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import streamlit as st
import pandas as pd
import subprocess
import time
import streamlit.components.v1 as components
# ---------------------------- Header and Introduction ----------------------------
st.set_page_config(page_title="LLMs for Cyber Security", page_icon="🔒", layout="wide", initial_sidebar_state="expanded")
st.title("🔒📊 LLMs for Cyber Security: State-of-the-Art Surveys📊🔒")
st.markdown("This app is based on the paper: [Large Language Models for Cyber Security](https://arxiv.org/pdf/2405.04760v3). It showcases LLMs in the cybersecurity landscape, summarizing key surveys and insights.")
st.markdown('🔒📊 https://arxiv.org/abs/2405.04760v3')
# ---------------------------- Data Preparation ----------------------------
data = {
"Reference": ["Motlagh et al.", "Divakaran et al.", "Yao et al.", "Yigit et al.", "Coelho et al.", "Novelli et al.", "LLM4Security"],
"Year": [2024, 2024, 2023, 2024, 2024, 2024, 2024],
"Scope": ["Security application", "Security application", "Security application, Security of LLM", "Security application, Security of LLM", "Security application", "Security application", "Security application"],
"Dimensions": ["Task", "Task", "Model, Task", "Task", "Task, Domain specific technique", "Task, Model, Domain specific technique", "Model, Task, Domain specific technique, Data"],
"Time frame": ["2022-2023", "2020-2024", "2019-2024", "2020-2024", "2021-2023", "2020-2024", "2020-2024"],
"Papers": ["Not specified", "Not specified", 281, "Not specified", 19, "Not specified", 127]
}
df = pd.DataFrame(data)
# ---------------------------- Display Data Table ----------------------------
st.subheader("📊 Survey Overview Table")
st.dataframe(df, height=300)
st.markdown("---")
# ---------------------------- Mermaid Diagram Visualization ----------------------------
st.subheader("🛡️ Security Model Visualization with Mermaid")
mermaid_code = '''
graph TD;
A[LLMs in Security] --> B[Security Application]
B --> C[Task]
B --> D[Model]
D --> E[Domain-Specific Techniques]
E --> F[Data]
'''
# HTML component for Mermaid diagram
mermaid_html = f"""
<html>
<body>
<pre class="mermaid">
{mermaid_code}
</pre>
<script src="https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js"></script>
<script>
mermaid.initialize({{ startOnLoad: true }});
</script>
</body>
</html>
"""
components.html(mermaid_html, height=300)
st.markdown("""
Figure: The diagram illustrates how Large Language Models (LLMs) are applied in security, highlighting the flow from general applications to specific tasks, models, domain-specific techniques, and data considerations.
""")
st.markdown("---")
# ---------------------------- Interactive Chart Example ----------------------------
st.subheader("📈 Interactive Chart Example")
# Sample data for the chart
chart_data = [
{"year": 2020, "papers": 50},
{"year": 2021, "papers": 80},
{"year": 2022, "papers": 120},
{"year": 2023, "papers": 200},
{"year": 2024, "papers": 250},
]
# HTML component for Chart.js
chart_html = f"""
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
<canvas id="myChart" width="400" height="200"></canvas>
<script>
var ctx = document.getElementById('myChart').getContext('2d');
var myChart = new Chart(ctx, {{
type: 'line',
data: {{
labels: {[d['year'] for d in chart_data]},
datasets: [{{
label: 'Number of Papers',
data: {[d['papers'] for d in chart_data]},
borderColor: 'rgb(75, 192, 192)',
tension: 0.1
}}]
}},
options: {{
responsive: true,
scales: {{
y: {{
beginAtZero: true
}}
}}
}}
}});
</script>
</body>
</html>
"""
components.html(chart_html, height=300)
st.markdown("This interactive chart shows the growth in the number of papers on LLMs in cybersecurity over the years.")
