- OWASP LLM SECURITY
Secure Your AI Before It Becomes
Your Biggest Risk
Generative AI is transforming business, but it also introduces new security risks. Protect your AI applications against prompt injection, data leakage, model abuse, and emerging OWASP LLM threats.
AI Security Assessments
OWASP LLM Risk Validation
Secure Enterprise AI Adoption
Scan
See the threat instantly
Understand
Know how you're protected
Trust
Proven results & coverage
Act
Book your assessment
- Access Control
BUILD AI SECURITY INTO EVERY LLM APPLICATION
Large Language Models are rapidly being integrated into customer portals, internal assistants, copilots, enterprise search, automation platforms, and AI agents. While these applications improve productivity, they also introduce an entirely new attack surface that traditional application security tools were never designed to protect.
Mechsoft helps organizations securely adopt Generative AI by identifying vulnerabilities unique to LLM applications and implementing security controls aligned with the OWASP Top 10 for LLM Applications. Our cybersecurity specialists evaluate AI architecture, prompts, APIs, plugins, retrieval pipelines, and business workflows to identify exploitable risks before attackers do. We help organizations build AI applications that remain secure, compliant, and resilient as AI adoption grows.
Our solution ensures every message — whether inbound or outbound — is scanned, analyzed, and secured before it can harm your organization.
- Security Challenges
THE AI SECURITY CHALLENGES ORGANIZATIONS FACE TODAY
Prompt Injection
Attackers manipulate prompts to bypass safeguards or influence AI behaviour.
Sensitive Data Exposure
AI applications may unintentionally expose confidential business information or customer data.
Untrusted AI Integrations
Plugins, external tools, APIs, and third-party models introduce additional supply chain risks.
AI Hallucinations
Incorrect or fabricated responses can influence business decisions and reduce trust.
Excessive AI Permissions
AI assistants may receive more access than necessary, increasing business risk.
Rapid AI Adoption
Organizations often deploy AI faster than security governance can keep pace.
- Risk Assessment
HOW MANY OF THESE SOUND FAMILIAR?
Employees are using public AI tools without governance.
AI applications have access to sensitive business data.
Prompt security has never been tested.
AI responses are trusted without verification.
AI plugins and integrations have not undergone security assessment.
There is no formal AI security policy.
AI applications are deployed without dedicated security testing.
Reality Check :-If several of these challenges sound familiar, your organization should assess AI security before expanding enterprise AI adoption.
•INTERACTIVE
HOW OWASP LLM SECURITY ASSESSMENT WORKS
Select a step to see what happens behind the scenes.
Assess
Review AI architecture, business use cases, and model integrations. Review AI architecture, business use cases, and model integrations.
Discover
Identify AI assets, models, APIs, plugins, vector databases, and retrieval systems.
Assess
Test
Evaluate AI applications against OWASP LLM risks using controlled attack scenarios.
Monitor
Validate
Identify exploitable prompt injection, data leakage, excessive permissions, and AI workflow vulnerabilities.
Prioritize
Recommend
Provide practical remediation guidance and AI security best practices.
Integrat
Verify
Retest implemented controls to confirm vulnerabilities have been mitigated.
Improve
Continuously strengthen AI security as applications, models, and business requirements evolve.
- Security Strategy
WHY AI SECURITY REQUIRES A NEW APPROACH
Traditional application security focuses on software vulnerabilities. AI applications introduce new risks including prompt manipulation, sensitive information disclosure, excessive autonomy, insecure integrations, and AI-specific attack techniques.
- A modern AI security strategy validates AI applications against recognized frameworks such as the OWASP Top 10 for LLM Applications while continuously testing prompts, models, APIs, retrieval systems, plugins, and AI workflows. This enables organizations to adopt AI confidently without introducing unnecessary business risk.
- Core Capabilities
WHAT A MODERN AI SECURITY PROGRAM SHOULD DELIVER
- OWASP LLM Security Assessment
- AI Red Teaming
- Prompt Injection Testing
- AI API Security Assessment
- Retrieval-Augmented Generation (RAG) Security Review
- AI Governance & Risk Assessment
- AI Security Recommendations
- Executive AI Risk Reporting
- Business Benefits
BUSINESS OUTCOMES
Eliminate Security Blind Spots
Gain complete visibility into every connected asset across enterprise and operational environments.
Strengthen Cyber Resilience
Understand operational risks before attackers exploit hidden assets.
Improve Incident Response
Provide security teams with accurate asset intelligence during investigations.
Simplify Compliance
Maintain continuously updated asset inventories that support regulatory requirements.
Reduce Operational Risk
Identify vulnerable or unmanaged devices before they impact production.
Support Zero Trust
Build identity and segmentation strategies using accurate, real-time asset intelligence.
- Industry Coverage
INDUSTRY APPLICATIONS
Manufacturing
Oil & Gas
Utilities & Energy
Healthcare
Transportation & Logistics
Smart Buildings & Campuses
- Expert Perspective
OUR PERSPECTIVE
AI security cannot be treated as an extension of traditional application security.
Large Language Models introduce entirely new attack vectors that require dedicated testing, governance, and continuous validation. At Mechsoft, we help organizations build AI securely by combining AI security assessments, OWASP LLM testing, and practical remediation guidance. The goal is not simply to deploy AI, but to deploy AI that the business can trust.
- Support
FREQUENTLY ASKED QUESTIONS
It is an industry-recognized framework that identifies the most critical security risks affecting Large Language Model applications and Generative AI systems.
LLMs introduce risks such as prompt injection, sensitive information disclosure, excessive permissions, insecure integrations, and AI-specific attack techniques that are not covered by traditional application security testing.
Prompt injection is an attack where malicious instructions manipulate an AI model into ignoring its intended behaviour or exposing sensitive information.
Yes. AI applications can be assessed using AI security testing, red teaming, prompt injection testing, API validation, and workflow security assessments designed specifically for LLMs.
No. Enterprise AI assistants, internal copilots, Retrieval-Augmented Generation (RAG) systems, AI agents, and privately hosted LLMs all require security validation.
AI security should be validated continuously as models, prompts, integrations, plugins, and business workflows evolve to ensure new risks are identified before they can be exploited.
BUILD TRUST INTO EVERY AI APPLICATION
Secure your AI initiatives with comprehensive OWASP LLM security assessments that identify vulnerabilities, strengthen governance, and enable responsible AI adoption.
Whether you're deploying AI assistants, enterprise copilots, customer-facing chatbots, or autonomous AI workflows, Mechsoft helps you build AI systems that are secure, resilient, and ready for enterprise use.

