Find AI Security Vulnerabilities Before Your Users Do
Specialized security review for AI features, LLM integrations, and AI-generated code. Testing against the OWASP LLM Top 10 framework.
IOanyT Innovations offers specialized AI Security Assessment services focused on the OWASP LLM Top 10 framework. The service covers prompt injection testing, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. Available in three tiers: Focused Review ($5K-$10K, 1-2 weeks), Full Audit ($15K-$25K, 2-3 weeks), and Ongoing Monitoring (retainer). Every finding includes severity rating, proof-of-concept, remediation steps, and verification tests.
AI Features Have Attack Surfaces That Traditional Security Audits Miss
Traditional security audits test for SQL injection, XSS, and CSRF. But AI features introduce entirely new attack vectors:
- Prompt injection that bypasses your safety guardrails
- Data exfiltration through carefully crafted user inputs
- Insecure output handling that exposes internal system details
- Excessive agency where AI agents take actions beyond their intended scope
- Training data poisoning that corrupts model behavior over time
These vulnerabilities don't show up in standard penetration tests. They require specialized testing that understands how LLMs process and respond to inputs.
more XSS vulnerabilities
more insecure direct object references
AI-generated vs human-written code
Source: CodeRabbit, 470 real PRs
AI-Specific Security Testing, Not Generic Pentesting
We don't run a standard penetration test and call it AI security. Our assessment is built specifically around the OWASP LLM Top 10 framework — the industry standard for AI application security.
Every finding includes:
What We Test — OWASP LLM Top 10
The industry standard for AI application security
Prompt Injection
Direct and indirect injection attacks that bypass system prompts
e.g., User input that makes the AI ignore its instructions
Insecure Output Handling
AI outputs passed to other systems without sanitization
e.g., AI response containing JavaScript that executes in browser
Training Data Poisoning
Vulnerabilities in fine-tuning or RAG data pipelines
e.g., Malicious documents in knowledge base that alter AI behavior
Model Denial of Service
Inputs that cause excessive resource consumption
e.g., Recursive prompts that spike API costs
Supply Chain Vulnerabilities
Third-party model and plugin security
e.g., Compromised model weights or insecure API integrations
Sensitive Information Disclosure
AI revealing training data, system prompts, or user data
e.g., Prompts that extract PII from model memory
Insecure Plugin Design
LLM plugins with excessive permissions
e.g., AI tool that can read/write arbitrary files
Excessive Agency
AI agents taking actions beyond intended scope
e.g., Chatbot that can modify database records
Overreliance
Systems that trust AI output without verification
e.g., Auto-executing AI-generated code without review
Model Theft
Extraction of proprietary model behavior
e.g., Systematic querying to replicate model responses
Our Process
Scope Definition
Day 1Identify AI features, LLM integrations, and data flows to test.
Threat Modeling
Day 2-3Map attack surfaces specific to your AI implementation.
Automated Scanning
Day 3-5Run AI-specific security scanners against your application.
Manual Red-Teaming
Day 5-10Hands-on testing by security engineers who understand LLM behavior.
Report & Remediation
IncludedScored report with proof-of-concept exploits and specific fix guidance.
Verification (Optional)
1-2 daysRe-test after fixes to confirm vulnerabilities are resolved.
Scope Options
Focused Review
Single AI feature or LLM integration (chatbot, RAG pipeline, or AI agent).
Full Audit
All AI features + AI-generated code across entire application.
Ongoing Monitoring
Quarterly re-assessment + continuous threat intelligence for AI features.
Why Not a Standard Penetration Test?
| Factor | Standard Pentest | IOanyT AI Security |
|---|---|---|
| Scope | OWASP Web Top 10 | OWASP LLM Top 10 + Web Top 10 |
| Prompt injection | Not tested | Full direct + indirect testing |
| Data exfiltration via AI | Not tested | Tested across all AI interfaces |
| AI output sanitization | Not tested | Verified for XSS, code injection |
| Agent permissions | Not tested | Tested for excessive agency |
| AI threat model | Generic | Built for your AI implementation |
Who This Is For
AI Chatbot Deployments
Customer-facing AI chat that needs security validation before launch or scaling.
RAG Pipeline Security
Knowledge-based AI systems where data leakage or poisoning is a concern.
AI Agent Systems
Autonomous AI agents that take actions (API calls, database writes, tool use) needing permission boundary testing.
Frequently Asked Questions
Do you need access to our AI models?
Can you test AI features built with any LLM provider?
What do we get at the end?
How is this different from AI Code Rescue?
Do you offer remediation?
Getting Started
Describe your AI features
What they do, which LLM provider, user-facing or internal
Receive scope proposal
Which OWASP categories apply, timeline, investment
Assessment begins
Minimal disruption to your team
Don't Wait for a Security Incident to Find Out What Your AI Can Expose.
AI features create new attack surfaces that traditional security audits miss. Let us find the vulnerabilities before your users do.