st.markdown("---")
# ---------------------------- Interactive D3.js Visualization ----------------------------
st.subheader("🌐 Interactive D3.js Visualization")
# Sample data for the D3 visualization
d3_data = [
{"name": "Task", "value": 30},
{"name": "Model", "value": 25},
{"name": "Domain-Specific", "value": 20},
{"name": "Data", "value": 15},
{"name": "Security of LLM", "value": 10},
]
# HTML component for D3.js visualization
d3_html = f"""
<html>
<head>
<script src="https://d3js.org/d3.v7.min.js"></script>
<style>
.bar {{ fill: steelblue; }}
.bar:hover {{ fill: brown; }}
</style>
</head>
<body>
<div id="d3-chart"></div>
<script>
const data = {d3_data};
const margin = {{top: 20, right: 20, bottom: 30, left: 40}};
const width = 400 - margin.left - margin.right;
const height = 200 - margin.top - margin.bottom;
const svg = d3.select("#d3-chart")
.append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", `translate(${{margin.left}},${{margin.top}})`);
const x = d3.scaleBand()
.range([0, width])
.padding(0.1);
const y = d3.scaleLinear()
.range([height, 0]);
x.domain(data.map(d => d.name));
y.domain([0, d3.max(data, d => d.value)]);
svg.selectAll(".bar")
.data(data)
.enter().append("rect")
.attr("class", "bar")
.attr("x", d => x(d.name))
.attr("width", x.bandwidth())
.attr("y", d => y(d.value))
.attr("height", d => height - y(d.value));
svg.append("g")
.attr("transform", `translate(0,${{height}})`)
.call(d3.axisBottom(x));
svg.append("g")
.call(d3.axisLeft(y));
</script>
</body>
</html>
"""
components.html(d3_html, height=300)
st.markdown("This D3.js visualization shows the distribution of different aspects in LLM cybersecurity research.")
st.markdown("---")
# ---------------------------- Scrollable Content for Additional Insights ----------------------------
st.subheader("📝 Additional Insights")
st.markdown("""
<style>
.scrollable-content {
height: 250px;
overflow-y: scroll;
padding: 10px;
border: 1px solid #ccc;
}
</style>
<div class="scrollable-content">
<h4>Survey Highlights:</h4>
<ul>
<li><strong>Motlagh et al. (2024)</strong>: Focused on security applications within 2022-2023 but did not specify the number of papers reviewed.</li>
<li><strong>Divakaran et al. (2024)</strong>: Explored security applications from 2020-2024 without specifying the number of papers.</li>
<li><strong>Yao et al. (2023)</strong>: Reviewed 281 papers covering both security applications and the security of LLMs between 2019-2024.</li>
<li><strong>Yigit et al. (2024)</strong>: Concentrated on security applications and the security of LLMs from 2020-2024 without specifying paper count.</li>
<li><strong>Coelho et al. (2024)</strong>: Introduced domain-specific techniques in security applications, covering 19 papers from 2021-2023.</li>
<li><strong>Novelli et al. (2024)</strong>: Discussed tasks, models, and domain-specific techniques in security applications without specifying paper count.</li>
<li><strong>LLM4Security (2024)</strong>: Comprehensive survey of 127 papers from 2020-2024, covering models, tasks, domain-specific techniques, and data.</li>
</ul>
<h4>Key Observations:</h4>
<ol>
<li>The interest in applying LLMs to cybersecurity has significantly increased since 2019.</li>
<li>There's a growing focus on not just using LLMs for security tasks but also securing the LLMs themselves.</li>
<li>Domain-specific techniques are becoming more prominent, indicating a move towards specialized security solutions.</li>
</ol>
</div>
""", unsafe_allow_html=True)
st.markdown("---")
# ---------------------------- Security Audit Section ----------------------------
st.subheader("🔍 Run Python Dependency Security Audit")
st.markdown("Keeping your project's dependencies secure is crucial. Use the button below to run a security audit on the Python packages used in this environment.")
