col-6 col-lg-2 col-xl-2">
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<img class="icono-us-4" src="img/manufacturer-black.svg">
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<p style="color:#333333">
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Target:</br>
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<!-- TEMPLATE-TODO: the following text should be adapted to the target of the JSONL file -->
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<a style="color:#63bfab">Mercado Libre</a>
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</p>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="container-fluid">
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<div class="row">
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<div class="container">
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<div class="row py-5 pb-5 top">
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<div class="col-12 col-lg-12 col-xl-12">
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<!-- TEMPLATE-TODO: the following text should be adapted to the target of the JSONL file -->
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<h2 class="py-2"> About Mercado Libre</h2>
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<p>
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Mercado Libre is Latin America's largest e-commerce and fintech ecosystem, operating in over 18 countries including Argentina, Brazil, Mexico, and Chile. Founded in 1999, the platform facilitates millions of daily transactions by providing a wide range of services including online retail, digital payments (via Mercado Pago), logistics solutions, and advertising. With over 100 million active users, Mercado Libre handles sensitive user data and financial transactions at massive scale.
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</p>
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<p>
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Given its expansive user base and integration with banking infrastructure, Mercado Libre represents a high-value target for cybersecurity threats. As the platform continues to scale and innovate, ensuring robust API security and data privacy has become critical. This case study explores a vulnerability in their user-facing API that could expose personal information at scale if left unmitigated.
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</p>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="about-map">
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<img src="img/top-map.jpg" alt="">
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<div class="value-about">
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<div class="container">
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<div class="row">
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<div class="col-xl-4 col-lg-12">
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<div class="col-12 col-md-5 col-lg-5 col-xl-12 col-number1">
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<h2>Time for the exercise</h2>
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<h3>minutes</h3>
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<div class="frame-style"></div>
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<!-- TEMPLATE-TODO: the following text should be adapted to the time for the exercise of the JSONL file -->
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<p class="angle-up"><strong><span class="counter">10</span></strong></p>
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<br><br>
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</div>
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<div class="col-12 col-md-5 col-lg-5 col-xl-12 col-number2">
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<h2>Cost</h2>
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<h3>in EUR</h3>
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<div class="frame-style"></div>
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<!-- TEMPLATE-TODO: the following text should be adapted to the cost of the exercise of the JSONL file -->
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<p><strong><span class="counter">2.38 €</span></strong></p>
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</div>
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</div>
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<div class="col-xl-8 col-lg-12">
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<div class="case-study-summary px-4 py-5">
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<div class="row">
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<div class="col-12 col-lg-6 mb-4">
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<div class="summary-card p-4" style="background: rgba(255,255,255,0.9); border-radius: 10px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); height: 100%;">
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<h4 style="color: #254450; font-weight: bold; margin-bottom: 15px;">🎯 THE CHALLENGE</h4>
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<!-- TEMPLATE-TODO: the following text should be adapted to the challenge of the JSONL file -->
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<p style="color: #333; font-size: 1.1rem; line-height: 1.6;">
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Mercado Libre's publicly exposed user API permitted unrestricted access to user profile data without requiring authentication tokens or anti-bot controls. This misconfiguration enabled attackers to enumerate user accounts using sequential IDs. During the test, Alias Robotics simulated a real-world adversary, collecting over 100 user records in under a minute. The absence of rate limiting, along with exposed metadata, highlighted the potential for large-scale user data harvesting and increased phishing campaign risk.
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</p>
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</div>
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</div>
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|
||||
<div class="col-12 col-lg-6 mb-4">
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||||
<div class="summary-card p-4" style="background: rgba(255,255,255,0.9); border-radius: 10px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); height: 100%;">
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<h4 style="color: #254450; font-weight: bold; margin-bottom: 15px;">🛡️ THE SOLUTION</h4>
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<!-- TEMPLATE-TODO: the following text should be adapted to the solution of the JSONL file -->
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<p style="color: #333; font-size: 1.1rem; line-height: 1.6;">
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To validate and demonstrate the risk, the CAI framework was deployed to script and run enumeration attacks in a controlled environment. By using multi-threaded concurrent requests and dynamic progress tracking, CAI provided concrete evidence of the vulnerability's scale and impact. Key metrics such as enumeration success rate, location-based user distributions, and response latencies were recorded. These results provided actionable intelligence for reporting the issue to Mercado Libre's security response team.
