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- About
- Core Mission
- Key Capabilities
- Security Frameworks & Methodologies
- Target Users
- Feature Overview
- Compliance Mapping
- Example Workflows & User Benefits
- Use Cases
- Case Studies
- Support
- Get Involved
- Trial accounts have a limit of up to 5 scans per license period
- Upgrade to a full license for unlimited scanning and advanced features
- Sign up for a trial account
Threat Shield implements enterprise-grade security measures to protect your data and ensure the highest level of application security:
- Antivirus scanning is automatically performed on all file uploads to prevent malware transmission
- All uploaded files are scanned in real-time before processing
- File size limit: Individual file uploads are limited to 100 MB (GitHub repository size limit)
- OWASP Top 10 for API Security compliance - Threat Shield has been thoroughly tested against OWASP Top 10 for API Security vulnerabilities for application security
- Two-Factor Authentication (2FA) - Enhanced security for login and signup with 2FA verification
- Secure authentication with JWT tokens and encrypted session management
- Input validation and sanitization for all user inputs to prevent injection attacks
- No sensitive data storage - Repository source code, DFDs, API keys, and Personal Access Tokens submitted to the application are never stored permanently
- Temporary processing only - Sensitive data is used solely to initiate scans and is immediately purged after analysis completion
- Minimal data retention - Only trivial information is retained for record-keeping:
- Repository URLs (for scan tracking)
- System names (for project identification)
- Model names (for LLM assessment tracking)
- Encrypted data transmission using TLS 1.3 for all communications
- Secure cloud infrastructure with regular security audits and compliance certifications
- Automated security monitoring with real-time threat detection
- Regular penetration testing by certified security professionals
To learn more about our paid plans and pricing options, please contact our team:
📧 Email: support@zeroshield.ai
Our team will work with you to find the right plan for your organization's needs.
Get started with a trial account to experience Threat Shield today!
Threat Shield is an advanced threat modelling platform within the ZeroShield platform, designed to give organizations deep, actionable insight into risks across classic applications, codebases, and cutting-edge GenAI/LLM integrations. Threat Shield leverages Large Language Models to perform comprehensive threat modeling, analyzing system architectures, identifying vulnerabilities, and generating actionable security insights. Threat Shield unifies real-time threat modeling, LLM threat modeling, and MCP codebase vulnerability scanning for a complete, compliance-ready view of your security posture.
Empower teams to rapidly identify, understand, and remediate security risks—across all technologies—by automating threat analysis using Large Language Models, vulnerability detection, and compliance mapping. Threat Shield bridges the gap between security, development, and compliance, enabling secure digital innovation.
- Automated Threat Modeling for Classic and Cloud-Native Systems (powered by Large Language Models)
- Advanced LLM Threat Modeling (comprehensive security analysis using Large Language Models)
- MCP Server-Based Vulnerability Scanning & Compliance analysis ( using Large Language Models )
- Real-Time Compliance & OWASP Top 10 for API Security Mapping (ISO, PCI, SOC2, NIST, GDPR)
- Unified Risk Analytics & Visualizations
Threat Shield employs industry-standard security frameworks enhanced by Large Language Models to ensure comprehensive threat analysis:
STRIDE is a threat modeling framework that categorizes security threats into six fundamental types:
- S - Spoofing: Impersonating someone or something else
- T - Tampering: Modifying data or code in unauthorized ways
- R - Repudiation: Denying having performed an action
- I - Information Disclosure: Exposing information to unauthorized parties
- D - Denial of Service: Disrupting or degrading service availability
- E - Elevation of Privilege: Gaining unauthorized elevated access
DREAD is a risk assessment model that evaluates threats across five dimensions:
- D - Damage: Potential impact if the threat is exploited
- R - Reproducibility: How easily the threat can be reproduced
- E - Exploitability: How difficult it is to exploit the threat
- A - Affected Users: Number of users potentially impacted
- D - Discoverability: How easily the threat can be discovered
Each threat receives a DREAD score (1-10 scale) across all five dimensions, providing a quantitative risk assessment for prioritization and remediation planning.
Automate threat modeling, vulnerability scanning, and compliance.