if st.button('Run pip-audit for Security Check'):
with st.spinner('Running security audit...'):
time.sleep(2)
result = subprocess.run(['pip-audit'], capture_output=True, text=True)
st.code(result.stdout)
st.success('Security audit completed!')
st.markdown("Note: The pip-audit tool checks your Python environment for packages with known vulnerabilities, referencing public CVE databases.")
st.markdown("---")
# ---------------------------- AI Pair Programming Recommendations ----------------------------
st.subheader("🤖 AI Pair Programming: Security Recommendations")
st.markdown("""
Leveraging AI in pair programming can enhance code security and quality. Here are some recommendations:
1. **Reduce Code Complexity**: AI tools can suggest code refactoring to simplify complex code blocks, making them more maintainable and less error-prone.
2. **Minimize Attack Surface**: AI can identify unnecessary code paths and dependencies, allowing developers to remove or secure them.
3. **Automate Security Scans**: Integrate AI-powered security scanners to continuously monitor code for vulnerabilities.
4. **Code Review Assistance**: AI can assist in code reviews by highlighting potential security issues and non-compliance with best practices.
5. **Secure Coding Practices**: AI can provide real-time suggestions for secure coding patterns and discourage the use of insecure functions.
""")
st.markdown("---")
# ---------------------------- Azure Deployment Information ----------------------------
st.subheader("☁️ Azure Deployment Information")
st.markdown("""
While this demo does not include operational deployment, here's how you can deploy this application using Azure services:
**Azure Container Apps**: Use Azure Container Apps to deploy and manage containerized applications at scale without managing infrastructure.
- Benefits:
- Serverless containers
- Built-in support for scaling
- Integrated with Azure services
**Azure Container Registry (ACR)**: Store and manage your container images securely.
- Steps:
1. Build your Docker image.
2. Push the image to ACR.
3. Configure Azure Container Apps to pull the image from ACR.
**Azure Cosmos DB**: Use Cosmos DB to store security audit results, logs, and other application data.
- Features:
- Globally distributed
- Multi-model database service
- Low latency and high availability
""")
st.markdown("---")
# ---------------------------- Footer and Additional Resources ----------------------------
st.subheader("📚 Additional Resources")
st.markdown("""
- [Official Streamlit Documentation](https://docs.streamlit.io/)
- [pip-audit GitHub Repository](https://github.com/pypa/pip-audit)
- [Mermaid Live Editor](https://mermaid.live/) - Design and preview Mermaid diagrams.
- [Azure Container Apps Documentation](https://docs.microsoft.com/en-us/azure/container-apps/)
- [Cybersecurity Best Practices by CISA](https://www.cisa.gov/cybersecurity-best-practices)
""")
st.markdown("If you have any questions or would like to contribute to this project, please reach out or submit a pull request on GitHub.")
# ---------------------------- Sidebar Content ----------------------------
st.sidebar.title("Navigation")
st.sidebar.markdown("""
- [Introduction](#llms-for-cyber-security-state-of-the-art-surveys)
- [Survey Overview Table](#survey-overview-table)
- [Security Model Visualization](#security-model-visualization-with-mermaid)
- [Interactive Chart](#interactive-chart-example)
- [D3.js Visualization](#interactive-d3js-visualization)
- [Additional Insights](#additional-insights)
- [Security Audit](#run-python-dependency-security-audit)
- [AI Recommendations](#ai-pair-programming-security-recommendations)
- [Azure Deployment](#azure-deployment-information)
- [Additional Resources](#additional-resources)
""", unsafe_allow_html=True)
st.sidebar.title("About")
st.sidebar.info("""
This Streamlit app was developed to demonstrate the intersection of Large Language Models and Cybersecurity, highlighting recent surveys and providing tools and recommendations for secure coding practices.
""")
# ---------------------------- End of App ---------------------------- |