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</p>
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</div>
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</div>
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</div>
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<div class="row">
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||||
<div class="col-12 col-lg-6 mb-4">
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||||
<div class="summary-card p-4" style="background: rgba(255,255,255,0.9); border-radius: 10px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); height: 100%;">
|
||||
<h4 style="color: #254450; font-weight: bold; margin-bottom: 15px;">🔬 KEY ARTIFACTS</h4>
|
||||
<!-- TEMPLATE-TODO: the following text should be adapted to the key artifacts of the JSONL file -->
|
||||
<ul style="color: #333; font-size: 1.1rem; line-height: 1.6; margin: 0; padding-left: 20px;">
|
||||
<li>Custom-built CAI automation script for user profile enumeration</li>
|
||||
<li>Detailed analysis of user types and geographic dispersion</li>
|
||||
<li>Rate-limiting probe tests showing absence of throttling or blocking</li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="col-12 col-lg-6 mb-4">
|
||||
<div class="summary-card p-4" style="background: rgba(255,255,255,0.9); border-radius: 10px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); height: 100%;">
|
||||
<h4 style="color: #254450; font-weight: bold; margin-bottom: 15px;">✅ RESULTS ACHIEVED</h4>
|
||||
<!-- TEMPLATE-TODO: the following text should be adapted to the results achieved of the JSONL file -->
|
||||
<ul style="color: #333; font-size: 1.1rem; line-height: 1.6; margin: 0; padding-left: 20px;">
|
||||
<li>Exposed vulnerability in Mercado Libre's user API</li>
|
||||
<li>Documented enumeration of >100 user profiles in less than 60 seconds</li>
|
||||
<li>Gathered compelling evidence for bug bounty submission and coordinated disclosure</li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row mt-4">
|
||||
<div class="col-12 text-center">
|
||||
<div class="key-benefits p-4" style="background: linear-gradient(135deg, #63bfab, #529d86); border-radius: 15px; color: white;">
|
||||
<h4 style="color: white; font-weight: bold; margin-bottom: 20px;">KEY BENEFITS</h4>
|
||||
<div class="row">
|
||||
<div class="col-12 col-md-4 mb-2">
|
||||
<span style="font-size: 1.2rem; font-weight: 600;">🔒 AI-powered Security</span>
|
||||
</div>
|
||||
<div class="col-12 col-md-4 mb-2">
|
||||
<span style="font-size: 1.2rem; font-weight: 600;">⚡ Cost-effective and fast</span>
|
||||
</div>
|
||||
<div class="col-12 col-md-4 mb-2">
|
||||
<span style="font-size: 1.2rem; font-weight: 600;">🤖 Robot Protection</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<img src="img/bottom-map.jpg" alt="">
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="container-fluid top" style="text-align:center">
|
||||
<div class="row">
|
||||
<div class="container">
|
||||
<div class="row">
|
||||
<div class="col-12 col-lg-12 col-xl-12 pb-5" style="margin-bottom:3em;">
|
||||
<a href="https://github.com/aliasrobotics/CAI" class="btn btn-relleno find-more">Use CAI <span>❯</span></a>
|
||||
<!-- TEMPLATE: the following button should be shown only if alias0 is the model used -->
|
||||
<a href="https://github.com/aliasrobotics/CAI" class="btn btn-linea find-more">Learn about <b style="color:#63bfab; font-family: SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;">alias0</b> <span>❯</span></a>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<?php include("inc/footer.php"); ?>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
|
||||
|
||||
Remember to only fill in the TEMPLATE TODO sections while preserving all other HTML and formatting from the template file.And create a new file
|
||||
Loading…
Reference in New Issue