Real World Scenario: Sarah, a Senior Security Engineer at a fintech startup, is tasked with conducting a comprehensive security assessment of their new payment processing system before the PCI audit next month. Using Threat Shield, she uploads the system architecture diagrams and connects the GitHub repository. The platform's Large Language Models engine performs comprehensive threat modeling analysis. Within hours, she receives a detailed threat model identifying 23 STRIDE threats, including critical API key exposure and authentication bypass vulnerabilities. The DREAD heatmap helps her prioritize the 7 high-risk issues that could fail PCI compliance controls. She exports an audit-ready report showing all findings mapped to PCI DSS requirements, saving weeks of manual analysis and ensuring the system passes the audit on the first try.
Integrate risk analysis into CI/CD and shift security left.
Real World Scenario: Mike, a DevOps Lead at a SaaS company, needs to implement security scanning in their CI/CD pipeline for 15 microservices. He integrates Threat Shield into their GitHub Actions workflow, enabling automatic Large Language Models-powered threat modeling on every pull request. When a developer pushes code with a hardcoded database password, Threat Shield's Large Language Models analysis immediately flags it as a STRIDE Information Disclosure threat with a high DREAD score. The system blocks the merge, provides the developer with an actionable fix, and creates a compliance tracking ticket. This prevents 3 potential security incidents in the first month, reducing their security debt by 40% and accelerating their SOC2 certification timeline.
Secure and validate LLM/GenAI features with real threat modeling security probes.
Real World Scenario: Dr. Lisa Chen, an AI Product Manager at a healthcare technology company, is launching an AI-powered patient consultation chatbot that handles sensitive medical data. Before going live, she runs Threat Shield's threat modeling assessment powered by Large Language Models, which executes 40+ threat modeling security probes. The results reveal high vulnerability to prompt injection attacks that could expose patient data, triggering failed HIPAA compliance controls. Using the detailed LLM-generated remediation suggestions, her team implements input sanitization and context filtering. After re-running the assessment, the risk level drops to "Low" and all compliance checks pass, allowing the chatbot to launch securely and handle thousands of patient interactions without security incidents.
Map vulnerabilities to compliance frameworks and track remediation.
Real World Scenario: Robert, a Compliance Manager at a financial services company, needs to prepare for an annual ISO 27001 audit covering 8 different applications. Instead of manually reviewing hundreds of security controls, he uses Threat Shield to generate comprehensive compliance reports for each system. The platform's LLM automatically maps all identified vulnerabilities to ISO 27001 controls, showing that 15 out of 114 controls need attention. He creates remediation tickets with clear priorities based on DREAD scores and tracks progress through the dashboard. The audit-ready reports demonstrate 95% control coverage, impressing the external auditors and resulting in a clean audit report with only minor observations.
Design secure architectures with instant, actionable feedback.
Real World Scenario: Elena, a Cloud Solution Architect at a global e-commerce company, is designing a new multi-cloud infrastructure for handling Black Friday traffic spikes. She uses Threat Shield to model the architecture with 12 microservices across AWS and Azure, including payment processing, user authentication, and inventory management. The platform's LLM Powered threat modeling analysis identifies 31 potential attack vectors, with the DREAD heatmap highlighting that 5 threats could cause service outages affecting millions of users. She redesigns the architecture to eliminate single points of failure, adds proper encryption at rest and in transit, and implements zero-trust networking. The final architecture passes all OWASP Top 10 for API Security checks and compliance requirements, ensuring a secure and scalable Black Friday deployment that handles 10x traffic without security incidents.
Description: This diagram illustrates the comprehensive threat modeling architecture for normal systems, showing how Threat Shield uses Large Language Models to analyze system components, data flows, trust boundaries, and generates STRIDE-based threat assessments with compliance mapping.
Normal.Systems.of.Threat_Modelling.mp4
- Project List Screen:
- Table: [Name, Repo URL, Total Threats, Unresolved Threats, Total Non-Conformant, OWASP Found, Created At]
- “Create Project” button to launch new assessment
- Project Creation Dialog:
- Tabs: Threat modeling / LLM Threat Modeling / MCP Scanning
- Fields: System Name, Architecture Type, Description, Internet Facing (Y/N), Upload Architecture/DFD/UML, GitHub Repo connect, Auth Type, Data Sensitivity, Dependencies
- Note: Uploaded diagrams and files must be under 100 MB (GitHub repository size limit)
- Project Dashboard:
- Summary Cards:
- Total STRIDE Threats Identified
- Threats Unresolved
- Threats Resolved
- Threat Score (overall risk indicator)
- DREAD Heatmap: Visualizes risk by Damage, Reproducibility, Exploitability, Affected Users, Discoverability (the five components of DREAD risk assessment).
- Failed Controls Card: Number of failed compliance controls.
- OWASP Top 10 for API Security Tracker: Visual (circle graph) and list of which categories are triggered.
- Attack Trees: Visual graph showing attack paths and relationships.
- Threat Details Table:
- Columns: Threat, DREAD Score, Severity, Compliance, Location, STRIDE Category, Status, Suggested Fixes
- Search & filter options
- Generate Report Button: Exportable audit-ready report.
- Summary Cards:
Description: The main dashboard for a Threat Modeling project, showcasing the system's overall risk score (6.5), key metrics like Total STRIDE Threats (6 identified, 6 unresolved), and the high-level DREAD Heat Map for rapid risk prioritization.
- Summary Cards: Instantly see the scale and urgency of risk in the system.
- DREAD Heatmap: Pinpoints highest-impact threats for prioritization using the five DREAD risk factors.
- Failed Controls: Shows direct compliance gaps (e.g., SOC2, PCI).
- OWASP Tracker: Maps findings to industry standards for external reporting.
- Attack Trees: Helps visualize possible attack paths and lateral movement.
- Threat Table: Enables fast triage, assignment, and remediation tracking.
Description: An in-depth view of the Threat Modeling dashboard, displaying the PASTA Attack Tree visualization to map out potential attack paths and lateral movement. It also highlights the OWASP Top 10 for API Security Tracker, showing 3 of 10 categories triggered, including Identification and Authentication Failures (A07) and Security Misconfiguration (A05).
Description: An expanded view of a Threat Modeling finding, detailing an API Key Exposure threat. It shows the impact across five major compliance frameworks (ISO, NIST, PCI DSS, GDPR, SOC 2), the associated OWASP Broken Access Control (A01:2021) vulnerability, and a comprehensive, actionable AI-generated Suggested Fix for secure key management.
Description: The top portion of the Threat Modeling finding details, clearly showing the threat's High Severity, the associated STRIDE Categories (S-Spoofing, I-Information Disclosure), DREAD Scores for risk calculation, and a comprehensive list of affected Compliance Controls across ISO, NIST, PCI DSS, GDPR, and SOC 2.failed compliance controls like Malware Detection and Prevention, and the specific AI-generated remediation fix to update detection rules.
- Upload system diagrams, code, and metadata.
- The Large Language Models-powered engine analyzes flows, trust boundaries, and identifies threats using STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege), DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability), and OWASP logic.
- Each threat is auto-mapped to compliance controls using Large Language Models analysis.
- Dashboard and report update in real-time as threats are resolved.
Alice, a Security Engineer, is onboarding a new payments platform. She uploads the system's DFDs and architecture, connects the GitHub repo, and sets data sensitivity. Threat Shield's AI engine instantly generates a comprehensive threat model showing 23 STRIDE threats, highlighting 7 unresolved high-risk issues and 2 failed PCI compliance controls. Using the DREAD heatmap and attack tree, Alice quickly triages which risks to escalate to DevOps for urgent fixes, and exports a report to satisfy the next audit.
Description: This diagram shows the LLM threat modeling architecture, demonstrating how Threat Shield uses Large Language Models to perform comprehensive security analysis on Large Language Models, including threat modeling security probe execution, vulnerability assessment, and compliance mapping for AI systems.
LLM.Video.mp4
- LLM Threat Modeling Projects:
- Table: [Name, Total Tests, Total Hits, Hit Rate, Risk Level, Created At]
- "Create Project" button to launch new threat modeling assessment
- LLM Threat Modeling Project Dashboard:
- Summary Cards:
- Total Tests
- Total Hits
- Hit Rate
- Risk Level (visual/highlight)
- Failed Controls: Number of non-conformant controls triggered by LLM findings.
- OWASP Vulnerabilities Card: Number and list of OWASP Top 10 for API Security categories found.
- Threat Graph: Distribution of exploit hits across threat modeling security probes.
- Threat Modeling Security Probe Details Table:
- Columns: Probe Name, Passed, Total, Hit Rate, Detector, Suggested Fixes
- "View Suggestions" for every threat modeling security probe
- Summary Cards:
- Summary Cards: High-level risk/weakness snapshot of LLM.
- Failed Controls: Links AI security to compliance posture.
- OWASP Card: Shows if LLM flaws could impact web/app security standards.
- Threat Modeling Security Probe Table: Full transparency—see exactly which prompts/threat modeling security probes succeeded, failed, and why.
- AI Powered Threat modeling analyzes LLM systems for:
- ANSI escapes, attack generation, audio attacks, antivirus/spam bypass, data leakage, prompt injection, roleplay/jailbreaks, SQLi, XSS, hallucinated package names, adversarial suffixes, emotional manipulation, and more.
- LLM Exposure Analysis:
- Beyond threat modeling security probe results, Threat Shield's Large Language Models analyzes the LLM's exposure and risk posture, surfaces all successful bypasses, and maps to OWASP Top 10 for API Security and failed compliance controls.
Description: A deep-dive into an LLM security finding, showing the results of a malware evasion threat analysis. The panel displays the Medium Risk Level, a 20.8% hit rate, failed compliance controls like Malware Detection and Prevention, and the specific AI-generated remediation fix to update detection rules.
Threat Shield uses Large Language Models to perform comprehensive threat modeling for LLM systems, analyzing dozens of security categories:
| Threat Category | What It Analyzes | Why It Matters |
|---|---|---|
| ANSI Escape | Model exposes terminal codes | Downstream system compromise |
| Attack Generation | Can model be tricked into toxic/illegal output | Content moderation, brand risk |
| Audio | Multimodal attacks bypassing text safety | Jailbreaks on non-text input |
| AV/Spam Scanning | Will model output malware/spam test strings | Bypassing security scanning |
| Continuation/Slurs | Model completes offensive/inappropriate words | Social/ethical risk |
| Data Leakage | Model repeats training data | Confidentiality breach |
| Doctor/Roleplay | Policy puppetry to bypass safety | Regulatory/safety risk |
| Encoding/Jailbreak | Can model decode/obfuscate harmful payloads | Filter bypass, prompt injection |
| Exploitation (SQLi) | Model can output exploitable code | Direct app/system compromise |
| Fileformats | Model exposes details about underlying files | Environment leakage |
| Glitch Tokens | Model misbehaves on edge tokens | Model stability, DoS |
| Goodside/Malwaregen | Known adversarial/prompt injection patterns | Real-world exploit research |
| Grandma/Emotional | Will model yield to emotional manipulation | Social engineering risk |
| Hallucination | Model invents fake package/library names | Supply chain attack |
| Prompt Injection | Model can be hijacked to output specific strings | LLM-powered app hijacking |
| XSS/Visual Jailbreak | Model outputs code/image links for data exfiltration | Web/visual AI security |
| Latent Injection | Model attacked through hidden instructions in data | RAG, document QA system risk |
Further Analysis:
Beyond these threat categories, Threat Shield analyzes all threat modeling results for exposure patterns—automatically scoring LLM risk, mapping incidents to OWASP Top 10 for API Security and compliance controls, and surfacing which prompt/attack types are most dangerous in your use case.
- Real-world threat modeling using LLMs and comprehensive security analysis techniques.
- Immediate understanding of LLM weaknesses and exposure through Large Language Models analysis.
- Human-readable explanations and remediation steps generated by Large Language Models for every finding.
- Continuous assessment as models/prompts evolve.
- Supports secure, compliant AI adoption in any environment.
Raj, an AI/ML Product Lead, is launching a GenAI-powered helpdesk. He runs a Threat Shield LLM threat modeling assessment powered by Large Language Models, which analyzes 40+ security threat vectors. The dashboard reveals high-risk prompt injection and roleplay vulnerabilities, and a failed GDPR control. Raj reviews the AI-generated suggested fixes, patches the prompt template, and re-runs the assessment—dropping risk level to "Low" and passing all compliance checks before go-live.
Description: This diagram presents the MCP Server vulnerability scanning architecture, illustrating how Threat Shield performs static code analysis on MCP repositories, identifies security vulnerabilities, and uses AI to generate explanations with compliance mapping.
MCP.Video.mp4
- MCP Scans List:
- Table: [Repository Name, Total Results, Language, Status, Created At]
- "Create Project" for new scan
- Note: Repository files must be under 100 MB each (GitHub repository size limit)
- MCP Scanning Project Dashboard:
- Summary Cards:
- Total Results
- Total Resolved
- Total High Risk
- Failed Controls: Compliance controls failed by code issues.
- OWASP Vulnerabilities: Number and list of Top 10 for API Security triggered.
- Threat Graph: Shows trends/types/frequency across findings.
- Result Table:
- Columns: Result Class, Severity, Rule Detected, Status, Suggested Fixes
- Summary Cards:
Description: The dashboard for an MCP Server Vulnerability Scan, providing an executive summary with Total Findings (5), High Risk items (5), and the primary development language (Python). It also displays a list of detected OWASP Vulnerabilities (A03, A06, A04, A01) and a visualization of Failed Compliance Controls over time.
Description: A detailed view of a security finding from the MCP Server scan, specifically the detect-command-execution rule. The panel highlights the affected code snippet, relevant compliance controls (NIST SP 800-53 and PCI DSS), the associated OWASP Injection vulnerability (A03:2021), and the AI-generated recommended fix to prevent unauthorized command execution.
- Summary Cards: Snapshot of codebase health and urgency.
- Failed Controls: Highlights direct compliance impact.
- OWASP Card: Maps code issues to industry standards.
- Threat Graph: Visualizes risk over time or per category.
- Result Table: Drill down to every finding, fix, and compliance mapping.
- Phase 1:
- Repository is temporarily cloned and scanned for static code analysis.
- The system flags all code patterns matching vulnerability rules.
- Note: Repository files must be under 100 MB each (GitHub repository size limit).
- Phase 2:
- Each finding is analyzed by an LLM to generate explanations and actionable fixes.
- Further mapped to OWASP Top 10 for API Security vulnerabilities and failed compliance controls (ISO 27001, PCI DSS, NIST, SOC2, GDPR, etc.).
- Compliance mapping is visualized per finding and overall scan.
Description: A close-up view of the code analysis within the MCP Scan, showing the exact file (mcp_server.py) and code snippet where the vulnerability was detected. The finding is given a Medium risk severity, accompanied by a AI-generated Explanation of the command execution risk found in the code.
- Step 1: Static Analysis
- When a scan is triggered, Threat Shield temporarily clones the selected MCP repository.
- The system runs a suite of static analysis rules covering OWASP Top 10 for API Security, common code smells, secrets, and insecure configurations.
- The analysis engine is chosen for its speed, accuracy, and extensibility (custom rules per project/language).
- Note: Repository files must be under 100 MB each (GitHub repository size limit).
- Step 2: Large Language Models-Powered Findings
- Each finding is passed to a Large Language Models model for further analysis, generating:
- Human-readable explanation of risk
- Contextual remediation steps
- Mapping to compliance controls (ISO, PCI, SOC2, NIST, GDPR, etc.)
- Each finding is passed to a Large Language Models model for further analysis, generating:
- Step 3: Dashboard and Reporting
- All results are visualized in the MCP scan dashboard, showing severity, status, compliance impact, and suggested fixes.
- Reports can be exported for audit/evidence.
- Automated, deep static analysis on every codebase.
- LLM-powered explanations for fast triage and remediation.
- Clear compliance gap visibility for every finding.
- Audit-ready reporting and compliance evidence on demand.
Jane, DevSecOps Lead, pushes a new release to the MCP repo. Threat Shield automatically kicks off a scan. Within minutes, Jane sees 13 new findings—two critical SQL injection issues, several failed PCI controls, and a high-risk secrets leak. The AI-generated explanations help her prioritize fixes, and she exports a compliance report for the next audit.
| Vulnerability / Threat Type | OWASP Top 10 for API Security | ISO 27001 | PCI DSS | SOC2 | NIST | GDPR |
|---|---|---|---|---|---|---|
| Insecure Authentication | A07 | ✓ | ✓ | ✓ | ✓ | ✓ |
| Injection (SQLi, Command, etc.) | A03 | ✓ | ✓ | ✓ | ✓ | |
| Data Exposure/Leakage | A01, A06 | ✓ | ✓ | ✓ | ✓ | ✓ |
| Security Misconfiguration | A05 | ✓ | ✓ | ✓ | ✓ | |
| Vulnerable/Outdated Components | A06 | ✓ | ✓ | ✓ | ✓ | |
| Prompt Injection (LLM) | A01, A05 | ✓ | ||||
| Malicious Code Generation | A06, A10 | ✓ | ||||
| Compliance Control Failure (General) | All | ✓ | ✓ | ✓ | ✓ | ✓ |
Each finding in the dashboard is automatically mapped to these standards using Large Language Models-enhanced threat modeling security probe/static analysis metadata and logic. Compliance impact is shown per issue and in summary cards.
Threat Modeling Workflow:
- Upload system diagrams and architecture details, LLM analyzes and generates comprehensive threat model, review generated threat model, prioritize/assign remediation, track resolution and compliance mapping.
- Dashboard instantly shows unresolved STRIDE threats, failed PCI compliance controls, and actionable fixes generated by Large Language Models.
LLM Security Workflow:
- Register LLMs for assessment, receive prioritized threat modeling reports with detailed Large Language Models analysis, act on AI-generated fix steps, monitor ongoing risk.
- AI/ML lead performs threat modeling on a chatbot; dashboard highlights prompt injection risk and GDPR compliance issues; fixes are applied and risk drops.
MCP Vulnerability Workflow:
- Trigger scan for MCP repositories, review findings and AI-generated explanations, analyze OWASP/compliance mapping, assign remediations, produce compliance-ready reports.
- DevSecOps lead triggers a code scan; the system finds vulnerabilities, LLM explains and maps them to OWASP and compliance, all results are reviewed and exported for audit.
Benefits:
- Prevent real-world security incidents and compliance failures before they happen.
- Save time with automation and actionable guidance.
- Communicate risk and progress clearly to all stakeholders.
- Security architecture reviews for new/evolving systems
- Continuous compliance (ISO, PCI, NIST, SOC2, GDPR)
- DevSecOps integration into CI/CD
- GenAI/LLM security validation and monitoring
- MCP workload/codebase security
A public repository of case studies and technical deep-dives are available below, demonstrating Threat Shield's real-world impact across various security domains.
-
Case Study 1: Securing a High-Risk Payments Platform (Threat Modeling)
-
Case Study 2: Validating a GenAI Chatbot against Prompt Injection (LLM Security Testing)
- 📧 Contact: vartul@zeroshield.ai
- 📧 Support Queries: support@zeroshield.ai
Value Proposition:
Unified, automated threat modeling and vulnerability management for the next generation of enterprise security "Thraet Shield turns complex, manual security and compliance challenges into automated, actionable insights—empowering teams to move faster, safer, and with confidence." Threat Shield, a part of ZeroShield, brings in-depth, practical security to every step of your development and deployment lifecycle.
We welcome contributions from the security community! Here's how you can get involved with Threat Shield:
Sign up for Threat Shield to start securing your systems today!
- Report Issues: Found a bug or have a feature request? Open an issue on our repository
- Code Contributions: Submit pull requests to help improve Threat Shield
- Documentation: Help improve our documentation and examples
- Security Research: Contribute new threat models, attack patterns, or vulnerability tests
- Note: When contributing files to the repository, please ensure individual files are under 100 MB (GitHub repository size limit)
- GitHub Discussions: Join our community discussions and share ideas
- Security Research: Collaborate on new security analysis techniques
- Feedback: Share your experience using Threat Shield in your organization
- General Contact: vartul@zeroshield.ai
- Support: support@zeroshield.ai
All rights reserved. This software and its documentation are the intellectual property of ZeroShield.